1,253 research outputs found
A Performance Comparison of Neural Network and SVM Classifiers Using EEG Spectral Features to Predict Epileptic Seizures
Epilepsy is one of the most common neurological disorders, and aïŹicts approximately 70 million people globally. 30-40% of patients have refractory epilepsy, where seizures cannot be controlled by anti-epileptic medication, and surgery is neither appropriate, nor available. The unpredictable nature of epileptic seizures is the primary cause of mortality among patients, and leads to signiïŹcant psychosocial disability. If seizures could be predicted in advance, automatic seizure warning systems could transform the lives of millions of people. This study presents a performance comparison of artiïŹcial neural network and sup port vector machine classiïŹers, using EEG spectral features to predict the onset of epileptic seizures. In addition, the study also examines the inïŹuence of EEG window size, feature selection, and data sampling on classiïŹcation performance. A total of 216 generalised models were trained and tested on a public seizure database, which contained over 1300 hours of EEG data from 7 subjects. The results showed that ANN outperform SVM, when using spectral features (p = 0.035). The beta and gamma frequency bands were shown to be the best predictors of seizure onset. No signiïŹcant diïŹerences in performance were determined for the dif ferent window sizes, or for the feature selection methods. The data sampling method signiïŹcantly inïŹuenced the performance (p \u3c 0.001), and highlighted the importance of treating class imbalance in EEG datasets
Approaches to Feature Identification and Feature Selection for Binary and Multi-Class Classification
University of Minnesota Ph.D. dissertation. 2007. Major: Electrical Engineering. Advisor: Keshab Parhi. 1 computer file (PDF); 182 pages.In this dissertation, we address issues of (a) feature identification and extraction, and (b) feature selection. Nowadays, datasets are getting larger and larger, especially due to the growth of the internet data and bio-informatics. Thus, applying feature extraction and selection to reduce the dimensionality of the data size is crucial to data mining. Our first objective is to identify discriminative patterns in time series datasets. Using auto-regressive modeling, we show that, if two bands are selected appropriately, then the ratio of band power is amplified for one of the two states. We introduce a novel frequency-domain power ratio (FDPR) test to determine how these two bands should be selected. The FDPR computes the ratio of the two model filter transfer functions where the model filters are estimated using different parts of the time-series that correspond to two different states. The ratio implicitly cancels the effect of change of variance of the white noise that is input to the model. Thus, even in a highly non-stationary environment, the ratio feature is able to correctly identify a change of state. Synthesized data and application examples from seizure prediction are used to prove validity of the proposed approach. We also illustrate that combining the spectral power ratios features with absolute spectral powers and relative spectral powers as a feature set and then carefully selecting a small number features from a few electrodes can achieve a good detection and prediction performances on short-term datasets and long-term fragmented datasets collected from subjects with epilepsy. Our second objective is to develop efficient feature selection methods for binary classification (MUSE) and multi-class classification (M3U) that effectively select important features to achieve a good classification performance. We propose a novel incremental feature selection method based on minimum uncertainty and feature sample elimination (referred as MUSE) for binary classification. The proposed approach differs from prior mRMR approach in how the redundancy of the current feature with previously selected features is reduced. In the proposed approach, the feature samples are divided into a pre-specified number of bins; this step is referred to as feature quantization. A novel uncertainty score for each feature is computed by summing the conditional entropies of the bins, and the feature with the lowest uncertainty score is selected. For each bin, its impurity is computed by taking the minimum of the probability of Class 1 and of Class 2. The feature samples corresponding to the bins with impurities below a threshold are discarded and are not used for selection of the subsequent features. The significance of the MUSE feature selection method is demonstrated using the two datasets: arrhythmia and hand digit recognition (Gisette), and datasets for seizure prediction from five dogs and two humans. It is shown that the proposed method outperforms the prior mRMR feature selection method for most cases. We further extends the MUSE algorithm for multi-class classification problems. We propose a novel multiclass feature selection algorithm based on weighted conditional entropy, also referred to as uncertainty. The goal of the proposed algorithm is to select a feature subset such that, for each feature sample, there exists a feature that has a low uncertainty score in the selected feature subset. Features are first quantized into different bins. The proposed feature selection method first computes an uncertainty vector from weighted conditional entropy. Lower the uncertainty score for a class, better is the separability of the samples in that class. Next, an iterative feature selection method selects a feature in each iteration by (1) computing the minimum uncertainty score for each feature sample for all possible feature subset candidates, (2) computing the average minimum uncertainty score across all feature samples, and (3) selecting the feature that achieves the minimum of the mean of the minimum uncertainty score. The experimental results show that the proposed algorithm outperforms mRMR and achieves lower misclassification rates using various types of publicly available datasets. In most cases, the number of features necessary for a specified misclassification error is less than that required by traditional methods
Performance Analysis of Deep-Learning and Explainable AI Techniques for Detecting and Predicting Epileptic Seizures
Epilepsy is one of the most common neurological diseases globally. Notably, people in low to middle-income nations could not get proper epilepsy treatment due to the cost and availability of medical infrastructure. The risk of sudden unpredicted death in Epilepsy is considerably high. Medical statistics reveal that people with Epilepsy die more prematurely than those without the disease. Early and accurately diagnosing diseases in the medical field is challenging due to the complex disease patterns and the need for time-sensitive medical responses to the patients. Even though numerous machine learning and advanced deep learning techniques have been employed for the seizure stages classification and prediction, understanding the causes behind the decision is difficult, termed a black box problem. Hence, doctors and patients are confronted with the black box decision-making to initiate the appropriate treatment and understand the disease patterns respectively. Owing to the scarcity of epileptic Electroencephalography (EEG) data, training the deep learning model with diversified epilepsy knowledge is still critical. Explainable Artificial intelligence has become a potential solution to provide the explanation and result interpretation of the learning models. By applying the explainable AI, there is a higher possibility of examining the features that influence the decision-making that either the patient recorded from epileptic or non-epileptic EEG signals. This paper reviews the various deep learning and Explainable AI techniques used for detecting and predicting epileptic seizures using EEG data. It provides a comparative analysis of the different techniques based on their performance
Machine learning based brain signal decoding for intelligent adaptive deep brain stimulation
Sensing enabled implantable devices and next-generation neurotechnology allow real-time adjustments of invasive neuromodulation. The identification of symptom and disease-specific biomarkers in invasive brain signal recordings has inspired the idea of demand dependent adaptive deep brain stimulation (aDBS). Expanding the clinical utility of aDBS with machine learning may hold the potential for the next breakthrough in the therapeutic success of clinical brain computer interfaces. To this end, sophisticated machine learning algorithms optimized for decoding of brain states from neural time-series must be developed. To support this venture, this review summarizes the current state of machine learning studies for invasive neurophysiology. After a brief introduction to the machine learning terminology, the transformation of brain recordings into meaningful features for decoding of symptoms and behavior is described. Commonly used machine learning models are explained and analyzed from the perspective of utility for aDBS. This is followed by a critical review on good practices for training and testing to ensure conceptual and practical generalizability for real-time adaptation in clinical settings. Finally, first studies combining machine learning with aDBS are highlighted. This review takes a glimpse into the promising future of intelligent adaptive DBS (iDBS) and concludes by identifying four key ingredients on the road for successful clinical adoption: i) multidisciplinary research teams, ii) publicly available datasets, iii) open-source algorithmic solutions and iv) strong world-wide research collaborations.Fil: Merk, Timon. CharitĂ© â UniversitĂ€tsmedizin Berlin; AlemaniaFil: Peterson, Victoria. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - Santa Fe. Instituto de MatemĂĄtica Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de MatemĂĄtica Aplicada del Litoral; Argentina. Harvard Medical School; Estados UnidosFil: Köhler, Richard. CharitĂ© â UniversitĂ€tsmedizin Berlin; AlemaniaFil: Haufe, Stefan. CharitĂ© â UniversitĂ€tsmedizin Berlin; AlemaniaFil: Richardson, R. Mark. Harvard Medical School; Estados UnidosFil: Neumann, Wolf Julian. CharitĂ© â UniversitĂ€tsmedizin Berlin; Alemani
Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings
LâĂ©pilepsie est une des maladies neurologiques les plus frĂ©quentes, touchant prĂšs dâun
pourcent de la population mondiale. De nos jours, bien quâenviron deux tiers des patients
Ă©pileptiques rĂ©pondent adĂ©quatement aux traitements pharmacologiques, il reste quâun tiers des
patients doivent vivre avec des crises invalidantes et imprévisibles. Quoique la chirurgie
dâĂ©pilepsie puisse ĂȘtre une autre option thĂ©rapeutique envisageable, le recours Ă la chirurgie de
résection demeure trÚs faible en partie pour des raisons diverses (taux de réussite modeste, peur
des complications, perceptions nĂ©gatives). Dâautres 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 dâenregistrements de lâactivitĂ© cĂ©rĂ©brale des patients. La capacitĂ© de prĂ©dire la survenue de
crises permettrait notamment aux patients, aidants naturels ou personnels médical 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, dâimportants efforts ont Ă©tĂ©
dĂ©ployĂ©s pour dĂ©velopper des algorithmes de prĂ©diction de crises et dâen amĂ©liorer les
performances.
Toutefois, le manque dâenregistrements Ă©lectroencĂ©phalographiques intracrĂąniens (iEEG) de
longue durée de qualité, la quantité limitée de crises, ainsi que la courte durée des périodes
interictales constituaient des obstacles majeurs à une évaluation adéquate de la performance des
algorithmes de prĂ©diction de crises. RĂ©cemment, la disponibilitĂ© en ligne dâenregistrements iEEG
continus avec échantillonnage bilatéral (des deux hémisphÚres) acquis chez des chiens atteints
dâĂ©pilepsie focale Ă lâaide du dispositif de surveillance ambulatoire implantable NeuroVista a
partiellement facilitĂ© cette tĂąche. Cependant, une des limitations associĂ©es Ă lâutilisation de ces
donnĂ©es durant la conception dâun algorithme de prĂ©diction de crises Ă©tait lâabsence
dâinformation concernant la zone exacte de dĂ©but des crises (information non fournie par les
gestionnaires de cette base de données en ligne). Le premier objectif de cette thÚse était la mise
en oeuvre dâun algorithme prĂ©cis de prĂ©diction de crises basĂ© sur des enregistrements iEEG canins
de longue durée. Les principales contributions à cet égard incluent une localisation quantitative
de la zone dâapparition des crises (basĂ©e sur la fonction de transfert dirigĂ© âDTF), lâutilisation
dâune nouvelle fonction de coĂ»t via lâalgorithme gĂ©nĂ©tique proposĂ©, ainsi quâune Ă©valuation
quasi-prospective des performances de prĂ©diction (donnĂ©es de test dâun total de 893 jours). Les rĂ©sultats ont montrĂ© une amĂ©lioration des performances de prĂ©diction par rapport aux Ă©tudes
antérieures, atteignant une sensibilité moyenne de 84.82 % et un temps en avertissement de 10 %.
La DTF, utilisée précédemment comme mesure de connectivité pour déterminer le réseau
épileptique (objectif 1), a été préalablement validée pour quantifier les relations causales entre les
canaux lorsque les exigences de quasi-stationnarité 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-stationnarité, la fonction de transfert adaptatif pondérée par le spectre
(Spectrum weighted adaptive directed transfer function - swADTF) a Ă©tĂ© introduit en tant quâune
version variant dans le temps de la DTF. Le second objectif de cette thĂšse Ă©tait de valider la
possibilitĂ© dâidentifier les endroits Ă©metteurs (ou sources) et rĂ©cepteurs dâactivitĂ© Ă©pileptiques en
appliquant la swADTF sur des enregistrements iEEG de haute densité provenant de patients
admis pour Ă©valuation prĂ©-chirurgicale au CHUM. Les gĂ©nĂ©rateurs dâactivitĂ© Ă©pileptique Ă©taient
dans le volume réséqué pour les patients ayant des bons résultats post-chirurgicaux alors que
différents foyers ont été identifiés chez les patients ayant eu de mauvais résultats postchirurgicaux.
Ces rĂ©sultats dĂ©montrent la possibilitĂ© dâune identification prĂ©cise des sources et
rĂ©cepteurs dâactivitĂ©s Ă©pileptiques au moyen de la swADTF ouvrant la porte Ă la possibilitĂ© dâune
meilleure sĂ©lection dâĂ©lectrodes de maniĂšre quantitative dans un contexte de dĂ©veloppement
dâalgorithme de prĂ©diction de crises chez lâhumain.
Dans le but dâexplorer de nouvelles avenues pour la prĂ©diction de crises Ă©pileptiques, un
nouveau prĂ©curseur a aussi Ă©tĂ© Ă©tudiĂ© combinant lâanalyse des spectres dâordre supĂ©rieur et les
réseaux de neurones artificiels (objectif 3). Les résultats ont montré des différences
statistiquement significatives (p<0.05) entre lâĂ©tat prĂ©ictal et lâĂ©tat interictal en utilisant chacune
des caractéristiques extraites du bi-spectre. Utilisées comme entrées à un perceptron multicouche,
lâentropie bispectrale normalisĂ©e, lâentropie carrĂ© normalisĂ©e, et la moyenne ont atteint des
précisions respectives de 78.11 %, 72.64% et 73.26%.
Les résultats de cette thÚse confirment la faisabilité de prédiction de crises à partir
dâenregistrements dâĂ©lectroencĂ©phalographie intracrĂąniens. Cependant, des efforts
supplĂ©mentaires en termes de sĂ©lection dâĂ©lectrodes, dâextraction de caractĂ©ristiques, dâutilisation
des techniques dâapprentissage profond et dâimplĂ©mentation Hardware, sont nĂ©cessaires avant
lâintĂ©gration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT
Epilepsy is a chronic condition characterized by recurrent âunpredictableâ 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 âbuild-upâ, 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
Towards developing a reliable medical device for automated epileptic seizure detection in the ICU
Abstract. Epilepsy is a prevalent neurological disorder that affects millions of people globally, and its diagnosis typically involves laborious manual inspection of electroencephalography (EEG) data. Automated detection of epileptic seizures in EEG signals could potentially improve diagnostic accuracy and reduce diagnosis time, but there should be special attention to the number of false alarms to reduce unnecessary treatments and costs. This research presents a study on the use of machine learning techniques for EEG seizure detection with the aim of investigating the effectiveness of different algorithms in terms of high sensitivity and low false alarm rates for feature extraction, selection, pre-processing, classification, and post-processing in designing a medical device for detecting seizure activity in EEG data. The current state-of-the-art methods which are validated clinically using large amounts of data are introduced.
The study focuses on finding potential machine learning methods, considering KNN, SVM, decision trees and, Random forests, and compares their performance on the task of seizure detection using features introduced in the literature. Also using ensemble methods namely, bootstrapping and majority voting techniques we achieved a sensitivity of 0.80 and FAR/h of 2.10, accuracy of 97.1% and specificity of 98.2%. Overall, the findings of this study can be useful for developing more accurate and efficient algorithms for EEG seizure detection medical device, which can contribute to the early diagnosis and treatment of epilepsy in the intensive care unit for critically ill patients
Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring
Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision.
The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring.
In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively.
Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time.
These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces
Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
One of the challenges in modeling cognitive events from electroencephalogram
(EEG) data is finding representations that are invariant to inter- and
intra-subject differences, as well as to inherent noise associated with such
data. Herein, we propose a novel approach for learning such representations
from multi-channel EEG time-series, and demonstrate its advantages in the
context of mental load classification task. First, we transform EEG activities
into a sequence of topology-preserving multi-spectral images, as opposed to
standard EEG analysis techniques that ignore such spatial information. Next, we
train a deep recurrent-convolutional network inspired by state-of-the-art video
classification to learn robust representations from the sequence of images. The
proposed approach is designed to preserve the spatial, spectral, and temporal
structure of EEG which leads to finding features that are less sensitive to
variations and distortions within each dimension. Empirical evaluation on the
cognitive load classification task demonstrated significant improvements in
classification accuracy over current state-of-the-art approaches in this field.Comment: To be published as a conference paper at ICLR 201
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