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000-N12 - IBM SPSS Data Collection Technical Support Mastery Test v1 - Dump Information

Vendor : IBM
Exam Code : 000-N12
Exam Name : IBM SPSS Data Collection Technical Support Mastery Test v1
Questions and Answers : 60 Q & A
Updated On : February 20, 2019
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000-N12 Questions and Answers

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000-N12 IBM SPSS Data Collection Technical Support Mastery Test v1

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000-N12 exam Dumps Source : IBM SPSS Data Collection Technical Support Mastery Test v1

Test Code : 000-N12
Test Name : IBM SPSS Data Collection Technical Support Mastery Test v1
Vendor Name : IBM
Q&A : 60 Real Questions

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IBM IBM SPSS Data Collection

My Highlights from IBM believe 2018: information Science, SPSS, Augmented truth and the client journey | killexams.com Real Questions and Pass4sure dumps

I attended IBM’s inaugural think event in Las Vegas last week. This event, IBM’s greatest (estimated 30,000+ attendees!), focused on making your business smarter and protected keynotes and periods on such themes as artificial intelligence, information science, blockchain, quantum computing and cryptography. i was invited by way of IBM as a guest to share some insights from the viewpoint of a data scientist. under are just a few highlights of the experience.

information Science the usage of IBM SPSS SPSS at 50

50 Years of SPSS Innovation. click on photograph to enlarge.

IBM SPSS is IBM’s set of predictive analytics products that tackle the whole analytical method, from planning to statistics collection to analysis, reporting and deployment. IBM celebrated the fiftieth anniversary of IBM SPSS with their new beta free up of IBM SPSS data 25, the largest beta unlock in its historical past. The up-to-date edition contains new developments like publication-equipped charts, MS workplace integration, Bayesian records and superior information. additionally, they brought a new consumer interface which is pretty slick.

i was introduced to SPSS information in college and have used it for each of my research tasks in view that then. To be sincere, SPSS facts has aged more suitable than I actually have! I have already begun the usage of the new edition and am fairly excited concerning the new aspects and user interface. i will be able to record about journey in a later submit. check out SPSS with a free 14-day trial.

improving the client experience

contemporary reports have estimated that forty five% of dealers are expected to increase using artificial intelligence for consumer journey within the next three years, and fifty five% of dealers are concentrated on optimizing the client adventure to boost customer loyalty. moreover, 85% of all client interactions with a company might be managed without human interplay by using 2020.

client journey management (CXM) is the system of knowing and managing clients’ interactions with and perceptions in regards to the company/company. IBM is familiar with that improving the customer journey is more and more fitting facts-intensive recreation, and the usage of the combined power of facts and nowadays’s processing capabilities can support agencies model the processes that have an impact on the customer adventure. I attended a couple of classes to learn about how IBM is leveraging the vigor of IBM Watson to help their purchasers with Watson Commerce and Watson client event Analytics options. These options use the power of synthetic intelligence (e.g., predictive analytics) to enrich how groups can more desirable manage consumer relationships to boost client loyalty and circulate their company forward.

statistics Science Meets stronger Analytics and Augmented reality

These statistics authorities from Aginity, IBM Analytics, H2O.ai and IBM Immersive Insights are enhancing the way you get from records to insights.

I noticed a great demonstration of the intersection of statistics science, better analytics and augmented reality. Getting from facts to insights is the aim of information science efforts and, as facts sources continue to develop, we can want greater easy methods to get to those insights. Aginity is working with H2O.ai to display the best way to enhance your predictions by means of augmenting public facts with more desirable facts (with derived attributes) and more desirable analytics to make enhanced predictions. using baseball facts, Ari Kaplan of Aginity pointed out that the improvements in predictive fashions may translate into millions of greenbacks per player. while his demo concentrated on the use of these applied sciences in baseball facts, the principles are generalizable to any business vertical, including finance, healthcare and media.

at the equal demonstration station, Alfredo Ruiz, lead of the Augmented fact application at IBM Analytics, showed me how his crew (IBM Immersive Insights) is incorporating augmented truth into facts Science experience to assist groups improved remember their ever-increasing statistics units. I’m looking forward to seeing how his efforts in marrying augmented truth and records science progress.

I had the privilege of interviewing Ari Kaplan of Aginity who talked about the work he is doing to enrich how Aginity and H2O.ai is improving the facts science procedure. check out what he has to assert below.

Don’t omit this interview with Ari Kaplan, a true “Moneyball” and neatly generic around essential League Baseball, as he talks concerning the newest machine getting to know applied sciences powering today’s baseball choices, and take a look at the superb demo.

Posted with the aid of IBM facts Science on Thursday, March 22, 2018

data Science is a crew sport

Bob, Al and Dez. photo by means of Dez Blanchfield

I had the opportunity to talk with with many industry specialists who come to records science from a unique perspective than I do. whereas I focal point primarily on the records and arithmetic features of information science, a lot of my statistics peers strategy information science from a technological and programming attitude. really, for an upcoming podcast, Dez Blanchfield and i were interviewed via Al Martin of IBM Analytics to focus on our respective roles in records science. This conversation was a lively one, and i am anticipating reliving that night once the podcast is released. The final analysis is that statistics science requires such a various skill set that you really need to work with different americans who can complement your advantage.

I’m with information execs (and actors) Trisha Mahoney, Ryan Arbow and Shadi Copty.

This idea that statistics science is a group sport was placed on full reveal in an entertaining session by which a couples therapist (Trisha Mahoney) helped get to the bottom of an argument between an information science chief (Shadi Copty) and IT chief (Ryan Arbow). Asking probing questions, the counselor revealed that the facts science and IT chief had been at odds as a result of an absence of conversation. She introduced them to IBM’s statistics Science adventure, an commercial enterprise data science platform that enables them to effectively collaborate, use right open source equipment and get their models into creation faster.

Analytics: Your aggressive talents

For me, IBM suppose 2018 was all about making your company smarter via analytics. actually, research indicates that corporations which are superior able to carry the vigour of analytics to undergo on their enterprise complications may be in a far better position to outperform their analytics-challenged opponents. This idea become illustrated through keynotes, classes and conversations. through bringing distinct records science specialists together to leverage the tools and strategies of AI and desktop/deep gaining knowledge of will assist you movement your company ahead. in case you have been unable to attend the event, that you would be able to watch replays of lots of the keynotes here.

(Disclosure: IBM assisted me with go back and forth costs to IBM suppose 2018.)


a glance at the IBM SPSS Modeler and IBM SPSS facts analytics tools | killexams.com Real Questions and Pass4sure dumps

IBM's SPSS predictive analytics tools consist of IBM SPSS Modeler and IBM SPSS data. SPSS Modeler gives statistics mining and textual content evaluation utility, while SPSS facts is an integrated family of items. both tools allow users to construct predictive fashions and execute different analytics tasks.

The IBM SPSS Modeler goals clients who have little or no programming competencies. users are supplied with a drag-and-drop user interface, enabling them to build predictive models and function other information analytics. Modeler can follow distinct procedures and algorithms to support the person find counsel hidden in the information. The device can also assist in integrating and consolidating all kinds of data units from dispersed records sources across the corporation.

The IBM SPSS records suite is an integrated set of products geared toward extra knowledgeable records analysts. SPSS facts addresses the complete analytical system, from planning to statistics assortment, analysis, reporting and deployment.

IBM SPSS Modeler points

version 18 provides the following facets:

  • more than 30 base laptop learning algorithms.
  • Extensions that supply endured advancements for use with open supply products, equivalent to R and Python.
  • more desirable help for a number of multithreaded analytical algorithms, including Random timber, Tree-AS, Generalized Linear Engine, Linear-AS, Linear help Vector computing device and Two-Step-AS clustering.
  • The means to run quite a lot of Python and Spark desktop researching, as well as different Python analytics libraries natively in Modeler with out requiring the use of the Analytic Server, as changed into required in the old edition.
  • SPSS Modeler bundles are deployed on premises, and SPSS Modeler Gold is obtainable as a cloud offering. The customer entrance conclusion of SPSS Modeler runs under windows and macOS, while the server element runs on Unix, Linux and home windows.

    IBM SPSS Modeler provides right here variants:

  • SPSS Modeler personal: A single-consumer computing device product.
  • SPSS Modeler knowledgeable: A desktop product that works with IBM SPSS Analytic Server, proposing enhanced scalability and performance and enabling applications for use throughout an organization.
  • SPSS Modeler premium: This version contains superior algorithms and capabilities, comparable to text analytics, entity analytics and social network evaluation, that boost mannequin accuracy with unstructured data.
  • SPSS Modeler Gold: This version gives analytical determination administration, collaboration and deployment capabilities. SPSS Modeler Gold is also attainable as a cloud providing.
  • IBM SPSS data points

    SPSS information version 24 contains the following new elements:

  • The means to entry greater than one hundred extensions, enabling users to take competencies of free libraries written in R, Python and SPSS syntax.
  • The IBM SPSS Extension Hub to browse, download, update, remove and usually manage extensions.
  • a big improve to the custom Dialog Builder, enabling users to greater conveniently build and installation their own extensions. Enhancements consist of new controls and new residences for current controls and a number of other advancements to the user interface.
  • advancements that allow users to extra easily and right now import and export statistics into SPPS records.
  • advancements to the customized Tables module, including new statistical performance and consumer-requested features.
  • IBM SPSS records offers the following three versions (each and every with extra modules):

  • SPSS records commonplace tools give superior statistical techniques that help linear and nonlinear statistical fashions, in addition to predictive simulation modeling, which bills for doubtful inputs, geospatial analytics and customized tables.
  • SPSS records knowledgeable tools assist statistics instruction, lacking values and data validity, determination timber, and forecasting.
  • SPSS records top class provides superior analytical innovations, together with structural equation modeling, in-depth sampling evaluation and trying out. This bundle also includes strategies that goal direct advertising and marketing and high-end charts and graphs.
  • Pricing for the SPSS Modeler and SPSS information predictive analytics tools fluctuate depending on the bundle alternate options, the variety of users and the license duration. SPSS facts is now accessible as a subscription alternative or a perpetual license. IBM presents free trials of both IBM SPSS Modeler and IBM SPSS information.

    subsequent Steps

    Why the term unstructured information is a misnomer

    How large data is altering facts modeling suggestions

    massive facts techniques pose new challenges to data governance

    linked elements View extra

    the use of big facts to combat Fraud | killexams.com Real Questions and Pass4sure dumps

    Posted April 18, 2013 by way of Drew Robb     remarks

    while huge data will also be beneficial in fighting fraud, common company intelligence tools aren't tons assist.

    If it become handy to realize fraud, there wouldn’t be tons of it around. however at the present time, the sheer quantity of assistance being generated makes it extra tricky than ever to become aware of.

    “groups nowadays usually have a fragmented method to dealing with safety and fraud risks, which leaves them liable to even more attacks as criminals are quick to locate and make the most any features of weak spot,” mentioned Ellen Joyner-Roberson, international advertising principal at SAS protection Intelligence solutions. “due to this fact, security hazards and economic crimes are increasing dramatically because of a weak international economy, growth in geared up crime and the expanding sophistication of fraudulent schemes.”

    not Your regular Analytics

    unfortunately, many present analytics systems do not help effective analytical modeling, making it complex to system and analyze counsel speedy satisfactory to make a change. distinct departments often have disparate information sources and processes, so it’s complicated to spot suspicious activity across the commercial enterprise.

    in addition, there is a constant struggle to steadiness more difficult anti-fraud efforts with a great client adventure. If companies cease extra transactions that are suspicious in an try and cut back fraud losses, they may additionally anger shoppers making an attempt to complete official transactions.

    Banking, insurance, government, fitness care and retail are obvious aims for con artists. The banking trade, as an instance, has a large problem with identification theft and account takeover. There, it's essential that choices are made in actual time at the aspect of sale, which requires a monitoring and analytics strategy that can straight mannequin for conduct changes and stop that transaction without getting it incorrect too many times.

    because of such calls for, the consensus is that average enterprise intelligence (BI) isn’t subtle sufficient. It must be backed up by area-embedded suggestions and adaptive analytics that may also be tailored to several types of fraud or fiscal crime. Context also plays a a must-have half, and integrating contextual information from mobile apps and other channels can heighten success. And geospatial and speed facts seize at transaction origination should be backed up by using determination making in real time.

    “To combat fraud earlier than it motives financial losses, companies are the usage of precise-time interdiction (definition: to forbid or debar) capabilities at any place they can, and with raw response instances beneath 200 milliseconds,” stated Joyner-Roberson.

    Her enterprise developed SAS protection Intelligence to tackle this market. It includes numerous SAS items to handle fraud and improper funds, regulatory compliance and security. This includes SAS Fraud administration, SAS community Analytics, SAS financial Crimes Suite and SAS Fraud Framework.

    SAS security Intelligence takes an commercial enterprise strategy to information administration and consolidation, combining records integration, data great and master records management into a unified ambiance that brings together pass-channel enterprise information on a single platform. It has the ability to score and choice all transactions in real-time and intercept suspect deals.

    Haystacks, Needles and large statistics

    big facts handiest magnifies the problems inherent in tracking down wrongdoers. just how do you cope with capturing, processing, and storing massive quantities of true-time information so that you can study and adapt to changing behavior patterns?

    “The needle in the haystack analogy is primarily applicable for fraud detection because of the small amount of fraud compared to the complete of legit net site visitors,” said Jesse McKenna, head of threat research for Silver Tail methods, RSA. “besides the fact that children, as fraud patterns normally shift and evolve, so does the needle you are trying to find. today it could be a needle, the next day a pushpin or a nail.”  

    The challenge is that you simply don’t understand exactly what you are searching for apart from it will be different than the relaxation of the pile. whereas massive facts capacity you may be capable of derive extra insights, discovering the meaningful facts and building detection algorithms that aren't brittle and might not rapidly turn into beside the point takes a deep understanding of fraud.

    That’s where behavioral analytics and adaptive detection strategies turn into vital. You want the way to isolate what is distinct that you may additionally have never considered earlier than.

    Analytics apps also have to be of price to fraud investigators, who're those with the adventure of the real world. Machines can burn during the statistics mountain to locate abnormal occurrences, however they are nowhere near pretty much as good as individuals at evaluating and knowing context. hence, the app has to facilitate the americans or fraud gadgets it serves, not burden them with delays or lengthy techniques.

    RSA’s Silver Tail programs product suite (which includes Forensics, Mitigator, and Profile Analyzer equipment) offers behavioral analytics, precise-time customized suggestions and mitigation capabilities, as well as a forensics interface for performing investigations.

    as an example, by means of monitoring the particulars of each HTTP/S request that is sent to a website, it learns what commonplace habits seems like for the website and can then determine criminal behavior and alert the acceptable groups for investigation. Alternately, it can communicate with other community devices to redirect or terminate the internet session in real-time.

    consumer-primarily based behavioral analytics (apart from the population-based ones) helps identify when a specific person is deviating from their average conduct, as is the case with many fraud and account compromise situations.  

    “Silver Tail become created by means of ex-eBay and PayPal fraud gurus who lived the battle towards fraud at a time after they have been conserving both most targeted manufacturers on the internet,” stated McKenna. “Their experiences encouraged them to keep up with transferring web fraud assaults as an alternative of playing trap-up.”

    visual Aids

    IBM is also concerned in fraud analytics by way of a mixture of IBM SPSS Modeler, SPSS records, SPSS Collaboration and Deployment features, SPSS statistics assortment, SPSS determination administration, IBM Cognos BI, Cognos express, Cognos insight, Cognos Disclosure administration and Cognos financial plan Reporting.

    IBM believes that visualization is a a must-have part in making sense of analytics counsel, specially when it involves facilitating the work of authorities in fraud detection.

    “As information volumes continue to move up, BI projects can advantage enormously from the use of counsel visualization innovations,” pointed out Don Campbell, CTO, IBM business Analytics. “decent visual representations can assist individuals intuitively consider advanced statistics. Visualization may also be an impressive presentation and suggestions consumption interface. nonetheless it does not stand by itself.”

    Graphical illustration is also a spotlight of 21CT, a issuer of analytics for investigation and pattern detection. It has released enhanced graph search for its LYNXeon product to help spot cyber attackers and document criminal conduct. The intent behind the product is that computing device -based mostly attacks may also be caught with the aid of machines, however human-based attacks require specialized equipment to aid americans notice them earlier than the harm is executed.

    “fitness care fraud, particularly Medicare, is an enormous difficulty, costing billions a yr,” pointed out Kyle Flaherty, vp of advertising at 21CT. “The problem is not best the significant amounts of statistics, however additionally within the disparate connections inside the records. moreover, fraud has been rampant in this enviornment and the malicious entities have develop into sophisticated.”

    whereas regular BI tools deliver perception into one slice of the common assistance, Flaherty referred to, they aren't decent enough for fraud detection, which is dependent upon operational perception and the skill for a human to hunt via visualized information, find hyperlinks, and become aware of universal patterns of endeavor.

    “Fraud detection is a human versus human combat, and BI tools can spit out more facts and graphs to aid illustrate the problem,” talked about Flaherty. “in the end you should be in a position to hop across the records and examine.”

    Drew Robb is a contract writer focusing on technology and engineering. currently residing in California, he is in the beginning from Scotland, the place he obtained a level in geology and geography from the college of Strathclyde. he's the writer of Server Disk management in a windows ambiance (CRC Press).


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    HRV-derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia | killexams.com real questions and Pass4sure dumps

    Introduction

    Anaesthesia is a significantly important procedure used in almost all surgery (Lan et al., 2012; Schwartz et al., 2010). General anaesthesia is a drug-induced and reversible condition that has specific behavioural and physiological effects such as unconsciousness, analgesia, and akinesia. Clinically and practically, routine observations such as those of heart rate, respiration, blood pressure, lacrimation, and sweating are used to assist doctors in smoothly controlling and safely managing anaesthesia. Nevertheless, patients recovering from general anaesthesia can experience significant clinical challenges, including airway and oxygenation problems, emergence delirium (Lepouse et al., 2006), cognitive dysfunction (Saczynski et al., 2012), and delayed emergence, and the elderly are particularly at risk of stroke and heart attack (Neumar et al., 2008). Accurate monitoring of the depth of anaesthesia (DoA) would thus contribute to improvements in the safety and quality of anaesthesia use and would provide a superior experience for patients.

    A state of general anaesthesia is produced by anaesthetics that act on the spinal cord and the stem and cortex of the brain (Brown, Purdon & Van Dort, 2011; Ching & Brown, 2014); monitoring of electroencephalogram (EEG) patterns is therefore useful (Niedermeyer & Da Silva, 2005). The two main indices derived from an EEG pattern are the bispectral index (BIS) (Aspect Medical Systems, Newton, MA, USA) (Rosow & Manberg, 2001) and entropy (GE Healthcare, Helsinki, Finland) (Viertiö-Oja et al., 2004); the former is obtained by calculating adjustable weights on the power spectrum, the burst suppression pattern, and the bispectrum of EEG data, whereas the latter is constructed by associating the data degree of disorder (entropy) with the consciousness state of patients (Liang et al., 2015; Viertiö-Oja et al., 2004). Although EEG-based spectral indices have been applied commercially for nearly 20 years, they are still not part of standard anaesthesiology practice (Purdon et al., 2015), and the reasons for this are complex. First, these indices were developed from adult patient cohorts, and are not strictly relevant to infants or younger patients, thereby providing lower accuracy (Samarkandi, 2006), and second, the indices cannot generate precise DoA measurements for certain drugs, especially when ketamine and nitrous dioxide are used (Avidan et al., 2008; Sleigh & Barnard, 2004). In addition, EEG signals are sensitive to noise, and therefore more complex algorithms and resources for noise filtering are required. Moreover, using disposable EEG electrodes is much more expensive than using other physiological signal sensors.

    To overcome some of these disadvantages and provide alternatives to EEG-based solutions (Ahmed et al., 2011), it is crucial to pursue new ideas to support mainstream methods. In this respect, the electrocardiogram (ECG) provides important clinical physiological signals and is highly recommended for continuous monitoring and ensuring international standards for the safe practice of anaesthesia (Merry et al., 2010). Different anaesthetics affect the QT interval of an ECG during anaesthetic induction (Oji et al., 2012), and rhythmic-to-non-rhythmic observations from the ECG can provide anaesthetic information (Lin , 2015). In addition, heart rate variability (HRV), related to autonomic regulation, is strongly affected by general anaesthesia (Hsu et al., 2012) and varies with respect to differing anaesthetic procedures used (Billman, 2013; Mazzeo et al., 2011); therefore, heartbeat dynamics are highly correlated with a loss of consciousness (Citi et al., 2012). Furthermore, ECG signals are more stable than EEG signals, which means that ECG is more resistant to noise even when cheap electrode sensors are used. HRV analysis thus can be used to estimate DoA. Moreover, interindividual variation is normal and is influenced by age, weight, and life habits, which means that the ECG-derived index more specifically reflects an individual’s anaesthetic state than EEG-based indices that assume one index value indicates the same consciousness level for all anaesthetics and patients (Purdon et al., 2015). Performing DoA research based on the HRV is thus valuable. However, it is important to guarantee that the ECG is free of artefacts and the ECG waveform (Q R S T waveform) is accurately recognised; otherwise, incorrect variation properties may ultimately be obtained, resulting in an incorrect R–R interval distribution.

    An artificial neural network (ANN) is an advanced modelling tool used in statistics, machine learning, and cognitive science (Alpaydin, 2014; Kriegeskorte, 2015). This bio-inspired method supports self-learning from complex data by organizing training pattern set and resultant errors between the preferred output and the subsequent network output. It has the great ability of non-linear, distributed, local, and parallel processing and adaptation and one of the most often used models in engineering applications. An ensemble artificial neural network (EANN) comprises multiple models and combines them to produce the desired output, as opposed to using a single model (Kourentzes, Barrow & Crone, 2014; Ripley & Ripley, 2001; Tay et al., 2013). Normally, an ensemble of models performs better than any individual model because average effects are obtained in ensemble models (Baraldi et al., 2013; Zhou, Wu & Tang, 2002). In summary, the neural network is a powerful and effective method for use in data regression and model optimisation of nonstationary data. In biomedical fields, neural networks play a crucial role in the analysis of complex physiological data (Amato et al., 2013).

    This study aimed to optimise an indicator index, known as the similarity and distribution index (SDI), that is derived from measurements of HRV (Huang et al., 2008). The SDI is proposed to evaluate the DoA from ECG signals occurring in the time domain during routine surgery, and thus differs from the methods previously described herein, which are based on extracting EEG spectrum features in the frequency domain. The time domain parameter is calculated by measuring the similarity between the statistical distributions of R–R interval measurements in consecutive data segments. In this study, results obtained using the proposed method are compared with the expert assessment of consciousness level (EACL), which is determined using the average evaluation of five expert anaesthetists after data and patient observation. The model is then optimised by applying an EANN for estimating the DoA. Through SDI extraction in the time domain and EANN modelling targeting the EACL, results show that it is possible to predict the DoA throughout an entire surgery.

    The remainder of this paper is divided into four sections. ‘Materials and Methods’ describes the general anaesthesia used, patient participants and data analysis methods employed; ‘Results’ presents the results of processing and comparisons with the EACL; ‘Discussion’ presents the discussion and study limitations; and the conclusion is provided in ‘Conclusions’.

    Materials and Methods Ethics statement

    All studies were approved by the Research Ethics Committee, National Taiwan University Hospital (NTUH), Taiwan, and written informed consent was obtained from patients (No: 201302078RINC). During the experimental trial, the hospital endeavoured to ensure that all scheduled surgery was performed very well on time.

    Standard anaesthetic procedure

    Anaesthesia is essential during surgery, and its associated procedures are outlined in Fig. 1 (Cornelissen et al., 2015). Anaesthesia generally involves end-tidal gas concentration over time, and routine anaesthetic practice consists of four stages: consciousness, induction, maintenance, and emergence (recovery) (Merry et al., 2010). Prior to surgery, patients were required to take nil by mouth for at least 8 h. After the electrodes were placed, each patient received the volume of anaesthetic agents appropriate for the routine operation. Unconsciousness is usually induced by intravenous propofol, another analgesic drug (such as fentanyl), and a muscle relaxant medicine (such as nimbex). Gas anaesthetics (desflurane, sevoflurane) together with air and oxygen were used to maintain sedation for most patients after the mask had been placed, whereas propofol was employed in some cases. As the end of surgery approached, additional drugs were administrated (such as morphine and atropine). Table 1 summarises detailed information. General anaesthesia was performed safely during all stages by monitoring physiological signals, such as EEG, ECG, photoplethysmography (PPG), and intermittent vital signs of blood pressure (BP), heart rate (HR), pulse rate (PR), and pulse oximeter oxygen saturation (SpO2). If any of these observation signals underwent irregular changes, the anaesthetist adjusted the intraoperative standard anaesthesia machine correspondingly.

    Figure 1: Anesthetic procedure. Table 1:

    Patients clinical characteristics and demographics.

    Values are means (SD). Some eligible subjects are excluded by reasons described in Fig. 2. Parameters Age (year) 49.0 (12.5) Male gender (%) 16.4%, n = 18 Height (cm) 158.7 (7.6) Weight (kg) 59.4 (12.7) BMI (kg m−2) 23.6 (4.9) Median duration of surgery (min) 120 (CI:113.9∼138.9) Anesthetic management Propofol induction (mg) 115.6 (34.3), n = 100 Fentanyl induction (mg) 95.5 (41.4), n = 100 Lidocaine induction (mg) 48.1 (6.5), n = 60 Glycopymolfe induction (mg) 0.2 (0.04), n = 64 Nimbex induction (mg) 9.5 (1.7), n = 50 Xylocaine induction (mg) 44.5 (9.0), n = 33 Rubine induction (mg) 0.2 (0.06), n = 32 Maintenance drugs infusion rate – Sevoflurane maintenance (%) 53.4%, n = 59 Desflurane maintenance (%) 35.5%, n = 39 Propofol maintenance (%) 29.1%, n = 32 Additional drugs administrated when approaching the end of surgery Morphine (mg) 4.5 (2.3), n = 47 Ketamine (mg) 29.8 (7.3), n = 25 Atropine (mg) 1.1 (0.4), n = 49 Vagostin (mg) 2.4 (0.2), n = 48 Data recording

    ECG data acquired in this study were obtained from patients undergoing surgery at the NTUH using chest-mounted sensors and a MP60 anaesthetic monitor machine (Intellivue; Philips, Foster City, CA, USA). The machine was connected to a recording computer installed with real-time software developed by our research team using a Borland C+ + Builder 6 developing environment kit (Borland Company, C+ + version 6); this software collected data at a sampling rate of 500 Hz. The sampling rates of the EEG and PPG continuous waveforms were 128 Hz. Intermittent vital signs (such as BIS, HR, PR, BP and SPO2) were recorded every 5 s.

    Figure 2: Study protocol. In fact, patients before this collection period were consulted for their eligibility, dozens of cases were excluded for analysis such as technical and clinical reasons. The 110 remaining subjects are intact for four stages of analysis to evaluate depth of anaesthesia (DoA). Their demography information is shown in Table 1. Clinical data collection

    Prior to collecting data in this study, patients provided written consent for participation. Demographic and clinical data, including height, weight, age, gender, operation time, surgical procedure, and anaesthetic management, were acquired by hospital staff from anaesthesia recording sheets. Other data relating to the research procedure, such as body movement and electrotome operation, were recorded by the research team. Regular hospital recordings and specific research notes were then integrated to serve as auxiliary clinical information.

    Patient participants

    Patients scheduled for elective surgical procedures were recruited from the preoperative clinic at NTUH in 2015. Eligibility criteria related to age, consent, and specific operation type. Those ineligible for inclusion were either (1) under 22 years old, (2) diagnosed with a neurological or cardiovascular disorder, or (3) undergoing surgery involving local anaesthetic rather than general. The selection procedure is illustrated in Fig. 2. According to these criteria, hundreds of patients were eligible for inclusion. However, it was unfortunately not possible to obtain data for all eligible patients (technique failure, procedure interruption), and ultimately data for 110 patients were acquired. General parameter information was obtained for all 110 patients. However, anaesthetic drug management differed with respect to individuals, although propofol and fentanyl inductions were implemented for most patients (n = 100). The detailed characteristics of the participants are provided in Table 1.

    ECG data preprocessing Data conditioning

    Data conditioning, or preprocessing, is critical for signal analysis for determining DoA and can overcome problems with compatibility and a lack of analysis in advance. It generally consists of data format conversion, noise removal, and data rearrangement. Due to limitations with data collection storage, an ASCII file format was used in this study. Prior to implementing the algorithm, data were transferred into a MATLAB workspace and the notch filter was then used to remove 60 Hz power line noise. All participant data sets were then manually inspected to determine specific segments of artefacts resulting in extremely abnormal QRS waveforms or ECG series saturation (for example, electrical artefacts caused by medical equipment or body movement), particularly for the R peak, which was previously impossible to recognise. Our algorithm was then applied to pre-processed data for further analysis.

    EACL

    It is common knowledge that no accurate standard index exists that is capable of symbolising a patient’s anaesthetic state during clinical surgery. Therefore, five experienced anaesthesiologists were asked to plot subjective scores relating to ‘state of anaesthetic depth’ versus time, based on the data recordings referred to in the previous section and their own rich clinical experience. These scores thus represented an EACL. Criteria determined by the five anaesthesiologists with respect to their assessments of consciousness level were based on both their clinical practice knowledge and supporting information recorded by two research nurses. Any clinical events and signs potentially related to DoA were carefully recorded. Recorded information included (i) intermittent vital signs (such as HR, BP, SPO2); (ii) anaesthetic events, including induction of anaesthesia, tracheal intubation and extubation, the addition of muscle relaxant reversal drugs, and managing airway suction; (iii) surgical events, including the start and end of surgical procedure and the occurrence of any specific noxious stimulus; (iv) clinical signs of the patient, including any types of movement and unusual responses and arousability during induction and emergence from anaesthesia; and (v) any other events that were considered relevant, such as patient demography.

    Figure 3: Flowchart design of expert assessment of consciousness level (EACL). Recordings are clinically related BP, HR, SPO2 and drug administration records; assessments are done by five experienced experts by plotting the DoA curves with range from 0 to 100. After using ANSYS to digitalize the curve value, we obtained the final gold standard by averaging the five doctors’ assessments. EACL: expert of assessment of consciousness level. Figure 4: One representative of EACL. From (A) to (E), it is the five doctors’ assessments, respectively; the final one (F) is the gold standard: EACL. The Red solid line is the mean value, the two green dashed line is mean ± std. Figure 5: Similarity and distribution index (SDI) definition protocol. ECG denotes step 1, R (n) means step 2. Step 3 includes D (n) and histogram. The histogram distribution is used for SDI computation.

    Based on these criteria (Liu et al., 2015), the assessment procedure used in this study to score DoA (Fig. 3) is described as follows. First, research nurses continually observed each patient’s state to record the information described above. Each anaesthesiologist then made a continuous assessment and noted changes in ‘the state of anaesthetic depth’ of patients during the entire operation, based on hospital formal anaesthesia records. To maintain consistency with the BIS, scoring used the range of 0–100, from brain dead to fully awake (a score of 40–65 represents an appropriate level of anaesthesia during surgery). Finally, because original assessments were drawn by hand, the results were digitised using web-based software (webplotdigitizer; ANSYS, Canonsburg, PA, USA) (Dorogovtsev & Mendes, 2013) and resampled every 5 s using MATLAB interpolation to ensure concurrence with the BIS index. The result was then considered to be an EACL. However, because the experience of each anaesthesiologist differed with respect to subjective EACL standards, and to minimise the consciousness level error as much as possible, the data obtained from the five anaesthesiologists were averaged. Figure 4 shows one EACL case example from the five doctors and the mean value of the five scores, where it is evident that the mean value better represents absolute DoA.

    Data analysis SDI definition of HRV SDI protocol.

    The SDI is based on HRV recorded in ECG data. The SDI is a time domain parameter index representing the degree of similarity between consecutive data segments and is obtained by computing the statistical distribution of the R–R interval variability difference. Figure 5 shows details of the entire procedure used to compute the SDI from ECG recordings. The steps involved are as follows:

    Step 1. Extract the R peak of the ECG signal to obtain the instantaneous R–R interval, R n . Resample the data using the commonly used algorithm of Berger to 4 Hz (Berger et al., 1986).

    Step 2. Calculate the difference between two consecutive heartbeat intervals: (1) D n = R n + 1 − R n n = 1 , 2 , 3 …

    Step 3. Choose any time point, t, and then select a data block, where the data block contains M data points. Compare the statistical distribution of consecutive blocks, one from D(t − M + 1) to D(t), the other from D(t + 1) to D(t + M). Distribution histograms of both data blocks are generated using the same cell size. The relative frequency of the D n value of the ith cell of the histogram is denoted P1(i) for the first data block and P2(i) for the second. Determination of the cell number is described in ‘Data analysis’ part B below. For example, in the first data block, the data value range is 0 to 0.5 s if 100 cells are chosen, and the cell width should be set as 0.005 s. This means that P1(1) denotes the relative frequency between 0 and 0.005; that is, P 1 1 = relative frequency 0 < D n < 0 . 005 , P 1 2 = relative frequency 0 . 005 < D n < 0 . 010 and so on. This is the same for the second data block.

    Step 4. After multiplying the relative frequency of corresponding cells in the histograms of both data blocks, the sum of the product value in all cells is the SDI, as calculated using the following equation: (2) SDI = 1 − ∑ i = 1 n P 1 i ∗ P 2 i × 100 , where n is the number of cells and P1(i), P2(i) are the relative frequencies of each cell in the histograms of data blocks 1 and 2, respectively. Theoretically, high similarity between the distribution features of ECG data means that patients are in a stable physical condition during surgery and that they are under a state of anaesthesia with high values of P1 × P2. When the sum is deducted by 1 and shows a lower SDI, the DoA is deeper. When the sum is multiplied by 100, the index value ranges from 0 to 100 and is consistent with clinically recognised consciousness levels, such as BIS values that range from 0 (deep coma) to 100 (awake state), thus making it easier to determine the DoA.

    Implication of SDI value.

    Mathematically, the SDI is obtained from measuring features of the statistical distribution between two consecutive data segments. For a stable HR pattern, the consecutive data segments should have high similarity and a histogram will show a consistent distribution when P 1 i and P 2 i fluctuate simultaneously. Under the condition of Eq. (2), the SDI is lower in this situation; therefore, a higher SDI symbolises a much more variable HR, which occurs frequently when a patient is awake or under minimal anaesthesia. In this instance, the SDI can be expressed in accordance with the BIS index.

    Figure 6: The flow chart of ensemble artificial neural network (EANN) model construction. Figure 7: One case demo of SDI. (A) shows one SDI curve derived from a case ECG data, (B) one is the corresponding EACL, in which the blue thick line is the average of other five doctors’ thin lines. Figure 8: Histogram distribution of correlation coefficient between SDI and EACL. Except one in negative correlation, others are positive values, of which most are located at high value section from 0.6 to 0.9. Table 2:

    The correlation coefficient comparison between EACL and both original SDI and ANN fitting SDI of 20 cases.

    The latter one has better performance except few cases. From p value (paired Student t test), the two groups are considered statistically significant. (P < 0.05 means statistically significant). Case Original SDI & EACL Fitting SDI & EACL 1 0.7456 0.8478 2 0.8263 0.8799 3 0.8756 0.9570 4 0.8812 0.9661 5 0.7752 0.8857 6 0.6732 0.7146 7 0.7078 0.7197 8 0.7818 0.7976 9 0.7764 0.8880 10 0.8400 0.9401 11 0.8397 0.8815 12 0.5817 0.6448 13 0.7833 0.7330 14 0.8585 0.9199 15 0.9073 0.8764 16 0.8445 0.8718 17 0.6938 0.7565 18 0.7736 0.8939 19 0.8994 0.9198 20 0.7902 0.922 Mean ± std 0.7928 ± 0.0830 0.8508 ± 0.0913 p-value 0.0420 ECG analysis

    Data from the 110 participants were analysed to obtain the SDI. For every case, the SDI was calculated using data from the entire operation procedure, including the awake, induction, maintenance, and recovery states. All data were obtained under different types of anaesthetics to guarantee compatibility, and parameters were selected empirically. Because D n was in the range of 0 ms to 0.5 ms, it was used as the length of the histogram. The number of cells used was 100–500, and the best performance was obtained for 250. Dividing the data range into 250 cells required a cell width of 0.002, and the data block, M, was set as 128. Sample frequency D n of 4 Hz was used, and thus one data block required 32 s. At any one time, 64 s of data (two 32 s data blocks) were required to calculate the SDI.

    ANN analysis

    The Pearson correlation coefficient was calculated for 110 intact cases. To measure the DoA accurately, regression analysis was conducted to compute the model. ANN analysis was utilised to determine the relationship between the SDI and EACL, thereby generating a more accurate output for evaluation. An ANN consists of three parts: an input layer, a hidden layer, and an output layer. In this study, a feedback propagation–type ANN was used, which is the most widely used type of ANN in machine learning. In previous studies (Huang et al., 2013; Liu et al., 2015; Sadrawi et al., 2015), nonlinear and nonstationary medical data were used with a back propagation network that had four layers: an input layer, two hidden layers with 17 and 10 neuron nodes, respectively, and an output layer. The number of nodes and layers used is widely known to affect the performance of an ANN, including the fitting effect and time elapse. From an engineering perspective, three to four layers are mostly used (Kourentzes, Barrow & Crone, 2014; Ripley & Ripley, 2001). In this study, different ANN topologies were tested, where the performance of the network varied as a function of the data type. A final topology was selected that obtained the highest accuracy in the shortest time.

    Because the SDI data series is being used as the input to obtain a result similar to the EACL, the SDI needed to be consistent with the EACL for each case. As previously mentioned, there were variations in the subjective opinions of the five anaesthetists who completed the EACL, which thus resulted in a low correlation coefficient due to the different assessments. Therefore, 105 out of 110 data sets that had correlation coefficients higher than 0.3 (most of the value distribution was much higher than 0.3, as shown in the following ‘Statistical distribution of the correlation coefficient’ were used for ANN regression. In addition, 85 data sets were used separately in the model’s construction: 70% were used for training, 15% for validation, and 15% for testing. To enable selection of the best neural network, 1,000 epochs were set, and a large volume of data was employed to guarantee that the ANN model had a favourable fit. After the ANN model was generated, 20 sets of data were used for pure-testing of the ANN model to validate its performance.

    The modelling procedure was repeated 10 times to generate 10 ANN models for cross-validation, and the procedure involved was as follows. The initial weights were set randomly, and as mentioned previously, the training was set to 1,000 epochs. The data were finally used to create 20 models for testing of model accuracy. The data were acquired from regular surgical procedures conducted in the NTUH using valid and strict operating procedures and identical regimes. Each model was totally different, due to the randomness of the initial weights. The performance for the cross-validation of 10 models was then calculated to check the variability of the ANN models. The results showed that a different model was created each time ANN training was performed, despite using the same data set for the training, validation, and testing. Cross-validation was conducted in a blind test to prove that there was no change in the regression result despite changes in the samples input.

    In addition, an EANN was employed to optimise the prediction results. Utilisation of an ensemble obtains higher accuracy than using other neural network approaches (Minku & Yao, 2012) and can address the trade-off between prediction diversity and accuracy within an evolutionary multiobjective framework (Chandra & Yao, 2004). As shown in Fig. 6, a single network model can be established with the random creation of initial weights, scales, and parameters. In this study, 85 data cases were used to generate 10 ANN models with different initial weights, and the 10 ANN outputs were then averaged to validate the 20 cases for optimising the regression effect. Because each ANN generates a different result with a different error, the average of the model outputs was calculated to overcome associated errors, thus creating an EANN to improve results. All data analyses were conducted with MATLAB (Mathworks, R2014b, US).

    Figure 9: Difference between the original SDI and fitting SDI for correlation coefficient, mean absolute error (MAE) and area under curve (AUC). All of them (A) Correlation Coefficient; (B)Mean Absolute Error and (C) AUC indicates that the fitting SDI has better performance. Table 3:

    The MAE between EACL and both original SDI and ANN fitting SDI of 20 cases.

    The latter one shows better performance except in a few cases. From p value (Paired Student t test), the two groups are considered statistically different indicating the good ANN fitting results. (P < 0.05 means statistically significant). Case Original SDI & EACL Fitting SDI & EACL 1 25.3235 2.9221 2 24.4898 3.1145 3 24.4483 8.9847 4 21.6974 4.6953 5 38.0500 6.3051 6 8.6140 9.0382 7 46.8434 11.4393 8 30.7200 4.5732 9 23.8712 6.0356 10 41.8986 14.1500 11 36.0559 3.1404 12 35.9865 3.5006 13 33.9785 5.3338 14 28.5371 5.0643 15 33.0614 9.2370 16 22.8827 4.0254 17 33.6476 7.6811 18 29.6125 9.1065 19 19.8529 3.5845 20 36.3620 4.3487 Mean ± std 29.7967 ± 8.7180 6.314 ± 3.1201 p-value 9.2214e−14 Table 4:

    The AUC between EACL and both original SDI and ANN fitting SDI of 20 cases.

    P value (Paired Student t test) show two groups are significantly different. The latter one has higher mean value and lower standard deviation. (p < 0.05 means statistically significant). Case Original SDI & EACL Fitting SDI & EACL 1 0.9493 0.9985 2 0.8805 0.9771 3 0.8992 0.9973 4 0.9013 0.9999 5 0.8272 0.9229 6 0.6574 0.8843 7 0.7386 0.8800 8 0.5786 0.8181 9 0.9691 0.9692 10 0.9781 0.9878 11 0.9926 0.9557 12 0.9990 0.9213 13 0.9575 0.9120 14 0.8326 0.9892 15 0.7216 0.9141 16 0.9059 0.9520 17 0.9876 0.9874 18 0.8992 0.9993 19 0.8508 0.9921 20 0.9408 0.9924 Mean ± std 0.8733 ± 0.1176 0.9525 ± 0.0510 p-value 0.0088 Statistical analysis

    Statistical analysis was performed using SPSS (IBM v22, North Castle, NY, USA) and MATLAB. To evaluate the ANN effect, the performance of the original SDI was compared with the one random ANN regression–derived SDI. The Pearson correlation coefficient, mean absolute error (MAE), and area under the curve (AUC) for the EACL were computed and considered the gold standard. The receiver operating characteristic (ROC) curve was calculated to obtain the AUC, which is often used in medical fields during diagnosis of disease. The binary threshold used to distinguish between anaesthesia and consciousness was set to 65 (Johansen & Sebel, 2000). The parametric paired Student’s t-test was then used to assess the statistical significance. To prove the capability of the EANN-derived SDI to measure DoA, its relationship with EACL was analysed. Furthermore, the commonly used BIS was used as a reference. The same significance test was also undertaken between the two indices, thus demonstrating a solid and convincing result.

    Results Demonstration of typical SDI pattern

    Figure 7A shows a typical SDI trend for a representative patient, and Fig. 7B displays the corresponding EACL obtained from the scores of five experienced and professional anaesthesiologists. The DoA is shown to change throughout the operation, where a higher value denotes a lower level of consciousness. After induction, the SDI falls sharply, although some variation exists in the maintenance period, and the SDI increases dramatically during emergence from anaesthesia. Generally, it corresponds with the fluctuations of EACL.

    Statistical distribution of the correlation coefficient

    To determine the coefficient distribution characteristics of all 110 data sets, a histogram with a cell width of 0.1 was constructed (Fig. 8). Most of the data values are located in the range from 0.6 to 0.8, with mean ± SD equal to 0.78 ± 0.16, which reflects a strong relationship with the EACL. Only five cases show extremely low correlation, these cases were just discarded.

    Comparison between performance of original SDI and SDI fit using an ANN

    An ANN model can be trained to model nonlinear behaviour and was used to accurately evaluate DoA in this study. Twenty data sets were used to quantify the optimisation effect, and a comparison was then made to validate the ANN effect. The correlation coefficients between the EACL and both the original SDI and ANN-derived SDI for cases 1 to 20 are presented in Table 2. It is evident that the ANN-derived SDI has significantly improved correlation with the EACL compared with the original SDI (p < 0.05). From the mean value of the statistics shown in Fig. 9A, it is clear that the ANN-derived SDI has superior performance. Table 3 compares the MAE results in the shape of correlation coefficients. The MAE fitting results obtained for the ANN-derived values are much smaller than those obtained without the ANN, which demonstrates that the ANN performed favourably. It decreases the difference much from the EACL by showing the statistical results in Fig. 9B significantly (p < 0.05). In addition, the AUCs of both the original SDI and the ANN-derived SDI for the 20 cases were calculated, and the results are shown in Table 4. Furthermore, the ROC curve for one case is presented in Fig. 10 and proves that the optimised SDI evaluates the level of consciousness more accurately. Figure 9C shows that the AUC of the ANN-derived SDI is 0.95 ± 0.05, much higher than that of the original SDI. The paired Student t-test was then used to determine the difference level between the two groups. The comparison reveals a statistically significant difference (p < 0.05), indicating the favourable fitting effect for the SDI using the ANN. From the relationship and the value difference, it is evident that the ANN-derived SDI measures the DoA more accurately than the original SDI.

    Figure 10: The receiver operating characteristic (ROC) curve of original SDI and artificial neural network (ANN) derived one. Both show the prediction of DoA features (AUC > 0.5). The ANN fitting SDI (blue curve) has larger AUC than the original SDI (red one), indicating better ability to predict DoA. Figure 11: One typical representative of the ANN regression effect for SDI. The blue line represents the ANN derived output; it has more similar fluctuation rhythm with EACL (black line). Relatively, the original SDI (red line) shows weaker relationship.

    A typical ANN-derived curve is displayed in Fig. 11; the results were derived from the case shown in Fig. 7. Clearly, the ANN-fitted SDI is superior to the original SDI, which varies sharply at the induction stage, whereas the ANN-derived SDI is basically consistent with the EACL. Furthermore, the original SDI reaches zero during the early maintenance period, which is definitely unreasonable.

    ANN blind cross-validation

    The results detailed demonstrate that the ANN model improves the SDI performance. However, because only one ANN model test was conducted, a blind cross-validation test was conducted using the previously mentioned 20 cases to ensure that the ANN model was efficient. The results are presented in Table 5 and reveal that all 10 ANN models used for the 20 cases provide similar results. The same validation test was used for the MAE (Table 5). This demonstrated that the samples selected do not affect the construction and effectiveness of the ANN.

    Table 5:

    The correlation coefficient and MAE (mean ± std) between 10 group ANNs fitting SDI and EACL of 20 cases.

    From the mean value comparison, it proves the ANN performance regardless of different input case data. Case Correlation coefficient MAE 1 0.8508 ± 0.0913 6.314 ± 3.1201 2 0.8346 ± 0.0952 4.8873 ± 1.9292 3 0.8417 ± 0.1025 5.8552 ± 2.6317 4 0.8378 ± 0.0972 5.1737 ± 2.2588 5 0.8398 ± 0.0945 4.9005 ± 2.1774 6 0.8459 ± 0.0933 4.9101 ± 2.1289 7 0.8448 ± 0.0921 4.8997 ± 2.2364 8 0.8158 ± 0.0976 6.0248 ± 2.5059 9 0.8340 ± 0.0959 5.4458 ± 2.4640 10 0.8507 ± 0.0899 5.5916 ± 2.5198 EANN-derived SDI compared with the BIS

    To further improve the regression performance of the ANN, an EANN was utilised to predict the DoA. Figure 12A shows that the ANNs had little variance in terms of the correlation coefficient. The EANN has the highest correlation and the lowest standard deviation, thereby proving the superior performance of the EANN. In addition, the MAE distribution is shown in Fig. 12B. The individual ANNs had similar characteristics. In addition, the EANN has the lowest MAE, which is consistent with the correlation coefficient results.

    In comparison with the commonly used BIS, Fig. 13 shows that the EANN-derived SDI performs better than the BIS evaluation when referring to the EACL as the gold standard. Differences in terms of the correlation coefficient, MAE, and AUC are all significant (p < 0.05 parametric paired Student’s t-test). We also chose one representative case for which to plot the ROC curve for both the BIS and EANN-derived SDI (Fig. 14), where the AUC illustrates better discrimination between anaesthesia and consciousness. Tables 6 and 7 provide detailed results for the EANN and BIS over 20 cases, respectively.

    Figure 12: The mean value and standard deviation statistics of ANNs and the EANN. (A) correlation coefficient; (B) mean absolute error. (A) shows that the ANN has little fluctuation difference regardless of input training data in terms of correlation coefficient. The EANN has the highest correlation with lowest standard deviation to prove the better performance of EANN. MAE distribution is given in (B). As to individual ANN, they have similar ability, but not significantly. Similar to the result of correlation coefficient, EANN has almost the lowest MAE. Figure 13: Difference between the BIS and EANN derived SDI for correlation coefficient, MAE and AUC using EACL as gold standard. (A) means correlation coefficient, (B) denotes MAE and (C) shows AUC; all of them indicate the EANN derived SDI behaves better. Asterisk * represents the significant difference (p < 0.05, parametric paired student test). Figure 14: The ROC curve of BIS and EANN derived SDI from the representative case using EACL as gold standard. Both show good capability of DoA prediction (AUC > 0.5). The EANN derived SDI (blue curve) has larger AUC than the BIS (red one), indicating better performance. Table 6:

    The correlation coefficient and MAE value between EACL and EANN fitting SDI of 20 cases.

    Compared with all single ANN performance in Tables 4 and 5, the mean of correlation coefficient of 20 cases here is higher with lower standard deviation, while the MAE also proves this with lower mean and standard deviation, meaning that the EANN perform better than just one single ANN. Case Correlation coefficient MAE 1 0.8413 2.1975 2 0.8871 3.1593 3 0.9497 6.8287 4 0.8994 4.6681 5 0.8404 6.1740 6 0.8081 4.3851 7 0.7286 8.0616 8 0.8704 3.4809 9 0.8799 3.1161 10 0.9411 2.3909 11 0.8477 2.9354 12 0.7722 4.9511 13 0.7716 4.7145 14 0.9041 3.4764 15 0.8736 6.4892 16 0.8848 8.3562 17 0.7667 3.5179 18 0.8385 6.5030 19 0.9127 2.4303 20 0.9145 3.1895 Mean ± std 0.8566 ± 0.0612 4.5513 ± 1.9049 Table 7:

    The correlation coefficient, MAE value and AUC between EACL and BIS of 20 cases.

    These results are used to make comparison with EANN derived SDI. Significance test results are shown in Fig. 13. Generally, the BIS has weaker evaluation of DoA compared to EANN derived SDI in Table 6. Case Correlation coefficient Mean absolutely error AUC 1 0.7746 7.5005 0.9951 2 0.7798 4.9937 0.8878 3 0.621 17.7697 0.7919 4 0.3891 10.4033 0.9423 5 0.8188 6.4099 0.9995 6 0.555 20.6271 0.8773 7 0.7116 14.7956 0.9031 8 0.5617 6.1885 0.8036 9 0.574 9.7251 0.9884 10 0.7187 8.7184 0.9848 11 0.6139 8.8011 0.9703 12 0.694 9.9009 0.9302 13 0.6949 12.3573 0.976 14 0.6507 7.5062 0.996 15 0.5636 10.4242 0.861 16 0.663 8.0178 0.9758 17 0.8089 7.4653 0.9815 18 0.8937 8.8475 0.9942 19 0.7989 5.8428 0.9914 20 0.7553 9.0309 0.9782 Mean ± Std 0.6821 ± 0.1164 9.7663 ± 3.8673 0.9414 ± 0.0637 Discussion

    Doctors use many observations and physiological vital signs to evaluate level of consciousness during clinical operations. The medical parameters are usually HR, BP, and photoplethysmography (Merry et al., 2010). However, because these parameters cannot accurately represent the actual DoA, researchers have been developing new methods for this purpose. For example, auditory evoked potential (AEP)- and EEG-related indices (which are mentioned in ‘Introduction’) such as BIS or entropy have been employed to quantify DoA (Liu et al., 2015; Nishiyama, 2013; Rosow & Manberg, 2001), and such indices are powerful and effective to some extent. An SDI method, which is based on ECG signals, is proposed in this study to measure DoA. The SDI method has a strong relationship with HRV, which is correlated with autonomic nervous system (ANS) function. Such function is seriously affected by anaesthesia (Hsu et al., 2012; Tarvainen et al., 2010), and because this fact is widely accepted in the field of anaesthesia, the ECG has often been used in DoA research.

    Our aim was to construct a practical ECG-derived index, and thus the SDI proposed in this study is constructed to correspond with the EACL, the gold standard that researchers adhere to when developing methods of measuring DoA. EACLs were thus obtained by our research team members, which involved a large amount of effort and endeavour. Although DoA was clinically scored by experienced anaesthesiologists in this study, there were limitations associated with the subjective opinions of each anaesthesiologist, and it was thus necessary to collaboratively score certain cases. The aim of this paper was to propose the use of the SDI to measure DoA; thus, the SDI still requires certain future improvement with respect to the mathematical principles used. For example, the SDI is affected by ECG data fluctuations, which are related to the distribution and similarities between data block points. Parameter selection details must also be further investigated. Moreover, it is necessary to obtain a clearer understanding of the comparisons made between the SDI and the BIS, AEP, or entropy. It is considered that both EEG-derived and ECG-derived indices provide specific and useful features, and therefore further research is required in this respect.

    The ANN regression model used herein was obtained from a predefined framework of an initial neural network based on our previous engineering research experience (Jiang et al., 2015; Liu et al., 2015; Sadrawi et al., 2015). However, it would be beneficial to investigate the ANN’s parameters, such as numbers of layers, number of nodes in each layers, and type of ANN (Hinton et al., 2012), and to discuss the weights and expiration criteria for the maximum optimisation of the performance.

    Mathematically, the SDI does not represent heart rate or HRV but quantifies the difference between two consecutive data blocks (as explained in detail in ‘Materials and Methods’). When the difference is higher, the SDI value is also higher. The index is presumably affected by the shape of the distributions, as well as their similarity. If P1 and P2 are identical but both show either a uniform distribution (each value equally likely) or are deterministic (only a single value occurs in both), for example, different SDI will result. In the latter case, the SDI =1 − 12 = 0, and in the former case, SDI =1 − 100 × (1∕100)2 = 0.99, for n = 100. Therefore, the SDI not only measures similarity but is also affected by D(n), which means it can represent ECG data variability. Instead of simply using the correlation coefficient between the ECG and EACL as a definition of the SDI, which would be less dependent on shape, we used the procedure outlined in ‘Data analysis’, part A, to define the final standard SDI. Although an ANN has a relatively complicated relationship with DoA, it is utilised for the regression and an output is obtained to quantify DoA, thus solving the nonlinearity between the SDI and DoA. In addition, when patients are conscious, the ANS has a regulation function that affects ECG signals. Certain types of heart disease influence HRV (Mazzeo et al., 2011) and probably also the SDI. It is thus urgent for us to validate and optimise this potential effect, even though the regression results appear to be favourable. We do, however, assume that the SDI is not currently suitable for use in all occasions, and research is thus required to explore and amend any problems with the algorithm.

    Although data from more than 100 cases were collected to build the SDI and the results demonstrate favourable performance, most of our cases were middle-aged patients. Therefore, it is necessary to obtain more data from young patients to verify our methodology (Cornelissen et al., 2015; Gemma et al., 2016), Surgery is conducted with respect to certain protocols and patient safety is always the priority; therefore, the anaesthetic drugs used for the patients in this study were all chosen by experienced anaesthesiologists, who perhaps favoured the use of particular drugs. Although other types of drugs could also deliver successful outcomes (Mawhinney et al., 2012; Schwartz et al., 2010), the data obtained during the maintenance period were only related to the administration of propofol, sevoflurane, and desflurane (Table 1). It is thus necessary for us to obtain data based on the use of other drugs such as medetomidine, isoflurane, and nitrous oxide (Kenny et al., 2015; Purdon et al., 2015), which may enhance index compatibility. In addition, mixed anaesthetic agents were given to the patients, which made it difficult to evaluate the capability of the SDI to reflect the use of one specific drug regime. Furthermore, our data are obtained from routine surgery performed in a hospital and do not involve any other clinically specific anaesthetic settings; thus, investigations of this aspect would also be useful. We will conduct future experiments using related data, and strict and rigorous comparisons will be made between indices. Future efforts will be made to investigate and update our algorithm and to determine the possibility of improving DoA evaluation accuracy through a combination with BIS or entropy, for example, or consideration of different surgical circumstances.

    Another issue to be considered is the spectral analysis of the ANS. ANS function has been widely employed in the assessment of DoA using ECG frequency domain features (Guzzetti et al., 2015; Lin et al., 2014). Previous articles have mainly focused on the ratio between high and low frequency powers. Galletly et al. (1994) described the spectral influence of several common anaesthetic agents on HRV, which provides directions for spectral part analysis. In addition, multitaper time frequency analysis was undertaken for autonomic activity dynamics evaluation in Lin et al. (2014). Nevertheless, future research on spectral analysis is required to pursue the promising and valuable integration with the present temporal analysis. Finally, although the results of this work symbolise DoA from the perspective of the ANS, we also aimed to provide an alternative to EEG-derived evaluation (Purdon et al., 2015; Samarkandi, 2006; Sleigh & Barnard, 2004). Based on the results of this research, it is considered that to overcome the disadvantages of EEG-based methods, studies should be initiated using a combination of EEG- and ECG-based methods.

    Conclusions

    In this study, physiological data from 100 participants were analysed to determine the ability of our SDI algorithm to evaluate DoA. ECG data were used to derive the SDI, representing the differences in HRV to demonstrate the ability of the SDI to measure DoA. To optimise prediction accuracy, ANN models were constructed and blind cross-validations were performed to conduct a regression test. In addition, an EANN was employed to overcome random errors and overfitting of the ANN models. This study indicated that HRV analysis has the potential to become another effective method for the evaluation of DoA. However, because there is a current lack of ideal measurement methods for the assessment of patient consciousness level, it is considered that incorporating the SDI into other methods would be useful. Therefore, combining the use of the SDI with other physiological medical signals relating to anaesthesia, such as EEG signal, would also be meaningful and helpful in improving the accuracy of DoA evaluation.


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    Modeling the Impact of Antibiotic Exposure on Human Microbiota | killexams.com real questions and Pass4sure dumps

    Characterization of patients and sputum samples

    The patients and sputum samples included in the training and validation data sets were characterized with respect to two types of variables (Table 1 and 2). Fixed, patient-specific variables included gender, CFTR genotype, and disease severity (or aggressiveness) phenotype21,22. Time-dependent, sample-specific variables included patient age, lung function, and disease stage21 at the time of sample collection. Lung function was measured as percent predicted forced expiratory volume in one sec (%FEV1). Disease stage was defined as early when serial %FEV1 measures were >70; intermediate when %FEV1 measures were between 70 and 40; and advanced when %FEV1 measures were <40. The dominant operational taxonomic unit (OTU) detected in each sample by deep-sequencing was also included as a sample-specific variable. This was defined as the most abundant OTU detected in the sample. Bacterial community diversity of each sputum sample was measured by calculating the inverse Simpson index, which takes into account both the number of OTUs (richness) present in the sample and their relative abundance (evenness).

    Table 1: Characteristics of patients in the training and validation data sets Table 2: Characteristics of samples in the training and validation data sets

    The training set samples (n = 116) were from six men previously described by us in detail21. All six patients had a mild or moderate disease severity phenotype. Samples were collected when these patients were between 18 and 30 years of age. The samples were roughly evenly distributed among periods when these patients were in early, intermediate, or late stages of lung disease. The dominant OTU in most (82%) samples represented the genus Pseudomonas. The patients (n = 60) and sputum samples (n = 362) in the validation set were more heterogeneous. Men represented 53% of patients and there was a greater distribution of patients with mild, moderate and severe disease severity phenotypes. The validation set samples were also more diverse with respect to the dominant OTU detected. Of note, none of the patients in either the training set or the validation set were smokers.

    Antibiotic weighting score development

    The antibiotic exposure associated with each sample was measured by assessing the antibiotic administration to the source patient during a 30-day window prior to sample collection. The duration of exposure (no. of days receiving the antibiotic), the timing of administration relative to the day of sampling (e.g., 20 days vs 2 days prior to sampling), and the antibiotic class and route of administration were determined for each sample.

    These variables were used to develop antibiotic weighting components that yielded scores used as covariates in models predicting bacterial community diversity.

    Weight component A

    (wcA; Equation 1 in Materials and Methods, and Fig. S1) accounts for the duration of antibiotic use during this 30 day window by assessing the number of days an antibiotic was (wcA = 1) or was not (wcA = 0) administered (Fig. 1A). wcA for each antibiotic was determined from observed sample level data without subjective assessment and was constructed similarly for all samples in both the training and validation data sets.

    Figure 1: Antibiotic weight components (A) (wcA) and B (wcB). Figure 1

    Panel (A) depicts daily wcA values for patient P2 during the 30 days prior to collection of sample 27. This patient received four antibiotics during this time (tobramycin-IV, meropenem-IV, ciprofloxacin-PO, and doxycycline-PO). A value of 1 indicates antibiotic administration on that day, while 0 indicates no antibiotic administration. Panel (B) depicts wcB profiles during the 30 days prior to sampling. These profiles indicate equal weighting (black) as well as linear (red), concave (blue), and convex (green) increasing weights as days approach the sampling time. The data points for each profile (circles) were drawn based on values calculated by Equation 2 in the text and each value was listed in Table S1.

    Weight component B

    (wcB; Equation 2 in Materials and Methods) accounts for the proximity of antibiotic use relative to the sampling day. Four weighting schemes, described by the formulas in Equation 2, were assessed, including (i) an equal weight for each day irrespective of proximity to the sampling date, (ii) a linear increase in weights with increasing proximity to the sampling date, and either (iii) a concave or (iv) a convex increase in weights with increasing proximity to the sampling date (Fig. 1B and Table S1). A score for each antibiotic administered during the 30 days prior to the date of each sputum sample was calculated as a product of wcA and wcB (Equation 3 in Materials and Methods). The sum of the scores for all antibiotics administered in association with a sputum sample was calculated to provide the total antibiotic exposure for each sample (Equation 4 in Materials and Methods).

    Using the total antibiotic exposure for each sputum sample, the training dataset was analyzed to determine which of the four wcB weighting schemes best predicted the inverse Simpson index, which had been previously calculated for each sample, based on the Akaike Information Criterion (AIC), after adjusting for age and %FEV1 at the sampling time. A comparison of AICs indicated that the convex increasing weighting scheme provided the best prediction for the inverse Simpson index (Table S2). When the larger and independently sampled validation sample set was similarly analyzed, using the same four wcB weighting schemes, the convex increasing weighing scheme again provided the best prediction of the inverse Simpson index. Analyses on 500 bootstrap samples from the validation set demonstrated the stability of this wcB weighting scheme. The wcB convex increasing weights were ranked as the best fit in 75% of the bootstrap samples, outperforming the other choices, and were therefore used in the remainder of the study whenever wcB was considered.

    Weight component C

    (wcC; Equation 5 in Materials and Methods) accounts for the effects of antibiotic type and route of administration on predicting the inverse Simpson index. Since only 16 of the 37 antibiotic types observed in the validation set were used in the training set, we based our initial evaluation of this weight component on analyses of the combined training and validation sets. First, the ability of each antibiotic associated with a sputum sample (i.e., administered within 30 days of sample collection) to predict the inverse Simpson index was assessed, based on the wcA and wcB weighting components and adjusting for age and %FEV1 at sampling. No significant interactions between multiple antibiotics were detected. Next, the AICs for the 37 antibiotic types were modeled and ranked from lowest (best prediction model) to highest (worst prediction model). Antibiotic type coefficients from the 37 AIC models were also ranked based on largest to smallest impact on inverse Simpson index (Table S3).

    The AIC and coefficient ranks were summed and sorted from lowest to highest and grouped into terciles. wcC values of 0.5, 0.33 or 0.17 were assigned to each combination of antibiotic type and route of administration for the best, intermediate, and worst predictors of the inverse Simpson index, respectively (Equation 5 in Materials and Methods and Table S3). Antibiotics administered by the IV route were more likely to be in the top tercile (i.e., wcC value of 0.5) than were antibiotics administered orally or by inhalation (50% of IV administered antibiotics had wcC of 0.5 compared to 16% of oral/inhaled antibiotics; Fisher's exact test p = 0.04).

    The combined antibiotic weighting score (Equation 6 in Materials and Methods) for each sputum sample was calculated by multiplying wcA, wcB, and wcC for each of the 30 days prior to sputum collection and then summing these scores across the 30 days. Inclusion of wcC improved the prediction of community diversity (inverse Simpson index) in each of the training, validation and combined sets as opposed to using only wcA and wcB alone. A permutation test indicated that inclusion of wcC in the combined antibiotic weighting score gave a significantly lower AIC value (better model fit; p < 0.001) than would have occurred under 5000 random permutations of the wcC values across the 37 antibiotic types. The distribution of the combined antibiotic load score across validation samples is shown in Figure S2.

    Predictors of community diversity in CF

    To illustrate the utility of the antibiotic weighting scoring schemes, we included the combined antibiotic weighting score as a covariate in a multivariate model predicting community diversities in our collection of CF sputum samples. This allowed us to explore associations between bacterial community diversity (inverse Simpson index) and the patient- and sample-specific variables associated with these samples. Since disease stage was defined based on lung function (%FEV1), these correlated variables could not both be included in the model. We therefore included disease stage as a covariate in the model since it has a stronger predictive ability (AIC 1572.14) than lung function (AIC 1579.56).

    Table 3 shows results from the multivariate linear mixed model predicting the inverse Simpson index in the validation set. An increase of one unit in the antibiotic weighting score calculated over the month prior to sampling is associated with a 1.25 point decrease in the inverse Simpson index on average (95% CI, 0.44, 2.05 point decrease, p = 0.002) after adjusting for dominant OTU, disease severity, gender, CFTR genotype, disease stage, and patient age. We observed significant differences in community diversity with respect to dominant OTU (composite P < 0.001). Samples dominated by Pseudomonas or Burkholderia showed the least diversity, while samples dominated by Streptococcus showed the greatest diversity (approximately 3 inverse Simpson index points higher than Pseudomonas and Burkholderia, on average) after adjusting for age, gender, disease severity, disease stage, CFTR genotype, and antibiotic weighting in the previous month. On average, men had an approximately 1 point higher inverse Simpson index than women after adjusting for other factors (p = 0.004). We observed decreasing diversity with advancing disease stage after adjusting for other factors; on average, early disease stage had a 1.15 point higher inverse Simpson index than late disease stage (p = 0.02). We did not observe significant associations between community diversity and disease severity or CFTR genotype after adjusting for age, gender, dominant OTU, disease stage, and antibiotic use in the previous month. In addition, we did not observe a significant decrease in diversity with increasing age after adjusting for other factors.

    Table 3: Multivariate linear mixed model including antibiotic use as a covariate

    Figure 2 shows the community diversity of a “standardized” CF patient predicted by specific variables after adjusting for other variables. For example, after controlling for other variables, the estimated community diversity is 5.7 if the community is dominated by Streptococcus and 2.6 if dominated by Burkholderia.

    Figure 2: Estimated community diversity by each predictor. Figure 2

    Pseu: Pseudomonas, Burk: Burkholderia, Strep: Streptococcus. The predicted values by each predictor were calculated by controlling for other predictors based on the “standardized” CF patient profile: 57%, 12%, 14%, and 17% chance of being dominated by Pseudomonas, Burkholderia, Streptococcus or other bacteria, respectively; 18%, 49%, and 33% chance of being in early, intermediate or late disease stage, respectively; 22%, 30%, and 48% chance of having a mild, moderate or severe disease phenotype; 64% chance of being male; 44%, 44% and 12% chance of being delta F508 homozygous, delta F508 heterozygous or another CFTR genotype, respectively; average age = 28.13 years and an antibiotic load = 0.11.



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