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SAN JOSE, Calif., Nov 02, 2013 (enterprise WIRE) -- HP join-- At this week's HP connect NonStop advanced Technical Boot Camp, Tributary techniques announced customary availability of utility-best Tributary StoreOnce virtual Media manager(C) (TSI-SOVMM(C)).
Tributary StoreOnce virtual Media manager (TSI-SOVVM) is a software-simplest superior media manager for connecting and managing fiber channel-connected HP-StoreOnce digital Tape Libraries without delay from the NonStop host. obtainable most effective from Tributary programs, TSI-SOVMM runs on all NonStop Integrity and Blade servers working NonStop RVUs J06.12/H06.23 and later.
"Now, NonStop users who desire the total function set attainable from the HP StoreOnce statistics deduplication machine can at once connect, manipulate and manage digital tape libraries configured on HP StoreOnce," referred to Tributary techniques CEO Shawn Sabanayagam. "additionally, the need for physical tape is awfully true, principally in industries with longer term data retention requirements. With SOVMM-managed StoreOnce instruments and host-attached physical tape, TSI gives the person the option of both bodily and practically kept statistics. Priced at handiest $15,000 for the host-based software, here's a less high priced solution than one requiring an middleman Linux-primarily based server."
TSI-SOVMM contains enhancements to the Tributary-developed Tandem Library Media manager, Silo Media supervisor and Entry level Media supervisor items that NonStop has sold for 14 years.
Key facets of Tributary StoreOnce digital Media manager include:
-- "Plug-and-Play" host-based mostly utility requires no hardware changes or "in-between" bins
-- Runs on all NonStop RVUs J06.12/H06.23 and later
-- No have an impact on to present NonStop attached physical tape devices
-- makes it possible for full usage of all StoreOnce points, including replication and records de-duplication
-- contains computerized tape labeling and superior backup scheduling
-- Writes all information to the HP StoreOnce VTL in NonStop tape layout
-- helps multiple VTLs with (for all practical applications) unlimited digital cartridges on a single TSI-SOVMM license
-- supports fiber channel-connected StoreOnce models 42xx, 44xx and B6200
-- helps NonStop IOAME/VIO/CLIM fiber channel attachment
-- Has intuitive convenient-to-use GUI for windows
seek advice from Tributary in booth 27-28 at HP connect NonStop advanced Technical Boot Camp Nov 3-5.
Tributary methods (www.tributary.com) develops, sells and helps unified backup storage, far off replication and catastrophe restoration options. Tributary has been an o.e.m agency to HP for 17 years. Tributary shoppers latitude from the Fortune 20 to SMB agencies in financial functions, telecom and retail/distribution.
source: Tributary programsdemonstrate contact: Kevin St. Cyr, 817-291-2592, firstname.lastname@example.org Press contact: Hellena Smejda, 352-875-8156, email@example.com
Copyright company Wire 2013
a little greater than a yr after Hewlett-Packard begun to present its company-important programs (BCS) with Intel Xeon E7 microprocessors inside, the company reports that it sees some recovery in earnings of such machines. whereas BCS revenues continue to decline, shipments of x86 servers for mission-important workloads are beginning their suit boom.
For many years, corporations like IBM, HP and different server makers offered its enterprise-essential systems that includes proprietary microprocessors like Intel Itanium, IBM vigour, PA-RISC in addition to very advanced software, corresponding to IBM AIX, HP NonStop, HP-UX or different Unix working programs to clients who vital maximum reliability. Such machines have been very expensive to purchase and to retain. ultimately, a lot of Linux working methods grew to be significantly greater mature and authentic than they have been lower back within the early 2000s, which enormously reduced adoption of Unix and proprietary machines.
as a result of the aforementioned industrial trends as well as sluggish progress of Intel Corp.’s Itanium processors, revenue of HP’s BCS servers started to say no in 2007 – 2009. After it grew to be obvious in 2011 – 2013 that both Intel and HP don't plan to make investments plenty in building of Itanium, income of HP’s enterprise important systems began to waterfall.
This led HP to introduce its first Intel Xeon E7-powered NonStop X machines for BCS applications in November, 2013, after which to observe with Integrity Superdome X enterprise-vital servers featuring Intel Xeon E7 in December, 2014. The NonStop X machines run NonStopOS, whereas Superdome X supports SUSE Linux commercial enterprise 11 carrier Pack three, crimson Hat commercial enterprise Linux 6.5, 6.6, and seven.0. while HP nonetheless presents servers that includes Itanium and HP-UX, their popularity continues to decline, dragging the business’s revenues down. fortunately, it feels like HP’s valued clientele are at last starting to decide on the company’s x86 BCS machines.
“We saw more suitable performance in business-crucial systems and our new items are gaining traction with valued clientele,” observed Meg Whitman, chief government officer of HP, all over quarterly conference name with traders and monetary analysts. “during the quarter, we expanded our server portfolio with HP Integrity Superdome X and HP Integrity Nonstop X. These platforms run our clients’ most disturbing mission-crucial workloads on x86 architectures, with greater degrees of efficiency, scalability, availability and effectivity.”
in line with HP, earnings of BCS are nevertheless declining (salary in Q1 FY2015 have been down 9% yr-over-year to $208 million), but the company claims that it saw some recuperation in business-essential systems all over the quarter, an excellent news for Hewlett-Packard.
HP knows that finally revenue of Unix- and Intel Itanium-based mostly servers will drop to zero, but at present doesn't predict when this is expected to happen. even so, given the indisputable fact that the business now offers each Superdome and NonStop servers that includes x86 processors, sales of Itanium-primarily based programs will not going rebound. nonetheless, on the grounds that Unix systems are used for a long time (and even a long time sometimes), HP will proceed to record IA64 servers for somewhat a long time from now.
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KitGuru Says: despite of the undeniable fact that formally Intel Itanium nonetheless exists, its period has ended and it'll not ever come lower back.
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Hewlett-Packard on Monday launched a brand new line of Integrity NonStop servers that the enterprise says presents stronger performance over its other entry-level servers that require excessive uptime.
The NonStop Integrity NS2000 server family unit is focused at small and medium-sized groups looking for fault-tolerant servers that require close to one hundred percent availability. reckoning on consumer needs, the rackmount servers might go into small or colossal statistics facilities.
The servers encompass Intel's Itanium chips, which have an advanced RAS (reliability, availability and serviceability) characteristic to cut back statistics corruption and improve performance reliability. advanced RAS elements can correct error that may occur when data is being crunched on a processor.
"The Integrity platform has extra robust RAS ... than x86 processor servers," referred to Randy Meyer, director of NonStop product administration at HP. HP additionally bundles its own application to be certain better gadget reliability, Meyer observed.
The servers enable fast response times in functions involving price methods, securities buying and selling or digital affected person records, HP said. they're also priced low satisfactory that they may still enchantment to SMBs, Meyer pointed out.
as an instance, the server might attraction to a relatively small clinic that needs the equal high-availability server as a a good deal better health center however inside a smaller finances, Meyer said.
HP pitches NonStop as an alternative to IBM's mainframe server offerings, however its adoption has been relatively slow, mentioned Charles King, predominant analyst with Pund-IT.
The Integrity NS2000 line is an effort to take NonStop servers downstream to medium-sized enterprise where it could find new valued clientele. The decrease expenses could even be an effort on HP's half to keep customers and stop them from relocating to different mainframe platforms.
The server improves on the performance of previous NonStop entry-category techniques, HP said. The NS2000 server family unit doubles the efficiency of NonStop Integrity NS1000 servers, which also come with Itanium chips.
The NS2000 servers help between two to 4 dual-core Itanium 9100-collection processors, with each chip supporting between 8GB to 16GB of reminiscence. For US$a hundred twenty five,000, an NS2000 includes two 1.4 GHz Intel Itanium 9100-collection processors with 12MB of L3 cache, six 72GB 15K rpm SAS (serial-connected SCSI) disks and eight Ethernet ports.
The server family is now available worldwide via distribution channels.To comment on this article and other PCWorld content material, consult with our facebook web page or our Twitter feed.
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AI applications in healthcare can create $150 billion dollars in annual savings for the US healthcare economy by 2026
April 10, 2019 5 min read
Opinions expressed by Entrepreneur contributors are their own.
You're reading Entrepreneur India, an international franchise of Entrepreneur Media.
The milestones achieved by Artificial Intelligence have the world on its toes. Apart from all the industries, it has been touching, the modern healthcare industry has been receiving paramount importance. There has been an exemplary shift in the way patients are diagnosed by doctors because they now have a good amount of actionable data that can be put to good use.
AI is the game changer in the healthcare industry. As per the reports by Frost & Sullivan, the consulting firm, the healthcare AI market is likely to experience a compound annual growth rate of 40per cent by 2021, and it can change healthcare outcomes by 30-40per cent and cut treatment costs in half.
According to an analysis conducted by Accenture, AI applications in healthcare can create $150 billion dollars in annual savings for the US healthcare economy by 2026.
How AI is Changing Healthcare
The decisions made by medical practitioners can now be augmented by the extra layer of AI over the data. Training the code using this data reduces the likelihood of errors in the field of healthcare.
1. Electronic Health Records
Electronic Health Records are basically digital patient chart including information from multiple hospital encounters contained in an account which can be accessed across hospitals and facilities. EHR has all information including the disease, type of medicine and tests prescribed, results of the tests etc. Most of this data is fed manually at some point in time. But we can change it using AI by doing data extraction from free text (documents) using AI. We can also capture the clinical data by natural language processing.
2. Medical Imaging Diagnostics
AI plays a major role in enabling intelligence in the radiology images obtained through scanning machines. X-rays, CT scans and MRIs tells us about the body’s inner workings. The diagnostic imaging team, the pathologist and the doctors can reach a unanimous decision on the mode of treatment, and the chances of overcoming hurdles are very high. By making use of deep learning algorithms, it is now possible to distinguish between cancerous and non-cancerous cells in a much more precise way.
The radiologists and surgeons can now zoom into the problem, and study accurately, and do something more than what the human eyes could do.
3. Virtual Health Assistance
It is likely to increase patient engagement to the next level through Intelligent Virtual Assistant (IVA) and Medical Virtual Assistant (MVA). Today medical support has gone beyond wearables by advising patients to not just handle their goals, but also to actually assist them look after their health like a real assistant would such as medication reminders, provide medical advice for common ailments or complaints, suggest diet and eating habits, reminder for medication refill, remind doctor appointments and manage bookings, allow virtual interaction with doctors, chatbots to provide primary health and many such.
4. Robotic Assistance
The AI assistant can immediately deliver information on the patient’s past and present health and make recommendations that would help in the diagnosis. Surgeries have become minimally invasive techniques whereby hospital stay is considerably reduced, and thereby recovery of the patient. There are surgical bots that make use of computer vision to do surgeries after calculating the measurements of the human body precisely.
And the best part of all, the AI-assisted equipment monitor the patients and their health levels, after the doctors, nurses and caretakers have gone to rest or sleep. Human limitations will never be a problem in generating a commendable patient outcome.
5. Proactive Medical Care
In conventional medical treatment, the drift was to treat the patient after the disease is identified. Now with AI, reactive medical care became proactive medical care. In this kind of care, the patient’s comprehensive medical history is studied and high-risk markers for several diseases are emphasized. At risk, patients are then monitored for any variation in their conditions, and if anything seems alarming, the app can suggest medical intervention.
Benefits of Incorporating AI in Healthcare
Predictive Medical Care- A developing treatment model wherein the patient data is reviewed constantly to check for any anomalies, trailed by suggestions of medical intervention.
Personalized Medication - AI makes it likely for patients to have custom-made care based on their body composition and past medical history.
Better Diagnosis - Fast research and cross-referencing of data lead to improved diagnosis of diseases. The data also comprises handwritten notes, geospatial and sensor data and test results. Environment (both human and natural) influences are also considered.
Advanced treatment plans - New treatment means are generated and familiarized, including robotic surgery, cell biology, eye drops to dissolve cataracts instead of eye surgeries, wound healing by printing skin cells, 3D printing, artificial pancreas to balance blood glucose levels and administer insulin, and many such.
Non-stop monitoring - Uninterrupted monitoring of patients would make sure of timely care and treatment and even reduced hospital stay for the patients. The AI based app can check for the patient’s health and important signs in case of critically sick patients before notifying for medical intervention.
Economical for both patient and medical care provider - AI can make healthcare both effective and inexpensive as it helps in guiding treatment choice, making a precise diagnosis, helps the patients in taking better decisions concerning their health and makes important decisions in drug development.
For any fashion designer, inspiration comes from life experience. Although, few probably get it from personal life-or-death moments. But, that’s precisely where Steve Tidball and Nick Tidball got their ideas to create men’s outerwear Vollebak.
The adventure athletes have been running the men’s outdoor clothing brand for three years, using extreme sports and the runner’s high to spark their ideas for clothing that changes behavior and enhances our innate human abilities. They’ve had series of epiphanies on adventures like a training run in Tarfia, Spain where one brother nearly drowned after getting snatched by a rogue wave, and during a 78-mile ultramarathon through the Namibian desert.
From hallucinations to near-exhaustion, both brothers had experienced what they call a shared hallucination as they embarked on this extreme journey and came out with many light bulb moments the led to the idea for clothes that help you survive/change cognitive function. That’s why every new piece of outerwear aims to influence what they’re capable of.
Through these intense moments, they were inspired to create unique apparel such as pants that last 100 years, the first award-winning solar-charged jacket, and a hoodie that was intentionally designed to help extreme athletes calm down either before or after intense, high-stress or dangerous experiences. And they just launched a Black Squid Jacket, which has the adaptive camouflage capabilities of a squid.
I got the chance to chat with co-founders about their extreme adventures and how they’re turning those journeys into a clothing brand.
Q: You two have been all over the world and has some intense experiences. What are some of the craziest moments?
Steve: Over the last 10 years we’ve competed in some of the most extreme ultramarathons and adventure races in the world. We’ve raced 78 miles in 24 hours across the Namib desert in Namibia, competed in a 7-day ultramarathon through the Amazon jungle, and a 120-mile 48-hour race over the Alps that crosses through France, Italy, and Switzerland, with the same amount of climbing as going up and down Everest twice.
In the Namib desert I was so hot my nervous system went haywire and decided I must be really cold. So while running through a desert that was over 130 degrees, I was shaking with cold, and my teeth were chattering uncontrollably. After being pulled out of the race for 6 hours, I was allowed back in at night to race the last two marathons – a decision that seemed bizarrely logical at the time.
Nick: That night while running together the two of us experienced a shared and simultaneous hallucination. It was around 2 am, and we were so exhausted we both starting tripping. While running, our faces both started morphing into the faces of everyone we knew. These races sound like they’re about fitness, but they’re actually quite creative experiences, as you often find yourself in a world where things make a lot less sense.
Q: What did you learn from these experiences?
Steve: In the process we’ve learnt that putting ourselves under extreme physical and psychological pressure in some of the most inhospitable places in the world is a far greater test of your survival instinct than it is of your speed or skill, and it’s opened us up to crazy experiences that would have been pretty hard to access in any other way. They range from the life-affirming – learning mid-race to navigate by starlight while on the water – to rather deadly.
Nick: People talk about the physical challenge of these kinds of sports, but it’s really a mental challenge as racing ultra-long distances through the day and night leads to a profoundly altered state of consciousness. You move through extreme highs and depressions, where hallucinations are commonplace. You can move from despair to elation in a second. There can be hours where literally you have no thoughts in your head and other times where there is a non-stop monologue of you mind telling every single compelling reason why you should quit. So, the normal relationship between your mind and body that exists and feels quite logical just fundamentally breaks down.Vollebak
Q: When did you get the inspiration to start an athletic wear line?
Steve: Taking part in some of the toughest adventure races in the world made us realize that the sports gear we were wearing wasn’t as advanced, progressive or as smart as you were made to believe. And the kernel of that idea definitely started in the African desert.
Nick: Because we were also designers we knew that while in every other industry there was a player clearly building the future – Tesla in cars, SpaceX in space travel, Apple in tech, in the outdoor industry it wasn’t being done at all. So we started the company three years ago and ever since then have been using science and technology to create the most advanced sports gear ever made.
Steve: As we were traveling around the world, racing and recovering – while holding down full-time jobs – we started wondering what was stopping clothing from becoming more adaptable and intelligent. While other fields of technology and material science were advancing at lightning speed, clothing seemed to be stuck.
Nick: At the same time, having grown up together, worked together for 18 years and now raced around the world together, we wanted to build a company together. Companies are such complex things to get right, running it with someone you know that you trust with your life is a good thing. We looked at other creative business partnerships like Phil Knight and Bill Bowerman creating Nike, Steve Jobs and Steve Wozniak building Apple, and Walt Disney and his brother Roy, and thought that building it as twin brothers would be fun.
Q: So, which scenarios inspired specific pieces in your clothing line?
Nick: We use running as a way of enabling the free flow of ideas – we find that the flow state created by being out in nature every day lets you make connections between ideas that might not otherwise seem related. Our 50,000BC Jacket, for example, came from running in the hills in winter thinking wouldn’t it be amazing to have your own cave to crawl into wherever you are in the world. So what started out as a cold run ended up as us designing a jacket that would have worked for the prehistoric man.
Steve: After racing in the Amazon we also started to think a lot about how throwaway clothing is. At the end of the race, most athletes chucked their clothes in a fire because it was so destroyed. So, for the last two years, we’ve been working on a range of clothing which includes a 100 Year Hoodie, 100 Year Pants and 100 Year Shorts. The idea is very simple – it’s clothing that’s designed to last 100 years and can survive everything nature can throw at it, including fire.
Nick: And a lot of our adventures go through day and night, and when you’re running on a knife-edge ridge through the mountains or getting tracked by jackals in the desert at night, there is no such thing as being too well-lit. So one of the big things we've looked at is how your clothing can help you stay visible. We quickly turned to nature for answers and started building clothing that could mimic the astonishing color-shifting survival mechanisms found in the animal kingdom.
Steve: The surface of our Solar Charged Jacket can be charged by the sun in the day and then glows like a firefly in the dark, while our Black Squid Jacket recreates one of nature’s most brilliant solutions to high visibility – the adaptive camouflage of the squid. While the squid uses microscopic plate-like structures on the surface of its skin to change color, the Black Squid Jacket uses over 2 billion disruptively-structured microscopic glass spheres to reflect every color in the visible spectrum.Vollebak
Q: There are so many athletic wear and outdoor apparel brands. How is yours different?
Steve: We use cutting edge science and technology to build the future of clothing. Our mission is to create sports gear that no-one else can or will.
Nick: Our Graphene Jacket is the perfect example. We put it out into the world as an experimental prototype and are using the collective testing power of our early adopters to improve and iterate. We’re looking to harness the collective power of our early adopters as a test group to do it. Together they’re some of the smartest and most hardcore guys in the world. So, they’re likely to discover things that we simply don’t know. It brings massive scale to our ability to experiment with materials to see how they behave.
Steve: We’re working with the most advanced materials and concepts in the world, from building the world’s first Graphene Jacket to attempting to reverse engineer the craziest biological materials. We believe that over the next 10 to 20 years, wearable tech will become increasingly invisible – instead of wearing it over your eyes or on your wrist, it will become embedded, as clothing and tech simply merge. At the same time, we’re not afraid to use our customer base as an R&D lab, and they enjoy doing it. So we try and put our tech into the world as early as possible rather than too late.
Eighteen right-handed volunteers (three females) were considered in the study, mean age ± standard deviation (31.9 ± 5.5). All participants were right-handed to avoid any variation in functional response due to lateralisation of brain function. No participants reported a prior history of neurological or psychiatric disorder, a current unstable medical condition, or under medication at the time of testing. For study participation, written informed consent was obtained from all participants prior to initiation of the experiments. Throughout the experiments, the participants were instructed to keep their eyes closed29. Procedures and methods for this study followed the guidelines accepted by the Declaration of Helsinki. This research study was approved by full-board review process of the Taipei Medical University’s Joint Institutional Review Board under Contract Number 201307010.Pre-processing
Raw fNIRS data is generally contaminated by different sources of noise and pre-processing is required. Electrical noise in fNIRS data and cardiac pulsation (~1 Hz) was canceled by a finite impulse forth-order low-pass filter with a cut-off frequency of 0.16 Hz; this cut-off frequency helpped maintain the very-low frequency oscillations (VLFO)(0.01–0.08 Hz) and low frequency oscilation (VLO)(0.08–0.15 Hz) bands30. Then, motion artifacts were removed following a wavelet de-noising procedure31. In order to reduce extracerebral haemodynamics (scalp) and systemic variables27 (e.g. blood pressure, autonomic nervous system acitivty) existent in our data, we used principal component analysis (PCA) to identify and delete those components representing spurious signals28.Feature extraction
A feature can be defined as a representation of data, which can be binary, categorical or continuous16. Features can be defined in many ways and, in general, features are a simplification of the most representative pattern in the raw data. Feature extraction (or feature engineering) is the process of identifying the pertinent signal characteristics (attributes) from extraneous content and representing them in a compact and/or meaninful form, amenable to interpretation by a human or computer19. Feature extraction occurs after signals have been acquired and cleaned from noise, and it gathers the attributes that are used to solve the learning problem.
The number of studies in classification of experimental pain is small and direct comparisons with these studies is limited21. In addition, these studies used a small feature space to characterize the sample data and build a binary prediction (pain or no pain) with different success. Therefore, in this research, other similar applications in BCI and biomedical time-series analysis are used to construct a better representation from the fNIRS data. Most features in neural applications use spatial, temporal, and spectral analysis of brain signals of multiple channels and in different time points19. This set of simultaneously computed features is described as feature vector.
In this particular study, an important characteristic of the stimulation experiment is the stimulation intensity and the stimulus periodicity. Thus, the expectation is that features that could reflect these types of characteristics may produce better results. Identifying these attributes from the fNIRS data would be desirable to make the classification task simple and approaching high classification accuracy32. Each extracted feature was computed for each of the 24 channels, and with each channel containing its own vector. The idea behind this methodology is obtaining a multidimensional analysis which broadens the scope of pain to achieve the best representation of human pain. The feature extraction techniques are organized in three groups: time domain, frequency domain, and wavelet domain.Time domain
The haemodynamic response after each stimulus produces a positive response of HbO concentration. This response is referred to as the activation curve which generally is initiated by a small dip, followed by an increase of HbO to reach its maximum (peak) response, and then return to baseline. This response can be used to characterise different haemodynamic response (i.e., cold, heat). The extracted features in time domain (a total of 9 features) from the HbO activation curve are: mean, variance, skewness, kurtosis, peak amplitude, slope, area under the curve (AUC), time to peak, and root mean squared (RMS).Frequency domain
Fourier analysis provided a mapping of the HbO signal to the frequency domain. This projection exposes the spectral content of the signal in terms of the sum of its projections onto a set of sine or cosine functions. We used the power spectrum density of the HbO signal to divide the original signal into frequency bands. In specific, we were interested in two bands: very-low frequency oscillations (VLFO, 0.01–0.08 Hz) and low frequency oscillations (LFO, 0.08–0.15 Hz)30; these bands correspond to the period of the stimulation task and the main energy distribution is expected in these frequencies. The extracted features (a total of 23 features) in the frequency domain are: Fourier coefficients from the VLFO (8 features) and LFO (7 features) bands, energy spectrum’s mean in each band (2 features), the variance of the energy spectrum in each band (2 features), maximum energy value in each band (2 features), and the frequency of the maximum energy value in each band (2 features).Wavelet domain
The wavelet representation was done by the continuous wavelet transform (CWT). Wavelet analysis provides a detailed description of the power spectrum of the signal in terms of both time and frequency domain8. The levels of decomposition are selected according to the VLFO and LFO bands. The features extracted (a total of 37 features) in the wavelet domain are: wavelet coefficients from the VLFO (21 features) and LFO (9) bands, mean of the absolute values of the coefficients in each band (2 features), the variance of the wavelet coefficients in each band (2 features), the wavelet power spectrum from each frequency band (2 features), and the absolute mean ratio between the mean values of the VLFO and LFO band (1 feature). A summary of all defined features is presented in Table 4.Table 4 Summary of defined features (69) from each HbO signal in time, frequency and time-frequency (wavelet) domains.
A global representation of the different domains used to generate each feature is presented in Fig. 5. This image combines the tree domains, the raw HbO signal in the time domain is presented in the top panel, the power spectrum describing the distribution of power into frequency components composing the HbO signal is presented in the bottom left panel, and in the bottom right panel the HbO signal is exhibited in a time-frequency representation, time in the x axis, frequency in the y axis.Feature selection
Feature selection can be defined as a process to select a subset of features from the original set of features. Thus, the feature space can be optimally reduced according to a certain evaluation criteria33. Common objective criteria are: prediction accuracy, data size, and minimal use of input features to reduce associated costs34. The main goal of feature selection is to increase the classification performance by building a more compact feature subset, this is achieved by finding and eliminating those redundant or irrelevant features that have little influence to accurately describe the data.
The feature selection process was used to obtain the most relevant features according to three criteria. These criteria are: chi-squared statistics (χ2), information gain (IG), and joint mutual information (JMI). The reasoning behind using only ranking methods in contrast to other methods (e.g. wrappers) is that ranking methods evaluate the features independently of any classification model, are computationally simple and fast, and are computed only once16; these attributes are desired for clinical applications. In general, using feature selection will make the computational model less complex by using the most relevant features and potentially discharging irrelevant features; it will also enable a faster training process of the machine learning algorithm, thus it reduces the computational cost.Classification
The classification stage aimed to compare the effectiveness of the three ranking criteria in improving the accuracy of our classification models. The classification problem was to predict the class label (e.g., 1 = Low-Heat, or 4 = High -Heat) of unknown data points into one of the four categories. For that purpose, the data was randomly split on the subject level in training (13 subjects) and validation (5 subjects). One subset was used to train the classifier and the remaining subset was used to validate the performance of the classifier. The training process was carried out using leave-one-out cross validation (LOOCV) on the subject level. Data from one subject was held as the testing set and the remaining data (12 subjects) was used for training; this process was repeated 12 times, testing on a different subject in each iteration. The trained models were then validated using the remaining five subjects and the evaluations of predictive performance (classification accuracy) are then averaged across the five subjects and reported as the final scores. Classification accuracy was calculated by determining the number of correctly predicted labels divided by the total number of testing samples from each subject (i.e., correct/total).
Three well-established algorithms were compared, the linear discriminant algorithm (LDA), the k-nearest neighbour (K-NN), and support vector machines (SVM) for multi-class classification (one-vs-one voting). The LDA classifier is ideal for real-time applications due to its low computational cost and good results; this method was used as ground truth for the classification task. The K-NN model was parametrised using Euclidean distance as metric of similarity and the best K - neighbours were obtained by searching K from 1 to 20; the top five nearest neighbours were K = 1,3,5,7,9. Similarly, for the SVM models a grid search of parameter C (at 0.1, 1, 10, and 100) for both kernels and Gamma (at 0.001, 0.01, 0.1, and 1) for the Gaussian kernel was completed. Best obtained results, for both kernels parameter C = 1 and parameter Gamma = 0.01 for the Gaussian kernel.Experimental paradigm
Pain perceptions were investigated using the quantitative sensory testing (QST) protocol35. We defined pain threshold (low pain) as the lowest stimulus intensity at which stimulation becomes painful, and pain tolerance (high pain) as the highest intensity of pain at which stimulus becomes unbearable. The participants were exposed to gradually increasing or decreasing temperatures with a sensory analyzer (Pathway CHEPS, Medoc Ltd., Israel), which delivers heat and cold to the skin with a thermode; the thermode has a contact area of 9.0 cm2 and a baseline temperature of 32 °C. Pain measurements were obtained on the back of the left hand, the participants pressed a button when they experienced pain (threshold test) and highest intensity of pain (tolerance test). The temperature of the thermode, just as it became painful or unbearable was recorded as the thermal pain threshold or thermal pain tolerance, respectively. Research has showed no significant difference in QST between the right and left sides of the body35.
The stimulation protocol consisted of two tests: the thermal pain threshold (low pain) and the thermal pain tolerance (high pain), with a 2-minute rest between each test. Baseline data were measured at rest during the first 60 seconds of the experiments, after that, the stimulation was randomly applied between threshold and tolerance tests. Figure 6 presents an example of the stimulation paradigm. In this example, three consecutive measurements of cold and heat pain thresholds are obtained; 60-second rest between cold and heat detections was applied and a 30-second rest between stimuli was applied. Based on these measurements the fNIRS data were organized into four categories for classification: 1. Low-Cold (low pain), 2. Low-Heat (low pain), 3. High-Cold (high pain), and 4. High-Heat (high pain). These categories (1–4) were used to label the database and used as classes for the classification task.Figure 6
Stimulation paradigm. In this example, pain threshold test was first measured followed by pain tolerance test. In each test, cold and hot stimulus were applied on the back of the hand of each subject. Each stimulus was applied in a random order.fNIRS recording
Brain haemodynamics were acquired using a Hitachi ETG-4000 (Hitachi Medical Corporation, Japan) optical topography system. This system uses near-infrared (NIR) light to investigate cerebral hemodynamics. Two wavelengths of NIR light are used, oxy-hemoglobin (HbO) at 695 nm and deoxy-hemoglobin (HbR) at 830 nm. The equipment uses a 24-channel cap, configured in 12 channels per hemisphere. According to the EEG 10–20 system, the measuring probes were on the C3 and C4 positions29. The head probe has a predefined source-detector separation of 3 cm. Figure 7 presents the probe configuration, channels 1 to 12 sampled the right hemisphere, while channesl 13 to 24 sampled the left hemisphere. In this study, we used only the HbO signals because they exhibit a better signal-to-noise ratio than HbR signals36. The sampling frequency used in the experiments was was 10 Hz.Figure 7
Channel location and configuration. Channel probes were located around the C3 and C4 areas. Source-detector distance was 3 cm.
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