96 research outputs found

    Automatic Diagnosis of Schizophrenia and Attention Deficit Hyperactivity Disorder in rs-fMRI Modality using Convolutional Autoencoder Model and Interval Type-2 Fuzzy Regression

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    Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy

    Automated Deep Neural Network Approach for Detection of Epileptic Seizures

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    In this thesis, I focus on exploiting electroencephalography (EEG) signals for early seizure diagnosis in patients. This process is based on a powerful deep learning algorithm for times series data called Long Short-Term Memory (LSTM) network. Since manual and visual inspection (detection) of epileptic seizure through the electroencephalography (EEG) signal by expert neurologists is time-consuming, work-intensive and error-prone and it might take a couple hours for experts to analyze a single patient record and to do recognition when immediate action is needed to be taken. This thesis proposes a reliable automatic seizure/non-seizure classification method that could facilitate the identification process of characteristic epileptic patterns, such as pre-ictal spikes, seizures and determination of seizure frequency, seizure type, etc. In order to recognize epileptic seizure accurately, the proposed model exploits the temporal dependencies in the EEG data. Experiments on clinical data present that this method achieves a high seizure prediction accuracy and maintains reliable performance. This thesis also finds the most efficient lengths of EEG recording for highest accuracies of different classification in the automated seizure detection realm. It could help non-experts to predict the seizure more comprehensively and bring awareness to patients and caregivers of upcoming seizures, enhancing the daily lives of patients against unpredictable occurrence of seizures.Master of Science in Applied Computer Scienc

    Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings

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    L’épilepsie est une des maladies neurologiques les plus frĂ©quentes, touchant prĂšs d’un pourcent de la population mondiale. De nos jours, bien qu’environ deux tiers des patients Ă©pileptiques rĂ©pondent adĂ©quatement aux traitements pharmacologiques, il reste qu’un tiers des patients doivent vivre avec des crises invalidantes et imprĂ©visibles. Quoique la chirurgie d’épilepsie puisse ĂȘtre une autre option thĂ©rapeutique envisageable, le recours Ă  la chirurgie de rĂ©section demeure trĂšs faible en partie pour des raisons diverses (taux de rĂ©ussite modeste, peur des complications, perceptions nĂ©gatives). D’autres avenues de traitement sont donc souhaitables. Une piste actuellement explorĂ©e par des groupes de chercheurs est de tenter de prĂ©dire les crises Ă  partir d’enregistrements de l’activitĂ© cĂ©rĂ©brale des patients. La capacitĂ© de prĂ©dire la survenue de crises permettrait notamment aux patients, aidants naturels ou personnels mĂ©dical de prendre des mesures de prĂ©caution pour Ă©viter les dĂ©sagrĂ©ments reliĂ©s aux crises voire mĂȘme instaurer un traitement pour les faire avorter. Au cours des derniĂšres annĂ©es, d’importants efforts ont Ă©tĂ© dĂ©ployĂ©s pour dĂ©velopper des algorithmes de prĂ©diction de crises et d’en amĂ©liorer les performances. Toutefois, le manque d’enregistrements Ă©lectroencĂ©phalographiques intracrĂąniens (iEEG) de longue durĂ©e de qualitĂ©, la quantitĂ© limitĂ©e de crises, ainsi que la courte durĂ©e des pĂ©riodes interictales constituaient des obstacles majeurs Ă  une Ă©valuation adĂ©quate de la performance des algorithmes de prĂ©diction de crises. RĂ©cemment, la disponibilitĂ© en ligne d’enregistrements iEEG continus avec Ă©chantillonnage bilatĂ©ral (des deux hĂ©misphĂšres) acquis chez des chiens atteints d’épilepsie focale Ă  l’aide du dispositif de surveillance ambulatoire implantable NeuroVista a partiellement facilitĂ© cette tĂąche. Cependant, une des limitations associĂ©es Ă  l’utilisation de ces donnĂ©es durant la conception d’un algorithme de prĂ©diction de crises Ă©tait l’absence d’information concernant la zone exacte de dĂ©but des crises (information non fournie par les gestionnaires de cette base de donnĂ©es en ligne). Le premier objectif de cette thĂšse Ă©tait la mise en oeuvre d’un algorithme prĂ©cis de prĂ©diction de crises basĂ© sur des enregistrements iEEG canins de longue durĂ©e. Les principales contributions Ă  cet Ă©gard incluent une localisation quantitative de la zone d’apparition des crises (basĂ©e sur la fonction de transfert dirigĂ© –DTF), l’utilisation d’une nouvelle fonction de coĂ»t via l’algorithme gĂ©nĂ©tique proposĂ©, ainsi qu’une Ă©valuation quasi-prospective des performances de prĂ©diction (donnĂ©es de test d’un total de 893 jours). Les rĂ©sultats ont montrĂ© une amĂ©lioration des performances de prĂ©diction par rapport aux Ă©tudes antĂ©rieures, atteignant une sensibilitĂ© moyenne de 84.82 % et un temps en avertissement de 10 %. La DTF, utilisĂ©e prĂ©cĂ©demment comme mesure de connectivitĂ© pour dĂ©terminer le rĂ©seau Ă©pileptique (objectif 1), a Ă©tĂ© prĂ©alablement validĂ©e pour quantifier les relations causales entre les canaux lorsque les exigences de quasi-stationnaritĂ© sont satisfaites. Ceci est possible dans le cas des enregistrements canins en raison du nombre relativement faible de canaux. Pour faire face aux exigences de non-stationnaritĂ©, la fonction de transfert adaptatif pondĂ©rĂ©e par le spectre (Spectrum weighted adaptive directed transfer function - swADTF) a Ă©tĂ© introduit en tant qu’une version variant dans le temps de la DTF. Le second objectif de cette thĂšse Ă©tait de valider la possibilitĂ© d’identifier les endroits Ă©metteurs (ou sources) et rĂ©cepteurs d’activitĂ© Ă©pileptiques en appliquant la swADTF sur des enregistrements iEEG de haute densitĂ© provenant de patients admis pour Ă©valuation prĂ©-chirurgicale au CHUM. Les gĂ©nĂ©rateurs d’activitĂ© Ă©pileptique Ă©taient dans le volume rĂ©sĂ©quĂ© pour les patients ayant des bons rĂ©sultats post-chirurgicaux alors que diffĂ©rents foyers ont Ă©tĂ© identifiĂ©s chez les patients ayant eu de mauvais rĂ©sultats postchirurgicaux. Ces rĂ©sultats dĂ©montrent la possibilitĂ© d’une identification prĂ©cise des sources et rĂ©cepteurs d’activitĂ©s Ă©pileptiques au moyen de la swADTF ouvrant la porte Ă  la possibilitĂ© d’une meilleure sĂ©lection d’électrodes de maniĂšre quantitative dans un contexte de dĂ©veloppement d’algorithme de prĂ©diction de crises chez l’humain. Dans le but d’explorer de nouvelles avenues pour la prĂ©diction de crises Ă©pileptiques, un nouveau prĂ©curseur a aussi Ă©tĂ© Ă©tudiĂ© combinant l’analyse des spectres d’ordre supĂ©rieur et les rĂ©seaux de neurones artificiels (objectif 3). Les rĂ©sultats ont montrĂ© des diffĂ©rences statistiquement significatives (p<0.05) entre l’état prĂ©ictal et l’état interictal en utilisant chacune des caractĂ©ristiques extraites du bi-spectre. UtilisĂ©es comme entrĂ©es Ă  un perceptron multicouche, l’entropie bispectrale normalisĂ©e, l’entropie carrĂ© normalisĂ©e, et la moyenne ont atteint des prĂ©cisions respectives de 78.11 %, 72.64% et 73.26%. Les rĂ©sultats de cette thĂšse confirment la faisabilitĂ© de prĂ©diction de crises Ă  partir d’enregistrements d’électroencĂ©phalographie intracrĂąniens. Cependant, des efforts supplĂ©mentaires en termes de sĂ©lection d’électrodes, d’extraction de caractĂ©ristiques, d’utilisation des techniques d’apprentissage profond et d’implĂ©mentation Hardware, sont nĂ©cessaires avant l’intĂ©gration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT Epilepsy is a chronic condition characterized by recurrent “unpredictable” seizures. While the first line of treatment consists of long-term drug therapy about one-third of patients are said to be pharmacoresistant. In addition, recourse to epilepsy surgery remains low in part due to persisting negative attitudes towards resective surgery, fear of complications and only moderate success rates. An important direction of research is to investigate the possibility of predicting seizures which, if achieved, can lead to novel interventional avenues. The paucity of intracranial electroencephalography (iEEG) recordings, the limited number of ictal events, and the short duration of interictal periods have been important obstacles for an adequate assessment of seizure forecasting. More recently, long-term continuous bilateral iEEG recordings acquired from dogs with naturally occurring focal epilepsy, using the implantable NeuroVista ambulatory monitoring device have been made available on line for the benefit of researchers. Still, an important limitation of these recordings for seizure-prediction studies was that the seizure onset zone was not disclosed/available. The first objective of this thesis was to develop an accurate seizure forecasting algorithm based on these canine ambulatory iEEG recordings. Main contributions include a quantitative, directed transfer function (DTF)-based, localization of the seizure onset zone (electrode selection), a new fitness function for the proposed genetic algorithm (feature selection), and a quasi-prospective assessment of seizure forecasting on long-term continuous iEEG recordings (total of 893 testing days). Results showed performance improvement compared to previous studies, achieving an average sensitivity of 84.82% and a time in warning of 10 %. The DTF has been previously validated for quantifying causal relations when quasistationarity requirements are met. Although such requirements can be fulfilled in the case of canine recordings due to the relatively low number of channels (objective 1), the identification of stationary segments would be more challenging in the case of high density iEEG recordings. To cope with non-stationarity issues, the spectrum weighted adaptive directed transfer function (swADTF) was recently introduced as a time-varying version of the DTF. The second objective of this thesis was to validate the feasibility of identifying sources and sinks of seizure activity based on the swADTF using high-density iEEG recordings of patients admitted for pre-surgical monitoring at the CHUM. Generators of seizure activity were within the resected volume for patients with good post-surgical outcomes, whereas different or additional seizure foci were identified in patients with poor post-surgical outcomes. Results confirmed the possibility of accurate identification of seizure origin and propagation by means of swADTF paving the way for its use in seizure prediction algorithms by allowing a more tailored electrode selection. Finally, in an attempt to explore new avenues for seizure forecasting, we proposed a new precursor of seizure activity by combining higher order spectral analysis and artificial neural networks (objective 3). Results showed statistically significant differences (p<0.05) between preictal and interictal states using all the bispectrum-extracted features. Normalized bispectral entropy, normalized squared entropy and mean of magnitude, when employed as inputs to a multi-layer perceptron classifier, achieved held-out test accuracies of 78.11%, 72.64%, and 73.26%, respectively. Results of this thesis confirm the feasibility of seizure forecasting based on iEEG recordings; the transition into the ictal state is not random and consists of a “build-up”, leading to seizures. However, additional efforts in terms of electrode selection, feature extraction, hardware and deep learning implementation, are required before the translation of current approaches into commercial devices

    Intelligent Biosignal Processing in Wearable and Implantable Sensors

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    This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

    Ensemble approach on enhanced compressed noise EEG data signal in wireless body area sensor network

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    The Wireless Body Area Sensor Network (WBASN) is used for communication among sensor nodes operating on or inside the human body in order to monitor vital body parameters and movements. One of the important applications of WBASN is patients’ healthcare monitoring of chronic diseases such as epileptic seizure. Normally, epileptic seizure data of the electroencephalograph (EEG) is captured and compressed in order to reduce its transmission time. However, at the same time, this contaminates the overall data and lowers classification accuracy. The current work also did not take into consideration that large size of collected EEG data. Consequently, EEG data is a bandwidth intensive. Hence, the main goal of this work is to design a unified compression and classification framework for delivery of EEG data in order to address its large size issue. EEG data is compressed in order to reduce its transmission time. However, at the same time, noise at the receiver side contaminates the overall data and lowers classification accuracy. Another goal is to reconstruct the compressed data and then recognize it. Therefore, a Noise Signal Combination (NSC) technique is proposed for the compression of the transmitted EEG data and enhancement of its classification accuracy at the receiving side in the presence of noise and incomplete data. The proposed framework combines compressive sensing and discrete cosine transform (DCT) in order to reduce the size of transmission data. Moreover, Gaussian noise model of the transmission channel is practically implemented to the framework. At the receiving side, the proposed NSC is designed based on weighted voting using four classification techniques. The accuracy of these techniques namely Artificial Neural Network, Naïve Bayes, k-Nearest Neighbour, and Support Victor Machine classifiers is fed to the proposed NSC. The experimental results showed that the proposed technique exceeds the conventional techniques by achieving the highest accuracy for noiseless and noisy data. Furthermore, the framework performs a significant role in reducing the size of data and classifying both noisy and noiseless data. The key contributions are the unified framework and proposed NSC, which improved accuracy of the noiseless and noisy EGG large data. The results have demonstrated the effectiveness of the proposed framework and provided several credible benefits including simplicity, and accuracy enhancement. Finally, the research improves clinical information about patients who not only suffer from epilepsy, but also neurological disorders, mental or physiological problems

    Machine learning methods for the study of cybersickness: a systematic review

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    This systematic review offers a world-first critical analysis of machine learning methods and systems, along with future directions for the study of cybersickness induced by virtual reality (VR). VR is becoming increasingly popular and is an important part of current advances in human training, therapies, entertainment, and access to the metaverse. Usage of this technology is limited by cybersickness, a common debilitating condition experienced upon VR immersion. Cybersickness is accompanied by a mix of symptoms including nausea, dizziness, fatigue and oculomotor disturbances. Machine learning can be used to identify cybersickness and is a step towards overcoming these physiological limitations. Practical implementation of this is possible with optimised data collection from wearable devices and appropriate algorithms that incorporate advanced machine learning approaches. The present systematic review focuses on 26 selected studies. These concern machine learning of biometric and neuro-physiological signals obtained from wearable devices for the automatic identification of cybersickness. The methods, data processing and machine learning architecture, as well as suggestions for future exploration on detection and prediction of cybersickness are explored. A wide range of immersion environments, participant activity, features and machine learning architectures were identified. Although models for cybersickness detection have been developed, literature still lacks a model for the prediction of first-instance events. Future research is pointed towards goal-oriented data selection and labelling, as well as the use of brain-inspired spiking neural network models to achieve better accuracy and understanding of complex spatio-temporal brain processes related to cybersickness

    DYNAMIC SELF-ORGANISED NEURAL NETWORK INSPIRED BY THE IMMUNE ALGORITHM FOR FINANCIAL TIME SERIES PREDICTION AND MEDICAL DATA CLASSIFICATION

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    Artificial neural networks have been proposed as useful tools in time series analysis in a variety of applications. They are capable of providing good solutions for a variety of problems, including classification and prediction. However, for time series analysis, it must be taken into account that the variables of data are related to the time dimension and are highly correlated. The main aim of this research work is to investigate and develop efficient dynamic neural networks in order to deal with data analysis issues. This research work proposes a novel dynamic self-organised multilayer neural network based on the immune algorithm for financial time series prediction and biomedical signal classification, combining the properties of both recurrent and self-organised neural networks. The first case study that has been addressed in this thesis is prediction of financial time series. The financial time series signal is in the form of historical prices of different companies. The future prediction of price in financial time series enables businesses to make profits by predicting or simply guessing these prices based on some historical data. However, the financial time series signal exhibits a highly random behaviour, which is non-stationary and nonlinear in nature. Therefore, the prediction of this type of time series is very challenging. In this thesis, a number of experiments have been simulated to evaluate the ability of the designed recurrent neural network to forecast the future value of financial time series. The resulting forecast made by the proposed network shows substantial profits on financial historical signals when compared to the self-organised hidden layer inspired by immune algorithm and multilayer perceptron neural networks. These results suggest that the proposed dynamic neural networks has a better ability to capture the chaotic movement in financial signals. The second case that has been addressed in this thesis is for predicting preterm birth and diagnosing preterm labour. One of the most challenging tasks currently facing the healthcare community is the identification of preterm labour, which has important significances for both healthcare and the economy. Premature birth occurs when the baby is born before completion of the 37-week gestation period. Incomplete understanding of the physiology of the uterus and parturition means that premature labour prediction is a difficult task. The early prediction of preterm births could help to improve prevention, through appropriate medical and lifestyle interventions. One promising method is the use of Electrohysterography. This method records the uterine electrical activity during pregnancy. In this thesis, the proposed dynamic neural network has been used for classifying between term and preterm labour using uterine signals. The results indicated that the proposed network generated improved classification accuracy in comparison to the benchmarked neural network architectures

    A Multi-Tier Distributed fog-based Architecture for Early Prediction of Epileptic Seizures

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    Epilepsy is the fourth most common neurological problem. With 50 million people living with epilepsy worldwide, about one in 26 people will continue experiencing recurring seizures during their lifetime. Epileptic seizures are characterized by uncontrollable movements and can cause loss of awareness. Despite the optimal use of antiepileptic medications, seizures are still difficult to control due to their sudden and unpredictable nature. Such seizures can put the lives of patients and others at risk. For example, seizure attacks while patients are driving could affect their ability to control a vehicle and could result in injuries to the patients as well as others. Notifying patients before the onset of seizures can enable them to avoid risks and minimize accidents, thus, save their lives. Early and accurate prediction of seizures can play a significant role in improving patients’ quality of life and helping doctors to administer medications through providing a historical overview of patient's condition over time. The individual variability and the dynamic disparity in differentiating between the pre-ictal phase (a period before the onset of the seizure) and other seizures phases make the early prediction of seizures a challenging task. Although several research projects have focused on developing a reliable seizure prediction model, numerous challenges still exist and need to be addressed. Most of the existing approaches are not suitable for real-time settings, which requires bio-signals collection and analysis in real-time. Various methods were developed based on the analysis of EEG signals without considering the notification latency and computational cost to support monitoring of multiple patients. Limited approaches were designed based on the analysis of ECG signals. ECG signals can be collected using consumer wearable devices and are suitable for light-weight real-time analysis. Moreover, existing prediction methods were developed based on the analysis of seizure state and ignored the investigation of pre-ictal state. The analysis of the pre-ictal state is essential in the prediction of seizures at an early stage. Therefore, there is a crucial need to design a novel computing model for early prediction of epileptic seizures. This model would greatly assist in improving the patients' quality of lives. This work proposes a multi-tier architecture for early prediction of seizures based on the analysis of two vital signs, namely, Electrocardiography (ECG) and Electroencephalogram (EEG) signals. The proposed architecture comprises of three tiers: (1) sensing at the first tier, (2) lightweight analysis based on ECG signals at the second tier, and (3) deep analysis based on EEG signals at the third tier. The proposed architecture is developed to leverage the potential of fog computing technology at the second tier for a real-time signal analytics and ubiquitous response. The proposed architecture can enable the early prediction of epileptic seizures, reduce the notification latency, and minimize the energy consumption on real-time data transmissions. Moreover, the proposed architecture is designed to allow for both lightweight and extensive analytics, thus make accurate and reliable decisions. The proposed lightweight model is formulated using the analysis of ECG signals to detect the pre-ictal state. The lightweight model utilizes the Least Squares Support Vector Machines (LS-SVM) classifier, while the proposed extensive analytics model analyzes EEG signals and utilizes Deep Belief Network (DBN) to provide an accurate classification of the patient’s state. The performance of the proposed architecture is evaluated in terms of latency minimization and energy consumption in comparison with the cloud. Moreover, the performance of the proposed prediction models is evaluated using three datasets. Various performance metrics were used to investigate the prediction model performance, including: accuracy, sensitivity, specificity, and F1-Measure. The results illustrate the merits of the proposed architecture and show significant improvement in the early prediction of seizures in terms of accuracy, sensitivity, and specificity

    Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

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    Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization
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