Electroencephalogram Signalling diagnosis using Softcomputing

The two most frightening things for the researchers in clinical signal processing and computer aided diagnosis are noise and relativity of human judgment. The researchers made effort to overcome these two challenges by using various soft computing approaches. In this article the present benefits of these approaches in the accomplishment of the analysis of electroencephalogram (EEG) is acknowledge. There is also the presentation of the significance of several trend and prospects of further softcomputing methods that can produce better results in signal processing of EEG. Medical experts apply the different softcomputing techniques for disease diagnoses and decision making systems performed on brain actions and modeling of neural impulses of the human encephalon

Review of Wearable Devices and Data Collection Considerations for Connected Health

Wearable sensor technology has gradually extended its usability into a wide range of well-known applications. Wearable sensors can typically assess and quantify the wearer’s physiology and are commonly employed for human activity detection and quantified self-assessment. Wearable sensors are increasingly utilised to monitor patient health, rapidly assist with disease diagnosis, and help predict and often improve patient outcomes. Clinicians use various self-report questionnaires and well-known tests to report patient symptoms and assess their functional ability. These assessments are time consuming and costly and depend on subjective patient recall. Moreover, measurements may not accurately demonstrate the patient’s functional ability whilst at home. Wearable sensors can be used to detect and quantify specific movements in different applications. The volume of data collected by wearable sensors during long-term assessment of ambulatory movement can become immense in tuple size. This paper discusses current techniques used to track and record various human body movements, as well as techniques used to measure activity and sleep from long-term data collected by wearable technology devices

Learning Biosignals with Deep Learning

The healthcare system, which is ubiquitously recognized as one of the most influential system in society, is facing new challenges since the start of the decade.The myriad of physiological data generated by individuals, namely in the healthcare system, is generating a burden on physicians, losing effectiveness on the collection of patient data. Information systems and, in particular, novel deep learning (DL) algorithms have been prompting a way to take this problem. This thesis has the aim to have an impact in biosignal research and industry by presenting DL solutions that could empower this field. For this purpose an extensive study of how to incorporate and implement Convolutional Neural Networks (CNN), Recursive Neural Networks (RNN) and Fully Connected Networks in biosignal studies is discussed. Different architecture configurations were explored for signal processing and decision making and were implemented in three different scenarios: (1) Biosignal learning and synthesis; (2) Electrocardiogram (ECG) biometric systems, and; (3) Electrocardiogram (ECG) anomaly detection systems. In (1) a RNN-based architecture was able to replicate autonomously three types of biosignals with a high degree of confidence. As for (2) three CNN-based architectures, and a RNN-based architecture (same used in (1)) were used for both biometric identification, reaching values above 90% for electrode-base datasets (Fantasia, ECG-ID and MIT-BIH) and 75% for off-person dataset (CYBHi), and biometric authentication, achieving Equal Error Rates (EER) of near 0% for Fantasia and MIT-BIH and bellow 4% for CYBHi. As for (3) the abstraction of healthy clean the ECG signal and detection of its deviation was made and tested in two different scenarios: presence of noise using autoencoder and fully-connected network (reaching 99% accuracy for binary classification and 71% for multi-class), and; arrhythmia events by including a RNN to the previous architecture (57% accuracy and 61% sensitivity). In sum, these systems are shown to be capable of producing novel results. The incorporation of several AI systems into one could provide to be the next generation of preventive medicine, as the machines have access to different physiological and anatomical states, it could produce more informed solutions for the issues that one may face in the future increasing the performance of autonomous preventing systems that could be used in every-day life in remote places where the access to medicine is limited. These systems will also help the study of the signal behaviour and how they are made in real life context as explainable AI could trigger this perception and link the inner states of a network with the biological traits.O sistema de saúde, que é ubiquamente reconhecido como um dos sistemas mais influentes da sociedade, enfrenta novos desafios desde o ínicio da década. A miríade de dados fisiológicos gerados por indíviduos, nomeadamente no sistema de saúde, está a gerar um fardo para os médicos, perdendo a eficiência no conjunto dos dados do paciente. Os sistemas de informação e, mais espcificamente, da inovação de algoritmos de aprendizagem profunda (DL) têm sido usados na procura de uma solução para este problema. Esta tese tem o objetivo de ter um impacto na pesquisa e na indústria de biosinais, apresentando soluções de DL que poderiam melhorar esta área de investigação. Para esse fim, é discutido um extenso estudo de como incorporar e implementar redes neurais convolucionais (CNN), redes neurais recursivas (RNN) e redes totalmente conectadas para o estudo de biosinais. Diferentes arquiteturas foram exploradas para processamento e tomada de decisão de sinais e foram implementadas em três cenários diferentes: (1) Aprendizagem e síntese de biosinais; (2) sistemas biométricos com o uso de eletrocardiograma (ECG), e; (3) Sistema de detecção de anomalias no ECG. Em (1) uma arquitetura baseada na RNN foi capaz de replicar autonomamente três tipos de sinais biológicos com um alto grau de confiança. Quanto a (2) três arquiteturas baseadas em CNN e uma arquitetura baseada em RNN (a mesma usada em (1)) foram usadas para ambas as identificações, atingindo valores acima de 90 % para conjuntos de dados à base de eletrodos (Fantasia, ECG-ID e MIT -BIH) e 75 % para o conjunto de dados fora da pessoa (CYBHi) e autenticação, atingindo taxas de erro iguais (EER) de quase 0 % para Fantasia e MIT-BIH e abaixo de 4 % para CYBHi. Quanto a (3) a abstração de sinais limpos e assimptomáticos de ECG e a detecção do seu desvio foram feitas e testadas em dois cenários diferentes: na presença de ruído usando um autocodificador e uma rede totalmente conectada (atingindo 99 % de precisão na classificação binária e 71 % na multi-classe), e; eventos de arritmia incluindo um RNN na arquitetura anterior (57 % de precisão e 61 % de sensibilidade). Em suma, esses sistemas são mais uma vez demonstrados como capazes de produzir resultados inovadores. A incorporação de vários sistemas de inteligência artificial em um unico sistema pederá desencadear a próxima geração de medicina preventiva. Os algoritmos ao terem acesso a diferentes estados fisiológicos e anatómicos, podem produzir soluções mais informadas para os problemas que se possam enfrentar no futuro, aumentando o desempenho de sistemas autónomos de prevenção que poderiam ser usados na vida quotidiana, nomeadamente em locais remotos onde o acesso à medicinas é limitado. Estes sistemas também ajudarão o estudo do comportamento do sinal e como eles são feitos no contexto da vida real, pois a IA explicável pode desencadear essa percepção e vincular os estados internos de uma rede às características biológicas

Guidage non-intrusif d'un bras robotique à l'aide d'un bracelet myoélectrique à électrode sèche

Depuis plusieurs années la robotique est vue comme une solution clef pour améliorer la qualité de vie des personnes ayant subi une amputation. Pour créer de nouvelles prothèses intelligentes qui peuvent être facilement intégrées à la vie quotidienne et acceptée par ces personnes, celles-ci doivent être non-intrusives, fiables et peu coûteuses. L’électromyographie de surface fournit une interface intuitive et non intrusive basée sur l’activité musculaire de l’utilisateur permettant d’interagir avec des robots. Cependant, malgré des recherches approfondies dans le domaine de la classification des signaux sEMG, les classificateurs actuels manquent toujours de fiabilité, car ils ne sont pas robustes face au bruit à court terme (par exemple, petit déplacement des électrodes, fatigue musculaire) ou à long terme (par exemple, changement de la masse musculaire et des tissus adipeux) et requiert donc de recalibrer le classifieur de façon périodique. L’objectif de mon projet de recherche est de proposer une interface myoélectrique humain-robot basé sur des algorithmes d’apprentissage par transfert et d’adaptation de domaine afin d’augmenter la fiabilité du système à long-terme, tout en minimisant l’intrusivité (au niveau du temps de préparation) de ce genre de système. L’aspect non intrusif est obtenu en utilisant un bracelet à électrode sèche possédant dix canaux. Ce bracelet (3DC Armband) est de notre (Docteur Gabriel Gagnon-Turcotte, mes co-directeurs et moi-même) conception et a été réalisé durant mon doctorat. À l’heure d’écrire ces lignes, le 3DC Armband est le bracelet sans fil pour l’enregistrement de signaux sEMG le plus performant disponible. Contrairement aux dispositifs utilisant des électrodes à base de gel qui nécessitent un rasage de l’avant-bras, un nettoyage de la zone de placement et l’application d’un gel conducteur avant l’utilisation, le brassard du 3DC peut simplement être placé sur l’avant-bras sans aucune préparation. Cependant, cette facilité d’utilisation entraîne une diminution de la qualité de l’information du signal. Cette diminution provient du fait que les électrodes sèches obtiennent un signal plus bruité que celle à base de gel. En outre, des méthodes invasives peuvent réduire les déplacements d’électrodes lors de l’utilisation, contrairement au brassard. Pour remédier à cette dégradation de l’information, le projet de recherche s’appuiera sur l’apprentissage profond, et plus précisément sur les réseaux convolutionels. Le projet de recherche a été divisé en trois phases. La première porte sur la conception d’un classifieur permettant la reconnaissance de gestes de la main en temps réel. La deuxième porte sur l’implémentation d’un algorithme d’apprentissage par transfert afin de pouvoir profiter des données provenant d’autres personnes, permettant ainsi d’améliorer la classification des mouvements de la main pour un nouvel individu tout en diminuant le temps de préparation nécessaire pour utiliser le système. La troisième phase consiste en l’élaboration et l’implémentation des algorithmes d’adaptation de domaine et d’apprentissage faiblement supervisé afin de créer un classifieur qui soit robuste au changement à long terme.For several years, robotics has been seen as a key solution to improve the quality of life of people living with upper-limb disabilities. To create new, smart prostheses that can easily be integrated into everyday life, they must be non-intrusive, reliable and inexpensive. Surface electromyography provides an intuitive interface based on a user’s muscle activity to interact with robots. However, despite extensive research in the field of sEMG signal classification, current classifiers still lack reliability due to their lack of robustness to short-term (e.g. small electrode displacement, muscle fatigue) or long-term (e.g. change in muscle mass and adipose tissue) noise. In practice, this mean that to be useful, classifier needs to be periodically re-calibrated, a time consuming process. The goal of my research project is to proposes a human-robot myoelectric interface based on transfer learning and domain adaptation algorithms to increase the reliability of the system in the long term, while at the same time reducing the intrusiveness (in terms of hardware and preparation time) of this kind of systems. The non-intrusive aspect is achieved from a dry-electrode armband featuring ten channels. This armband, named the 3DC Armband is from our (Dr. Gabriel Gagnon-Turcotte, my co-directors and myself) conception and was realized during my doctorate. At the time of writing, the 3DC Armband offers the best performance for currently available dry-electrodes, surface electromyographic armbands. Unlike gel-based electrodes which require intrusive skin preparation (i.e. shaving, cleaning the skin and applying conductive gel), the 3DC Armband can simply be placed on the forearm without any preparation. However, this ease of use results in a decrease in the quality of information. This decrease is due to the fact that the signal recorded by dry electrodes is inherently noisier than gel-based ones. In addition, other systems use invasive methods (intramuscular electromyography) to capture a cleaner signal and reduce the source of noises (e.g. electrode shift). To remedy this degradation of information resulting from the non-intrusiveness of the armband, this research project will rely on deep learning, and more specifically on convolutional networks. The research project was divided into three phases. The first is the design of a classifier allowing the recognition of hand gestures in real-time. The second is the implementation of a transfer learning algorithm to take advantage of the data recorded across multiple users, thereby improving the system’s accuracy, while decreasing the time required to use the system. The third phase is the development and implementation of a domain adaptation and self-supervised learning to enhance the classifier’s robustness to long-term changes

Timing and Time Perception: Procedures, Measures, and Applications

Timing and Time Perception: Procedures, Measures, and Applications is a one-of-a-kind, collective effort to present the most utilized and known methods on timing and time perception. Specifically, it covers methods and analysis on circadian timing, synchrony perception, reaction/response time, time estimation, and alternative methods for clinical/developmental research. The book includes experimental protocols, programming code, and sample results and the content ranges from very introductory to more advanced so as to cover the needs of both junior and senior researchers. We hope that this will be the first step in future efforts to document experimental methods and analysis both in a theoretical and in a practical manner