A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction

Phase Synchronization Operator for On-Chip Brain Functional Connectivity Computation

This paper presents an integer-based digital processor for the calculation of phase synchronization between two neural signals. It is based on the measurement of time periods between two consecutive minima. The simplicity of the approach allows for the use of elementary digital blocks, such as registers, counters, and adders. The processor, fabricated in a 0.18- μ m CMOS process, only occupies 0.05 mm 2 and consumes 15 nW from a 0.5 V supply voltage at a signal input rate of 1024 S/s. These low-area and low-power features make the proposed processor a valuable computing element in closed-loop neural prosthesis for the treatment of neural disorders, such as epilepsy, or for assessing the patterns of correlated activity in neural assemblies through the evaluation of functional connectivity maps.Ministerio de Economía y Competitividad TEC2016-80923-POffice of Naval Research (USA) N00014-19-1-215

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

High-performance detection of epilepsy in seizure-free EEG recordings: A novel machine learning approach using very specific epileptic EEG sub-bands

We applied machine learning to diagnose epilepsy based on the fine-graded spectral analysis of seizure-free (resting state) EEG recordings. Despite using unspecific agglomerated EEG spectra, our fine-graded spectral analysis specifically identified the two EEG resting state sub-bands differentiating healthy people from epileptics (1.5-2 Hz and 11-12.5 Hz). The rigorous evaluation of completely unseen data of 100 EEG recordings (50 belonging to epileptics and the other 50 to healthy people) shows that the approach works successfully, achieving an outstanding accuracy of 99 percent, which significantly outperforms the current benchmark of 70% to 95% by a panel of up to three experienced neurologists. Our epilepsy diagnosis classifier can be implemented in modern EEG analysis devices, especially in intensive care units where early diagnosis and appropriate treatment are decisive in life and death scenarios and where physicians’ error rates are particularly high. Our approach is accurate, robust, fast, and cost-efficient and substantially contributes to Information Systems research in healthcare. The approach is also of high practical and theoretical relevance

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

L’épilepsie est une des maladies neurologiques les plus fréquentes, touchant près d’un pourcent de la population mondiale. De nos jours, bien qu’environ deux tiers des patients épileptiques répondent adéquatement aux traitements pharmacologiques, il reste qu’un tiers des patients doivent vivre avec des crises invalidantes et imprévisibles. Quoique la chirurgie d’épilepsie puisse être une autre option thérapeutique envisageable, le recours à la chirurgie de résection demeure très faible en partie pour des raisons diverses (taux de réussite modeste, peur des complications, perceptions négatives). D’autres avenues de traitement sont donc souhaitables. Une piste actuellement explorée par des groupes de chercheurs est de tenter de prédire les crises à partir d’enregistrements de l’activité cérébrale des patients. La capacité de prédire la survenue de crises permettrait notamment aux patients, aidants naturels ou personnels médical de prendre des mesures de précaution pour éviter les désagréments reliés aux crises voire même instaurer un traitement pour les faire avorter. Au cours des dernières années, d’importants efforts ont été déployés pour développer des algorithmes de prédiction de crises et d’en améliorer les performances. Toutefois, le manque d’enregistrements électroencéphalographiques intracrâniens (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 prédiction de crises. Récemment, la disponibilité en ligne d’enregistrements iEEG continus avec échantillonnage bilatéral (des deux hémisphères) acquis chez des chiens atteints d’épilepsie focale à l’aide du dispositif de surveillance ambulatoire implantable NeuroVista a partiellement facilité cette tâche. Cependant, une des limitations associées à l’utilisation de ces données durant la conception d’un algorithme de prédiction de crises était l’absence d’information concernant la zone exacte de début des crises (information non fournie par les gestionnaires de cette base de données en ligne). Le premier objectif de cette thèse était la mise en oeuvre d’un algorithme précis de prédiction de crises basé sur des enregistrements iEEG canins de longue durée. Les principales contributions à cet égard incluent une localisation quantitative de la zone d’apparition des crises (basée sur la fonction de transfert dirigé –DTF), l’utilisation d’une nouvelle fonction de coût via l’algorithme génétique proposé, ainsi qu’une évaluation quasi-prospective des performances de prédiction (données de test d’un total de 893 jours). Les résultats ont montré une amélioration des performances de prédiction par rapport aux études 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 été introduit en tant qu’une version variant dans le temps de la DTF. Le second objectif de cette thèse était de valider la possibilité d’identifier les endroits émetteurs (ou sources) et récepteurs d’activité épileptiques en appliquant la swADTF sur des enregistrements iEEG de haute densité provenant de patients admis pour évaluation pré-chirurgicale au CHUM. Les générateurs d’activité épileptique étaient 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 résultats démontrent la possibilité d’une identification précise des sources et récepteurs d’activités épileptiques au moyen de la swADTF ouvrant la porte à la possibilité d’une meilleure sélection d’électrodes de manière quantitative dans un contexte de développement d’algorithme de prédiction de crises chez l’humain. Dans le but d’explorer de nouvelles avenues pour la prédiction de crises épileptiques, un nouveau précurseur a aussi été étudié combinant l’analyse des spectres d’ordre supérieur et les réseaux de neurones artificiels (objectif 3). Les résultats ont montré des différences statistiquement significatives (p<0.05) entre l’état préictal et l’état interictal en utilisant chacune des caractéristiques extraites du bi-spectre. Utilisées comme entrées à un perceptron multicouche, l’entropie bispectrale normalisée, l’entropie carré normalisée, et la moyenne ont atteint des précisions respectives de 78.11 %, 72.64% et 73.26%. Les résultats de cette thèse confirment la faisabilité de prédiction de crises à partir d’enregistrements d’électroencéphalographie intracrâniens. Cependant, des efforts supplémentaires en termes de sélection d’électrodes, d’extraction de caractéristiques, d’utilisation des techniques d’apprentissage profond et d’implémentation Hardware, sont nécessaires avant l’intégration 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

Deep learning approach for epileptic seizure detection

Abstract. Epilepsy is the most common brain disorder that affects approximately fifty million people worldwide, according to the World Health Organization. The diagnosis of epilepsy relies on manual inspection of EEG, which is error-prone and time-consuming. Automated epileptic seizure detection of EEG signal can reduce the diagnosis time and facilitate targeting of treatment for patients. Current detection approaches mainly rely on the features that are designed manually by domain experts. The features are inflexible for the detection of a variety of complex patterns in a large amount of EEG data. Moreover, the EEG is non-stationary signal and seizure patterns vary across patients and recording sessions. EEG data always contain numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address these challenges deep learning approaches are examined in this paper. Deep learning methods were applied to a large publicly available dataset, the Children’s Hospital of Boston-Massachusetts Institute of Technology dataset (CHB-MIT). The present study includes three experimental groups that are grouped based on the pre-processing steps. The experimental groups contain 3–4 experiments that differ between their objectives. The time-series EEG data is first pre-processed by certain filters and normalization techniques, and then the pre-processed signal was segmented into a sequence of non-overlapping epochs. Second, time series data were transformed into different representations of input signals. In this study time-series EEG signal, magnitude spectrograms, 1D-FFT, 2D-FFT, 2D-FFT magnitude spectrum and 2D-FFT phase spectrum were investigated and compared with each other. Third, time-domain or frequency-domain signals were used separately as a representation of input data of VGG or DenseNet 1D. The best result was achieved with magnitude spectrograms used as representation of input data in VGG model: accuracy of 0.98, sensitivity of 0.71 and specificity of 0.998 with subject dependent data. VGG along with magnitude spectrograms produced promising results for building personalized epileptic seizure detector. There was not enough data for VGG and DenseNet 1D to build subject-dependent classifier.Epileptisten kohtausten havaitseminen syväoppimisella lähestymistavalla. Tiivistelmä. Epilepsia on yleisin aivosairaus, joka Maailman terveysjärjestön mukaan vaikuttaa noin viiteenkymmeneen miljoonaan ihmiseen maailmanlaajuisesti. Epilepsian diagnosointi perustuu EEG:n manuaaliseen tarkastamiseen, mikä on virhealtista ja aikaa vievää. Automaattinen epileptisten kohtausten havaitseminen EEG-signaalista voi potentiaalisesti vähentää diagnoosiaikaa ja helpottaa potilaan hoidon kohdentamista. Nykyiset tunnistusmenetelmät tukeutuvat pääasiassa piirteisiin, jotka asiantuntijat ovat määritelleet manuaalisesti, mutta ne ovat joustamattomia monimutkaisten ilmiöiden havaitsemiseksi suuresta määrästä EEG-dataa. Lisäksi, EEG on epästationäärinen signaali ja kohtauspiirteet vaihtelevat potilaiden ja tallennusten välillä ja EEG-data sisältää aina useita kohinatyyppejä, jotka huonontavat epilepsiakohtauksen havaitsemisen tarkkuutta. Näihin haasteisiin vastaamiseksi tässä diplomityössä tarkastellaan soveltuvatko syväoppivat menetelmät epilepsian havaitsemiseen EEG-tallenteista. Aineistona käytettiin suurta julkisesti saatavilla olevaa Bostonin Massachusetts Institute of Technology lastenklinikan tietoaineistoa (CHB-MIT). Tämän työn tutkimus sisältää kolme koeryhmää, jotka eroavat toisistaan esikäsittelyvaiheiden osalta: aikasarja-EEG-data esikäsiteltiin perinteisten suodattimien ja normalisointitekniikoiden avulla, ja näin esikäsitelty signaali segmentoitiin epookkeihin. Kukin koeryhmä sisältää 3–4 koetta, jotka eroavat menetelmiltään ja tavoitteiltaan. Kussakin niistä epookkeihin jaettu aikasarjadata muutettiin syötesignaalien erilaisiksi esitysmuodoiksi. Tässä tutkimuksessa tutkittiin ja verrattiin keskenään EEG-signaalia sellaisenaan, EEG-signaalin amplitudi-spektrogrammeja, 1D-FFT-, 2D-FFT-, 2D-FFT-amplitudi- ja 2D-FFT -vaihespektriä. Näin saatuja aika- ja taajuusalueen signaaleja käytettiin erikseen VGG- tai DenseNet 1D -mallien syötetietoina. Paras tulos saatiin VGG-mallilla kun syötetietona oli amplitudi-spektrogrammi ja tällöin tarkkuus oli 0,98, herkkyys 0,71 ja spesifisyys 0,99 henkilöstä riippuvaisella EEG-datalla. VGG yhdessä amplitudi-spektrogrammien kanssa tuottivat lupaavia tuloksia henkilökohtaisen epilepsiakohtausdetektorin rakentamiselle. VGG- ja DenseNet 1D -malleille ei ollut tarpeeksi EEG-dataa henkilöstä riippumattoman luokittelijan opettamiseksi

TEMPORAL DATA EXTRACTION AND QUERY SYSTEM FOR EPILEPSY SIGNAL ANALYSIS

The 2016 Epilepsy Innovation Institute (Ei2) community survey reported that unpredictability is the most challenging aspect of seizure management. Effective and precise detection, prediction, and localization of epileptic seizures is a fundamental computational challenge. Utilizing epilepsy data from multiple epilepsy monitoring units can enhance the quantity and diversity of datasets, which can lead to more robust epilepsy data analysis tools. The contributions of this dissertation are two-fold. One is the implementation of a temporal query for epilepsy data; the other is the machine learning approach for seizure detection, seizure prediction, and seizure localization. The three key components of our temporal query interface are: 1) A pipeline for automatically extract European Data Format (EDF) information and epilepsy annotation data from cross-site sources; 2) Data quantity monitoring for Epilepsy temporal data; 3) A web-based annotation query interface for preliminary research and building customized epilepsy datasets. The system extracted and stored about 450,000 epilepsy-related events of more than 2,497 subjects from seven institutes up to September 2019. Leveraging the epilepsy temporal events query system, we developed machine learning models for seizure detection, prediction, and localization. Using 135 extracted features from EEG signals, we trained a channel-based eXtreme Gradient Boosting model to detect seizures on 8-second EEG segments. A long-term EEG recording evaluation shows that the model can detect about 90.34% seizures on an existing EEG dataset with 961 hours of data. The model achieved 89.88% accuracy, 92.32% sensitivity, and 84.76% AUC based on the segments evaluation. We also introduced a transfer learning approach consisting of 1) a base deep learning model pre-trained by ImageNet dataset and 2) customized fully connected layers, to train the patient-specific pre-ictal and inter-ictal data from our database. Two convolutional neural network architectures were evaluated using 53 pre-ictal segments and 265 continuous hours of inter-ictal EEG data. The evaluation shows that our model reached 86.79% sensitivity and 3.38% false-positive rate. Another transfer learning model for seizure localization uses a pre-trained ResNext50 structure and was trained with an image augmentation dataset labeling by fingerprint. Our model achieved 88.22% accuracy, 34.99% sensitivity, 1.02% false-positive rate, and 34.3% positive likelihood rate