Thèses sur le sujet « Epileptic seizures detection »
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McGroggan, N. « Neutral network detection of epileptic seizures in the electroencephalogram ». Thesis, University of Oxford, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249426.
Texte intégralValko, Andras, et Antoine Homsi. « Predictive detection of epileptic seizures in EEG for reactive care ». Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15078.
Texte intégralPISANO, BARBARA. « Machine Learning Techniques for Detection of Nocturnal Epileptic Seizures from Electroencephalographic Signals ». Doctoral thesis, Università degli Studi di Cagliari, 2018. http://hdl.handle.net/11584/255953.
Texte intégralFan, Xiaoya. « Dynamics underlying epileptic seizures : insights from a neural mass model ». Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/279546/6/contratXF.pdf.
Texte intégralDoctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
Shahidi, Zandi Ali. « Scalp EEG quantitative analysis : automated real-time detection and prediction of epileptic seizures ». Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42748.
Texte intégralGheryani, Mostafa. « Epileptic seizure and anomaly detection in internet of medical things ». Electronic Thesis or Diss., Université Paris Cité, 2021. http://www.theses.fr/2021UNIP5211.
Texte intégralThe goal of my PhD is to investigate the characteristics of inertial and physiological signals via IoMT systems generated by epileptic seizure and to develop an algorithm to detect the seizure. The focus of the algorithms lies in nocturnal seizures where the risk of SUDEP is high because the patients are unsupervised while sleeping. In chapter III analysis we propose an IoMT platform for seizure detection. The proposed framework approach starts by deriving the RMS for ACM and Gyro, followed by the normalization of whole signals (ACM, Gyro and EMG) in the same range, and aggregate all into one signal. The chart’s control with its upper and lower limits are derived in the training phase and used to detect abnormal seizures and to raise an alarm. In chapter IV Our proposed algorithm is based on deriving instantaneous power measurements in a sliding window containing 3D ACM or 3D Gyro or EMG. The residual between forecasted and measured power is used as input for the detection algorithm based on Shewhart Control Chart (SCC). When the difference between forecasted and derived power exceeds chart limits [lower, upper] for several consecutive slots, an alarm is raised. Our proposed approach provides low FAR (4%) and sensitivity of 97%. In Chapter V our proposed method starts by reducing the dimension of collected data using RMS to derive one signal from 3D ACM and one signal from 3D Gyro. With the derived 3 collected signals (ACM, Gyro and EMG), we apply VTP to derive one signal used as input for anomaly detection mechanism. The robust version of z-score is applied on the resulting product signal to detect deviations associated with seizures before raising an alarm. Our experimental results show that our proposed approach is robust against nocturnal movements and achieves a high level of detection accuracy with low false alarm rate. Afterward, we compare the performance of our approach with the zero-crossings method calculated from sEMG. Our approach shows that the detection accuracy using VTP outperforms zero-crossing count over an overlapping sliding window of 1 second. In chapter VI, we propose an approach using the IoMT devices to acquire EMG, ACM and Gyro data and to transmit the measurements to a LPU for processing. When the LPU detects abnormal changes in the measurements, it raises an alarm for assistant. Our proposed approach uses SVM with reject option to distinguish seizures from normal daily life activity. Features presenting physiological changes of muscular activity and inertial data were extracted in LPU and are used as input for the detection algorithm. The reject option in SVM is used to enhance the reliability of the monitoring system and to reduce FAR, where the user is notified and can discard the alarm in his smartphone in the absence of seizure. The conducted experiments proved that our proposed approach could achieve a good accuracy with only 4% of false alarm rate. Finally, since we are using IoMT sensors, which are susceptible to data security issues. We proposed a solution to prevent Man in the Middle (MitM) attack, which can identify healthcare emergencies of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this chapter, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from Received Signal Strength Indication (RSSI). Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%
McNally, Kelly A. « Application of Signal Detection Theory to Verbal Memory Testing for the Differential Diagnosis of Psychogenic Nonepileptic and Epileptic Seizures ». University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178883120.
Texte intégralTruong, Nhan Duy. « Epileptic Seizure Detection and Forecasting Ecosystems ». Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/21932.
Texte intégralRamachandran, Ganesan. « Comparison of algorithms for epileptic seizure detection ». [Gainesville, Fla.] : University of Florida, 2002. http://purl.fcla.edu/fcla/etd/UFE0000597.
Texte intégralLiu, Hui. « Online automatic epileptic seizure detection from electroencephalogram (EEG) ». [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0012941.
Texte intégralKang, Lövgren Sandy, et Christine Rosquist. « Machine Learning Methods for EEG-based Epileptic Seizure Detection ». Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259638.
Texte intégralEpilepsi är en av de vanligaste neurologiska sjukdomarna, vilken påverkar miljontals av människor över hela världen. Sjukdomen har alltid varit relevant inom det biomedicinska området på grund av hälsoriskerna den orsakar. Epilepsi karakteriseras av upprepade, oprovocerade anfall och kan fastställas med hjälp av elektroencefalografi (EEG). EEG mäter den elektriska aktiviteten i hjärnan, och en viktig aspekt inom epilepsiforskning inkluderar analys av EEG-data för att kunna detektera epileptiska anfall i ett tidigt skede. Mycket arbete har hittills gjorts på patient-specifika klassificeringsmetoder, medan det är svårare att bygga patient-oberoende modeller. Denna studie fokuserar på patient-oberoende klassificering eftersom den är mer komplicerad på grund av hur EEG-data skiljer sig mellan olika individer. En jämförelse av maskinlärningsmetoder för EEG-baserad detektion av epileptiska anfall utfördes. Algoritmerna som jämfördes var Support Vector Machine (SVM) och K-Nearest Neighbor (KNN). Vår studie visar att båda metoderna gav liknande resultat, dock uppnådde KNN en något högre noggranhet under vissa omständigheter.
Yang, Yikai. « Towards advanced application of artificial intelligence (AI) in epileptic seizure management ». Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/30022.
Texte intégralJuffali, Walid. « Neural anomalies monitoring : applications to epileptic seizure detection and prediction ». Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10570.
Texte intégralMoghim, Negin. « Exploring machine learning techniques in epileptic seizure detection and prediction ». Thesis, Heriot-Watt University, 2014. http://hdl.handle.net/10399/2846.
Texte intégralZhu, Dongqing. « Time-frequency and Hidden Markov Model Methods for Epileptic Seizure Detection ». University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1242070584.
Texte intégralEsteller, Rosana. « Detection of seizure onset in epileptic patients from intracranial EEG signals ». Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/15620.
Texte intégralShoeb, Ali Hossam 1981. « Application of machine learning to epileptic seizure onset detection and treatment ». Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54669.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (p. 157-162).
Epilepsy is a chronic disorder of the central nervous system that predisposes individuals to experiencing recurrent seizures. It affects 3 million Americans and 50 million people world-wide. A seizure is a transient aberration in the brain's electrical activity that produces disruptive physical symptoms such as a lapse in attention and memory, a sensory hallucination, or a whole-body convulsion. Approximately 1 out of every 3 individuals with epilepsy continues to experience frequent seizures despite treatment with multiple anti-epileptic drugs. These intractable seizures pose a serious risk of injury, limit the independence and mobility of an individual, and result in both social isolation and economic hardship. This thesis presents novel technology intended to ease the burden of intractable seizures. At its heart is a method for computerized detection of seizure onset. The method uses machine learning to construct patient-specific classifiers that are capable of rapid, sensitive, and specific detection of seizure onset. The algorithm detects the onset of a seizure through analysis of the brain's electrical activity alone or in concert with other physiologic signals. When trained on 2 or more seizures and tested on 844 hours of continuous scalp EEG from 23 pediatric epilepsy patients, our algorithm detected 96% of 163 test seizures with a median detection delay of 3 seconds and a median false detection rate of 2 false detections per 24 hour period.
(cont.) In this thesis we also discuss how our detector can be embedded within a low power, implantable medical device to enable the delivery of just-in-time therapy that has the potential to either eliminate or attenuate the clinical symptoms associated with seizures. Finally, we report on the in-hospital use of our detector to enable delay-sensitive therapeutic and diagnostic applications. We demonstrate the feasibility of using the algorithm to control the Vagus Nerve Stimulator (an implantable neuro stimulator for the treatment of intractable seizures), and to initiate ictal SPECT (a functional neuroimaging modality useful for localizing the cerebral site of origin of a seizure).
by Ali Hossam Shoeb.
Ph.D.
Kharbouch, Alaa Amin. « Automatic detection of epileptic seizure onset and termination using intracranial EEG ». Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/75638.
Texte intégralCataloged from PDF version of thesis.
Includes bibliographical references (p. 87-90).
This thesis addresses the problem of real-time epileptic seizure detection from intracranial EEG (IEEG). One difficulty in creating an approach that can be used for many patients is the heterogeneity of seizure IEEG patterns across different patients and even within a patient. In addition, simultaneously maximizing sensitivity and minimizing latency and false detection rates has been challenging as these are competing objectives. Automated machine learning systems provide a mechanism for dealing with these hurdles. Here we present and evaluate an algorithm for real-time seizure onset detection from IEEG using a machine-learning approach that permits a patient-specific solution. We extract temporal and spectral features across all intracranial EEG channels. A pattern recognition component is trained using these feature vectors and tested against unseen continuous data from the same patient. When tested on more than 875 hours of IEEG data from 10 patients, the algorithm detected 97% of 67 test seizures of several types with a median detection delay of 5 seconds and a median false alarm rate of 0.6 false alarms per 24-hour period. The sensitivity was 100% for 8 out of 10 patients. These results indicate that a sensitive, specific and relatively short-latency detection system based on machine learning can be employed for seizure detection tailored to individual patients. In addition, we describe and evaluate an algorithm for the detection of the cessation of seizure activity within IEEG. Seizure end detection algorithms can enable important clinical applications such as the delivery of therapy to ameliorate post-ictal symptoms, the detection of status epilepticus, and the estimation of seizure duration. Our machine-learning-based approach is patient-specific. The algorithm is designed to search for the termination of electrographic seizure activity once a seizure has been discovered by a seizure onset detector. When tested on 65 seizures, 88% of all seizure ends were detected within 15 seconds of the time determined by a clinical expert to represent the electrographic end of a seizure. We explore the effects of channel pre-selection on seizure onset detection. We evaluate and present the results from a seizure detector that has been restricted to use only a small subset of the channels available. These channels are manually chosen to be those that show the earliest ictal activity. The results indicate that performance can suffer in many cases when the algorithm uses a small set of selected channels, often in the form of an increase in false alarm rate. This suggests that the inclusion of a full channel set allows the system to leverage information that is not readily apparent to a clinical reader (from regions seemingly not involved in the onset) to better differentiate ictal and inter-ictal patterns. Finally, we present and evaluate an algorithm for patient-specific feature extraction, where the feature extraction process for a given patient leverages the training data available for that patient. The results from an evaluation of a detector that supplemented the original spectral energy features with features computed in a patient-specific manner show a significant improvement in 3 out of 5 patients. The results suggest that this is a promising avenue for further improvement in the performance of the seizure onset detector.
by Alaa Amin Kharbouch.
Ph.D.
Grippe, Edward, et Mattias Lönnerberg. « Detecting Epileptic Seizures : Optimal Feature Extraction from EEG for Support Vector Machines ». Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166702.
Texte intégralSayeed, Md Abu. « Epileptic Seizure Detection and Control in the Internet of Medical Things (IoMT) Framework ». Thesis, University of North Texas, 2020. https://digital.library.unt.edu/ark:/67531/metadc1703334/.
Texte intégralSaulnier-Comte, Guillaume. « A machine learning toolbox for the development of personalized epileptic seizure detection algorithms ». Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=119550.
Texte intégralL'épilepsie est un trouble neurologique cérébral chronique qui touche environ 50 millions de personnes dans le monde. Cette maladie est caractérisée par la présence de crises d'épilepsie; un événement clinique transitoire causé par une activité cérébrale synchronisée et/ou anormale et excessive. Cette thèse présente un nouvel outil, utilisant des techniques d'apprentissage automatique, capable de générer des algorithmes personnalisés pour la détection de crises épileptiques qui exploitent l'information contenue dans les enregistrements électroencéphalographiques. Une grande variété de caractéristiques conçues pour la recherche en détection/prédiction de crises ont été implémentées. Ce large éventail d'information est adapté à chaque patient grâce à l'utilisation de techniques de sélection de caractéristiques automatisées. Par la suite, l'information découlant de cette procédure est utilisée par un modèle de décision complexe, qui peut détecter les crises en temps réel. La performance des algorithmes est évaluée en utilisant une validation croisée sur des sujets présents dans trois ensembles de données accessibles au public. Nous observons des résultats dignes de l'état de l'art: des taux de détections allant de 76% à 86% avec des taux de faux positifs médians en deçà de 2 par jour. L'outil ainsi qu'un nouvel ensemble de données sont rendus publics afin d'améliorer les connaissances sur la maladie et réduire la surcharge de travail causée par la création d'algorithmes dérivés.
Orellana, Marco Antônio Pinto. « Seizure detection in electroencephalograms using data mining and signal processing ». Universidade Federal de Viçosa, 2017. http://www.locus.ufv.br/handle/123456789/11589.
Texte intégralMade available in DSpace on 2017-08-22T13:26:59Z (GMT). No. of bitstreams: 1 texto completo.pdf: 5760621 bytes, checksum: f90e38633fae140744262e882dc7ae5d (MD5) Previous issue date: 2017-03-10
Agencia Boliviana Espacial
A epilepsia é uma das doenças neurológicas mais comuns definida como a predisposição a sofrer convulsões não provocadas. A Organização Mundial da Saúde estima que 50 milhões de pessoas estão sofrendo esta condição no mundo inteiro. O diagnóstico de epilepsia implica em um processo caro e longo baseado na opinião de especialistas com base em eletroencefalogramas (EEGs) e gravações de vídeo. Neste trabalho, foram desenvolvidos dois métodos para a predição automática de convulsões usando EEG e mineração de dados. O primeiro sistema desenvolvido é um método específico para cada paciente (patient-specific) que consiste em extrair características espectro-temporais de todos os canais de EEG, aplicar um algoritmo de redução de dimensão, recuperar o envelope do sinal e criar um modelo usando um classificador random forest. Testando este sistema com um grande banco de dados de epilepsia, atingimos 97% de especificidade e 99% de sensibilidade. Assim, a primeira proposta mostrou ter um grande potencial para colaborar com o diagnóstico em um contexto clínico. O segundo sistema desenvolvido é um método não específico do paciente (non-patient specific) que consiste em selecionar o sinal diferencial de dois eletrodos, aplicar um vetor de bancos de filtros para esse sinal, extrair atributos de séries temporais e criar um modelo preditivo usando uma árvore de decisão CART. O desempenho deste método foi de 95% de especificidade e 87% de sensibilidade. Estes valores não são tão altos quanto os de métodos propostos anteriormente. No entanto, a abordagem que propomos apresenta uma viabilidade muito maior para implementação em dispositivos que possam ser efetivamente utilizados por pacientes em larga escala. Isto porque somente dois elétrodos são utilizados e o modelo de predição é computacionalmente leve. Note-se que, ainda assim, o modelo xigerado apresenta um poder preditivo satisfatório e generaliza melhor que em trabalhos anteriores já que pode ser treinado com dados de um conjunto de pacientes e utilizado em pacientes distintos (non-patient specific). Ambas as propostas apresentadas aqui, utilizando abordagens distintas, demonstram ser alternativas de predição de convulsões com performances bastante satisfatórias sob diferentes circunstâncias e requisitos.
Epilepsy is one of the most common neurological diseases and is defined as the pre- disposition to suffer unprovoked seizures. The World Health Organization estimates that 50 million people are suffering this condition worldwide. Epilepsy diagnosis im- plies an expensive and long process based on the opinion of specialist personnel about electroencephalograms (EEGs) and video recordings. We have developed two meth- ods for automatic seizure detection using EEG and data mining. The first system is a patient-specific method that consists of extracting spectro-temporal features of 23 EEG channels, applying a dimension reduction algorithm, recovering the envelope of the signal, and creating a model using a random forest classifier. Testing this system against a large dataset, we reached 97% of specificity and 99% of sensitivity. Thus, our first proposal showed to have a great potential for diagnosis support in clinical context. The other developed system is a non-patient specific method that consists of selecting the differential signal of two electrodes, applying an array of filter banks to that signal, extracting time series features, and creating a predictive model using a decision tree. The performance of this method was 95% of specificity, and 87% of sensitivity. Although the performance is lower than previous propos- als, due to the design conditions and characteristics, our method allows an easier implementation with low hardware requirements. Both proposals presented here, using distinct approaches, demonstrate to be seizure prediction alternatives with very satisfactory performances under different circumstances and requirements.
Cimbálník, Jan. « Detekce vysokofrekvenční EEG aktivity u epileptických pacientů ». Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-255294.
Texte intégral« Detection, Prediction and Control of Epileptic Seizures ». Doctoral diss., 2016. http://hdl.handle.net/2286/R.I.40744.
Texte intégralDissertation/Thesis
Doctoral Dissertation Electrical Engineering 2016
Ramos, Mariana Ferreira. « Characterization and detection of epileptic seizures based on actigraphy data ». Master's thesis, 2016. http://hdl.handle.net/10316/97182.
Texte intégralEpilepsy is a severe medical condition affecting millions of people in the world. Detection and prediction of epileptic events is an open problem and an active field of research in the medical and neuroscience communities. Patients with epilepsy are monitored at the service of Neurology from the Hospital de Santa Maria in Lisbon for long periods where huge amount of data, such as EEG, ECG and video, is collected in order to capture the appropriated number of epileptic episodes needed to characterize the disease. These episodes, impossible to predict, are random and sparse in time which makes the task of analysis difficult and time consuming. In this thesis an actigraphy device (an accelerometer) was designed and assembled to be plugged into the amplifier used in the hospital to collected the EEG signals. This way the activity of the patient on his non-dominant wrist (usually the left one) is recorded and stored simultaneously with the other signals acquired during the exam avoiding to perform the usual complex procedure of signal alignment and sampling frequency adjustment. Algorithms for detection of specific movement patterns were designed in order to automatically detect epileptic seizures with associated movement disturbances. The goal is to perform an automatic annotation of the data collected during exam and help the technical staff in its analysis. In the future these algorithms will be used in ambulatory systems to identify and record epileptic seizures in normal life conditions of the patients. Two prototypes were produced and tested at the hospital with real patients and the algorithms for movement detection and epileptic seizure identification were designed, implemented and tested using synthetic and real data. The EEG data from the patients were annotated and classified manually by the technicians where all the paroxysmal events were identified and used for training and testing as ground truth information. A total of 62 events from 4 patients, checked by the medical doctor, where used to train the detector and validate the algorithm. An accuracy of more than 98% was achieved in detecting movements and among them more than 84% were correctly classified as epileptic seizures. The two prototypes are installed at the hospital where more data are being collected.
A epilepsia é uma condição médica grave que afeta milhões de pessoas no mundo. A detecção e previsão de crises epilépticas é um problema em aberto e um campo ativo de pesquisa nas comunidades médica e de neurociência. Os pacientes com epilepsia são monitorizados no serviço de Neurologia do Hospital Santa Maria em Lisboa durante longos períodos, durante o qual uma grande quantidade de dados, tais como EEG, ECG e vídeo, é recolhida a fim de detectar o número necessário de crises epilépticas necessário para caracterizar a doença. Estes episódios, impossíveis de prever, são aleatórios e dispersos no tempo o que torna a tarefa de análise difícil e demorada. Nesta tese foi projetado e montado um dispositivo de actigrafia (um acelerómetro) para ser conectado ao amplificador usado no hospital para adquirir os sinais de EEG. Desta forma, a actividade motora do paciente é gravada e armazenada simultaneamente com os outros sinais adquiridos durante o exame evitando a realização do procedimento habitual e complexo de alinhamento de sinal e ajuste da frequência de amostragem, usando o dispositivo no pulso não-dominante (usualmente a esquerdo). Dois algoritmos para detecção de padrões de movimento específicos foram concebidos a fim de detectar automaticamente crises epilépticas com distúrbios de movimento associados. O objetivo é realizar uma anotação automática dos dados recolhidos durante o exame e ajudar os técnicos na análise. Futuramente, estes algoritmos iram ser utilizados em sistemas ambulatórios para identificar e registar as crises epilépticas em condições normais de vida dos pacientes. Dois protótipos foram produzidos e testados no hospital com pacientes reais e os algoritmos de detecção de movimento e identificação de crise epiléptica foram concebidos, implementados e testados utilizando dados sintéticos e reais. Os dados de EEG adquiridos nos pacientes foram anotados e classificados manualmente pelos técnicos nos quais foram identificados e utilizados para treino e testes todos os eventos paroxísticos. Um total de 62 eventos adquiridos a partir de 4 pacientes, verificados pelo médico, foram utilizados para treinar o detector e validar o algoritmo. Uma precisão de superior a 98% foi atingida na detecção de movimentos e entre eles mais de 84% foram corretamente classificados como crises epilépticas. Os dois protótipos estão instalados no hospital onde mais dados estão a ser adquiridos.
Πίππα, Ευαγγελία. « Εξόρυξη χωροχρονικών δεδομένων από τον ανθρώπινο εγκέφαλο και εφαρμογές στην ανίχνευση των επιληπτικών κρίσεων ». Thesis, 2013. http://hdl.handle.net/10889/6385.
Texte intégralThe subject of this work is the research of analysis techniques on data coming from neuroimaging systems such as Electroencephalogram. The aim of the data analysis techniques is the detection of specific morphologies of these signals such as the epileptic seizures. A seizure is a sudden breakdown of the neuronal activity of the brain that is clinically manifested by an involuntary alteration in behavior, movement, sensation, or consciousness. These clinical behaviors are preceded and then accompanied by electroencephalographic alterations. The automatic detection of epileptic seizures can be faced as a classification problem of the signals into seizures or non seizures. The detection can be carried out in two steps. Firstly, features which capture the morphology of the epileptic seizures are extracted and then the feature vector is given to an appropriately trained classifier.
Dorai, Arvind. « Automated Epileptic Seizure Onset Detection ». Thesis, 2009. http://hdl.handle.net/10012/4342.
Texte intégral« Brain Dynamics Based Automated Epileptic Seizure Detection ». Master's thesis, 2012. http://hdl.handle.net/2286/R.I.14947.
Texte intégralDissertation/Thesis
M.S. Electrical Engineering 2012
Istiqomah et 伊思緹. « Development of Real-time Epileptic Seizure Detection Applications ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/qh232c.
Texte intégral國立交通大學
電機資訊國際學程
107
Abstract Around 65 million people have been diagnosed with epilepsy. There are many ways offered for the treatment of epilepsy. The first treatment that doctor offer is Antiepileptic drugs. Antiepileptic drugs provide seizure control that is satisfying for most epilepsy patients but there are still 35 % of patient has uncontrolled epilepsy. Surgery could become the other option to remove the epileptogenic zone. However, the patients who did surgery still have to consume medicine and suffer seizures occasionally. These treatments still cause occasional seizures in patients, which affects the patient's quality of life and leads to accidents that may pose a danger to the patient and the people around them. From that intention to handle if the patient still has a seizure, this thesis develops epileptic seizure applications which process data from Epileptic Seizure Detection Tag (ESDT). These applications consist of two part: 1) ESDT APP; 2) Cloud service ESDT. The ESDT APP has features to send alert message and can record a daily record and epileptic seizure record which saves in the cloud. The alert system has a verification procedure to handle a false seizure before send SMS alert. As a complementary system, Cloud service ESDT has function to represent data from the cloud. Those applications record two kinds of data is daily EEG raw data and 30-second pre-ictal and ictal seizure signal which important for epileptic seizure analysis and improvement epileptic seizure detection algorithm. The verification data in applications is done by comparing pattern signal shape between data is received from ESDT which display on ESDT APP and data in the cloud which shows in the Cloud service ESDT. Data which is sent from on ESDT APP into the cloud can help patient record their seizure data and it is shown in Cloud service ESDT which help doctors for diagnosis or research. Keywords: Epileptic Seizure Application, Cloud, Web Application
Chiang, Tzu-Chun, et 江子群. « Power Optimization of Epileptic Seizure Detector by Epileptic Channel Prediction ». Thesis, 2018. http://ndltd.ncl.edu.tw/handle/m465sn.
Texte intégral國立交通大學
生醫工程研究所
107
Epileptic seizure control is always a popular issue. As time progresses, medication treatment and surgery are not the only way to control the symptom. Recently, there are there are much research about multi-channel seizure detection about multi-channel seizure detection. In order to get higher accuracy and stability, we require more number of channels. Increasing process for the channel may cause a heavy load of power consumption in the system. This thesis mentions that how to use the different channel characteristics to adjust the detecting time. The research extracts two kinds of features to be the based points of controlling detection. One is the frequency band power and the other one is the position of channel. The FFT calculates three bands of frequency power is the principal feature for predicting. Then each channel activity is classified by SVM model. Finally, the predictor decreases the detecting positions which includes numerous non-active channels. It protects from unwanted calculations which tends to decrease the power consumption efficiently. We build the new model for predicting. After simulation, the model can certainly decrease up to 45% of calculations and the seizure detecting still stay in high accuracy. In the future, the number of channels may continuously increase, and then the prediction system can bring more benefit.
Maia, Paulo Manuel de Carvalho Branco. « NeuroMov : Multimodal approach for epileptic seizure detection and prediction ». Master's thesis, 2019. https://hdl.handle.net/10216/122327.
Texte intégralMaia, Paulo Manuel de Carvalho Branco. « NeuroMov : Multimodal approach for epileptic seizure detection and prediction ». Dissertação, 2019. https://hdl.handle.net/10216/122327.
Texte intégralChen, Wei-Hung, et 陳威宏. « Design and Implementation of a 16-Channel Epileptic Seizure Detection Chip ». Thesis, 2015. http://ndltd.ncl.edu.tw/handle/35487372912226415876.
Texte intégral國立交通大學
電子工程學系 電子研究所
104
Epileptic seizure control is a popular issue in recent years due to 30% of the epileptic patients remain drug-resistant and only some patients are suitable for resection surgery. The multi-channel seizure detection is also an important issue. The multi-channel detection can have more chances to cover the seizure onset zone so that the seizure can be suppressed efficiently. To achieve the better seizure control efficiency, the accuracy and the latency are necessary to reach certain levels (Accuracy > 90%, Latency < 5 s). In this thesis, a seizure detection algorithm with the training process and the simulation result is presented. The detection latency is 2.25s. For the data set, the sampling rate is 1024 Hz, 512 Hz or 256 Hz. However, in order to reduce the hardware complexity, the window length and the downsample issue are also simulated. The simulation result shows that with 1 s window and 128 Hz sampling rate, the accuracy can be up to 97.76%. A DSP processor for the 16-channel seizure detection has been designed and implemented. There are two main feature extraction circuits: 128-point approximate entropy and 128-point fast Fourier transform. The entropy block occupies 0.17mm2 while the FFT block occupies 0.58mm2, and the area of the DSP processor is 1.74mm2 in TSMC 0.18-um process. The operating frequency of the processor is 6.758 MHz and the power is 5.5 mW. To achieve better accuracy, the more complex algorithms are employed such as neural network (NN) and support vector machine (SVM). The simulation result shows that the multi-layer neural network can achieve the accuracy of 98.96% and the SVM is 99.25% so that the algorithms can provide a reliable detection results.
Han-YenChang et 張涵彥. « Combining ICA with Wavelet Transformation on Grouped EEG for Epileptic Seizure Detection ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/23888851512997709542.
Texte intégral國立成功大學
電腦與通信工程研究所
98
In this thesis, we propose a new scheme which combines ICA with wavelet transformation on grouped EEG signals for epileptic seizure detection. The Independent Component Analysis (ICA) is adopted to enhance epileptic seizure. Then, wavelet transformation is followed with a dynamic threshold for identifying the epileptic seizure location. A series of experiments has been conducted to evaluate the proposed approach. The experimental results show that the proposed method has a superior performance than other approaches.
Shen, Chia-Ping, et 沈家平. « Cloud-based Epileptic Seizure Detection System Using a Multi-Channel EEG Classification ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/78554144077690225154.
Texte intégral國立臺灣大學
生醫電子與資訊學研究所
102
Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. The Electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. In addition, multi-channel EEG signals have much more discrimination information than a single channel. However, traditional recognition algorithms of EEG signals are lack of multi-channel EEG signals. Due to large data computation, we propose a cloud based Epilepsy Analysis System (EAS) on multi-channel EEG signals. Both unipolar and bipolar EEG and ECG signals are both considered in our approach. We make use of approximate entropy (ApEn) and statistic values to extract features cascaded Genetic Algorithm (GA). Furthermore, EEG was also tested the performance by Support Vector Machine (SVM) and post-spike matching filters. We obtained accuracies of spikes and seizures are 86.69% and 99.77% for Clinical Data Set II. The detection system was further validated using the model trained by Clinical Data Set II on Clinical Data Set III. The system again showed high performance, with accuracies of spikes and seizures are 91.18% and 99.22%. Therefore, we built up a reliable, real-time, and complete (medical information and signal processing technology) system for detecting a large variety of seizures and spikes from multi-channel EEG data.
Liu, Shih-Ting, et 劉時廷. « Epileptic Seizure Detection System Using Multi-Channel EEG as Basis for Classification ». Thesis, 2012. http://ndltd.ncl.edu.tw/handle/33424262002891951141.
Texte intégral國立臺灣大學
生醫電子與資訊學研究所
100
Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Seizure episodes can cause temporal paralysis of the body, which can lead to severe injuries. Electroencephalogram (EEG) is a tool commonly used for analyzing brain activity and diagnosing brain disorders. EEG can be presented under different montage schemes. This study focuses on two of the montage schemes; unipolar montage and bipolar montage. Traditionally, the most commonly used montage for automated EEG analysis is unipolar. We experiment with incorporating bipolar EEG montage for creating a classification system to classify different epileptic wave forms. A series of functions were designed for bipolar EEG montage. We used wavelet transform (WT) to decompose EEG signal into its primary sub-bands. We use Approximate Entropy and Total Variation as features designed specifically for spike and seizure detection. We used Genetic Algorithm and Fisher Score to rank and selected most influential features for classifier. Finally we use multi-class Support Vector Machine as our classifier.
Lin, Shih-Kai, et 林詩凱. « An Ultra Low Power Smart Headband for Real-time Epileptic Seizure Detection ». Thesis, 2018. http://ndltd.ncl.edu.tw/handle/ksgh3v.
Texte intégral國立交通大學
電機工程學系
106
Epilepsy is one of the most common neurological disorders; about 65 million people in the world are affected. A considerable portion of epilepsy cannot be well controlled by today’s available treatments. Even patients who have resection surgery to remove the epileptogenic zone will still suffer seizures once in a while. In this thesis, the design of a smart headband for epileptic seizure detection is presented. The proposed headband consists of four key components: 1) an analog front-end circuitry, 2) an epileptic seizure detection tag (ESDT), 3) a Bluetooth Low Power (BLE) chip, and 4) customized electrodes. All the above components are integrated into a fabric headband with only 50.3 g. The current consumption of the smart headband system is 16.35 mA. The epileptic seizure detection algorithm inside ESDT is validated by using Boston Children’s Hospital’s CHB-MIT scalp EEG clinical database with the detection rate of 92.68% and the false alarm of 0.527/hour. We develop a service APP connected to the cloud so that the patients’ health condition can be recorded and then referenced by doctors for further diagnosis or research. By event record, doctor only needs to review 1 % EEG recordings for precise diagnosis.
Wang, Hsu-chuan, et 王敘全. « Combination of EEG Spectrum and Complexity Analysis for Robust Online Epileptic Seizure Detection ». Thesis, 2008. http://ndltd.ncl.edu.tw/handle/28980828820306405178.
Texte intégral國立成功大學
資訊工程學系碩博士班
96
Epilepsy is one of the most common neurological disorders, approximately 1% of people in the world have epilepsy, 25% of epilepsy patients cannot be treated sufficiently by any available therapy. Epilepsy is caused by abnormal discharges in the brain, thus EEG has been an especially valuable clinical tool for the evaluation, detection, and treatment of epilepsy. Through EEG recordings, a number of systems which can release drug or give an electrical stimulation to suppress the seizures have been developed and under clinical operation for years. However, a robust device has not yet been developed which compute quickly and fast enough to action to meet immediately pathological changes of different types of seizures in human. In this paper, we propose a fast and reliable epilepsy detection method based on the complexity analysis and spectrum analysis. We propose complexity measure ApC and combine it with selected frequency bands power as the features for detecting seizures. An early seizure detection method is also presented which can detect seizures in a short time while seizures onset. Three different types of seizures are used for testing the detection performance. By the experiment result, the proposed epilepsy detection method can detect seizures in accuracy above 95% with a short detection delay 0.36-0.69 sec.
Bandarabadi, Mojtaba. « Low-complexity measures for epileptic seizure prediction and early detection based on classification ». Doctoral thesis, 2015. http://hdl.handle.net/10316/27608.
Texte intégralThis thesis concerns the problems of epileptic seizure prediction and detection. We analyzed multichannel intracranial electroencephalogram (iEEG) and surface electroencephalogram (sEEG) recordings of patients suffering from refractory epilepsy, to access the brain state in real time by using relevant EEG features and computational intelligence techniques, and aiming for detection of pre-seizure state (in the case of prediction) or seizure onset times (in the case of detection). Our main original contribution is the development of a novel relative bivariate spectral power feature to track gradual transient changes prior to ictal events for real-time seizure prediction. Furthermore a novel robust and generalized measure for early seizure detection is developed, aimed to be used in closed-loop neurostimulation systems. The development of a general platform embeddable on a transportable low-power-budget device is of utmost importance, for real time warning to patients and their relatives about the impending seizure or beginning of an occurring seizure. The portable device can also be integrated to work in conjunction with a closed-loop neurostimulation or fast-acting drug injection mechanism to eventually disarm the impending seizure or to suppress the just-occurring seizure. Therefore, in this thesis we try to meet the dual-objective of developing algorithms for seizure prediction and early seizure detection that provide high sensitivity and low number of false alarms, fulfilling the requirements of clinical applications, while being low computational cost. To seek the first objective, a patient-specific seizure prediction was developed based on the extraction of novel relative bivariate spectral power features, which were then preprocessed, dimensionally reduced, and classified using a machine-learning algorithm. The introduced feature bears low complexity, and was discriminated using the powerful support vector machine (SVM) classifier. We analyzed the preictal EEG dynamics across different brain regions and throughout several frequency bands, using relative bivariate features to uncover the underlying mechanisms ending in epileptic seizures. The suggested prediction system was evaluated on long-term continuous sEEG and iEEG recordings of 24 patients, and produced statistically significant results with average sensitivity of 75.8% and false prediction rate of 0.1 per hour. Furthermore a novel statistical method was developed for proper selection of preictal period, and also for the evaluation of predictive capability of features, as well as for the predictability of seizures. The method uses amplitude distribution histograms (ADHs) of the features extracted from the preictal and interictal iEEG and sEEG recordings, and then calculates a criterion of discriminability among two classes. The method was evaluated on spectral power features extracted from monopolar and bipolar iEEG and sEEG recordings of 18 patients, in overall consisting of 94 epileptic seizures. To approach the objective of early seizure detection, we have formulated power spectral density (PSD) of bipolar EEG signal in the form of a measure of neuronal potential similarity (NPS) between two EEG signals. This measure encompasses the phase and amplitude similarities of two EEG channels in a simultaneous fashion. The NPS measure was then studied in several narrow frequency bands to find out the most relevant sub-bands involved in seizure initiations, and the best performing ratio of two NPS measures for seizure onset detection was determined. Evaluating on long-term continuous iEEG recordings of 11 patients with refractory partial epilepsy (overall of 1785 h and 183 seizures) the results showed high performance, while requiring a very low computational cost. On average, we could achieve a sensitivity of 86.3%, a low false detection rate (FDR) of 0.048/h, and a mean detection latency of 14.2s from electrographic seizure onsets, while in average preceding clinical onsets by 1.1s. Apart from the above mentioned primary objectives, we introduced two new and robust methods for offline or real-time labelling of epileptic seizures in long-term continuous EEG recordings for further studies. Methods include mean phase coherence estimated from bandpass filtered iEEG signals in specific frequency bands, and singular value decomposition (SVD) of bipolar iEEG signals. Both methods were evaluated on the same dataset employed in the previous study and demonstrated sensitivity of 84.2% and FDR of 0.09/h for sub-band mean phase coherence, and sensitivity of 84.1% and FDR of 0.05/h for bipolar SVD, on average. Most of this work was established in collaboration with the EPILEPSIAE project, aimed to predict of pharmacoresistant epileptic seizures. The developed methods in this thesis were evaluated by the accessibility of long-term continuous multichannel EEG recordings of more than 275 patients with refractory epilepsy, referred to as The European Epilepsy Database. This database was collected by the three clinical centers involved in EPILEPSIAE, and contains well-documented metadata. The results of this thesis are backing the hypothesis of the predictability of most of epileptic seizures using linear bivariate spectral-temporal brain dynamics. Moreover, the promising results of early seizure detection sustain the feasibility of integrating the proposed method with closed-loop neurostimulation systems. We hope the developed methods could be a step forward towards the clinical applications of seizure prediction and onset detection algorithms.
Esta tese versa os problemas de predição e de deteção de crises epiléticas. Analisa-se o eletroencefalograma multicanal intracraniano (iEEG) e de superfície (sEEG) de pacientes que sofrem de epilepsia refratária, para a estimação em tempo real do estado cerebral, usando características relevantes do EEG e técnicas de inteligência computacional, ambicionando a deteção do estado pré-ictal (no caso de previsão) ou dos instantes de início de uma crise (no caso de deteção). A principal contribuição original é o desenvolvimento de uma característica de potência espectral bivariada relativa para captar as mudanças transitórias graduais que levam a crises e que poderão ser usadas para previsão em tempo real. Além disso, é desenvolvida uma nova medida, robusta e generalizada para a deteção precoce, destinada a ser utilizada em sistemas de neuro estimulação em malha fechada. O desenvolvimento de uma plataforma geral possível de ser integrada num dispositivo transportável, energeticamente económico, é de grande relevância para o aviso em tempo real do doente e dos seus próximos sobre a eminência da ocorrência de uma crise. O dispositivo transportável também pode ser usado em malha fechada com um neuro estimulador ou com um dispositivo de injeção rápida de um fármaco que desarme eventualmente a crise em curso. Por isso nesta tese persegue-se o objectivo de desenvolver algoritmos para previsão mas também para deteção de crises. Em ambos os casos, pretende-se que os algoritmos tenham uma elevada sensibilidade e uma baixa taxa de falsos positivos, tornando viável a sua utilização clínica. Para o objectivo de previsão, desenvolveu-se um método de previsão personalizado baseado na extração de uma característica nova, denominada de potência relativa espectral bivariada, que foi submetida a pre-processamento, redução de dimensão e classificação com Máquinas de Vetores de Suporte (SVM). Esta nova característica, de baixa complexidade, é computacionalmente simples, mas permite a análise da dinâmica do EEG preictal em diferentes regiões do cérebro e ao longo de várias bandas de frequência, de modo a descobrir os mecanismos subjacentes às crises epiléticas. O sistema de previsão obtido foi avaliado em registos contínuos de sEEG e iEEG de 24 pacientes, e produziu resultados estatisticamente significativos com sensibilidade média de 75.8% e taxa de predição falsa de 0.1 por hora. Além disso, foi desenvolvido um novo método estatístico para a seleção apropriada do período preictal, e também para a avaliação da capacidade preditiva das características, assim como para a própria previsibilidade das crises. O método utiliza os histogramas de distribuição de amplitude (ADHS) das características extraídas nos períodos pré-ictal e ictal dos registos de iEEG e sEEG e, em seguida, calcula um critério de discriminabilidade entre as duas classes. O método foi avaliado nas características de potencia espectral extraídas de registos iEEG e sEEG, monopolares e bipolares de 18 pacientes, consistindo num número total de crises epilépticas de 94. O segundo objetivo, a deteção precoce de crises, foi abordado através da formulação da densidade de potência espectral (PSD) de canais de EEG bipolares na forma de uma medida da similaridade do potencial neuronal (NPS) entre dois sinais de EEG. Esta medida usa as similaridades entre as fases e as amplitudes de dois canais de EEG de um modo simultâneo. A medida NPS foi estudada em várias bandas estreitas de frequência de modo a descobrir-se quais as sub-bandas mais envolvidas na inicialização das crises; buscou-se assim a melhor razão entre duas NPS do ponto de vista da deteção precoce. Avaliadas em iEEG contínuos de longa duração de 11 doentes com epilepsia refratária parcial (num total de 1785 h e 183 crises), os resultados apresentam um desempenho com sensibilidade de 86.3% e taxa de deteção falsa (FDR) de 0.048/h, uma latência de 14.2s em relação ao início eletrográfico, sendo uma crise detetada em média 1.1s antes da sua manifestação clínica. Para além dos objetivos principais referidos acima, introduziram-se dois novos métodos, robustos, para etiquetagem em diferido e em tempo real das crises em registos contínuos de EEG de longa duração para estudos posteriores. Esses métodos incluem a coerência de fase média (mean phase coherence) estimada a partir de registos iEEG em bandas de frequência específicas (usando filtros passa-banda), e a decomposição em valores singulares (SVD) de sinais iEEG bipolares. Ambos os métodos foram avaliados no mesmo conjunto de dados do estudo anterior e apresentaram, em média, uma sensibilidade de 84.2% e um FDR de 0.09/h para a coerência de fase média calculada para as sub-bandas, e sensibilidade de 84.1% e FDR de 0.05/h para a metodologia que usa a decomposição SVD bipolar. Grande parte deste trabalho foi feito no âmbito do projeto EPILEPSIAE, visando a previsão de crises em doentes epiléticos fármaco-resistentes. Os métodos desenvolvidos nesta tese aproveitaram a acessibilidade aos dados bem documentados de mais de 275 pacientes que constituem a Base de Dados Europeia de Epilepsia (European Epilepsy Database), provenientes dos três centros hospitalares participantes no projeto. Os resultados desta tese apoiam a hipótese da previsibilidade da maioria das crises epiléticas usando dinâmicas cerebrais bivariadas lineares espetrais e temporais. Além disso os resultados são promissores relativamente à deteção precoce de crises e sustentam a fazibilidade da integração desses métodos com técnicas de neuroestimulação em malha fechada. Esperamos que os métodos desenvolvidos resultem num avanço no que respeita à aplicação clínica de algoritmos de previsão e deteção de crises.
FCT - SFRH/BD/71497/2010
Qian, Chengliang. « Low-Power Low-Noise CMOS Analog and Mixed-Signal Design towards Epileptic Seizure Detection ». Thesis, 2013. http://hdl.handle.net/1969.1/149508.
Texte intégralChang, Wan-lin, et 張琬琳. « Multi-type Epilepsy Diagnosis and Automatic Epileptic Seizure Detection Based on Recurrent Neural Networks ». Thesis, 2009. http://ndltd.ncl.edu.tw/handle/50252313988406642546.
Texte intégral國立成功大學
資訊工程學系碩博士班
97
Epilepsy is one of the most common neurological disorders, approximately 1% of people in the world have epilepsy, 25% of epilepsy patients cannot be treated sufficiently by any available therapy. Epilepsy is caused by abnormal discharges in the brain, thus EEG has been an especially valuable clinical tool for the evaluation, detection, and treatment of epilepsy. Through EEG recordings, a number of systems which can release drug or give an electrical stimulation to suppress the seizures have been developed and under clinical operation for years. However, a robust device has not yet been developed which compute quickly and fast enough to action to meet immediately pathological changes of different types of seizures in human. In this study, we research multi-type epilepsy diagnosis that can be applied to control the multi-type epilepsy with different method. We present a three-type epileptic diagnosis method, with using permutation entropy as the complexity index and spectrum band power with RBFSVM classify. The average accuracies of the RBFSVM reach to 79%. The detection rates of temporal EEGs can reach 97.6%. However, if we distinguish temporal EEGs (Set C) and non-temporal EEGs (Set A and B). The average accuracy can reach higher than 97% with RBFSVM. The classification results can be utilized to a system to determine what type of epilepsy when patients have mixed epilepsy. In the on-line detection, the results of the feasibility of developing algorithms to detect seizures based on automated analysis of the spatiotemporal dynamical characteristics of EEG recordings. We present a reliable epileptic seizures detection method, with using approximate entropy as the complexity index and spectrum band power. We present an adaptive threshold method that reduced false alarm. We also selecting recurrent neural network to be classifier which can detect seizures in a short time while seizures onset. The method has tested on three types of seizures including long-term recordings, robustly. This method was shown with several aspects of advantages, including high accuracy of on-line seizure detection (reach 100%), low false alarm (below 2.5%). The seizure detection latency was not greater than 0.5 sec after seizure onset.
Ho, Chia-Lun, et 何嘉倫. « Design and Implementation of Bio-Signal Processors for Closed-loop Epileptic Seizure Detector ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/11628325812446993946.
Texte intégral國立交通大學
電機工程學系
102
Epilepsy is one of the most common neurological disorders, by which around 1% of the people in the world are affected. Unfortunately, 30% of the epilepsy patients cannot be treated sufficiently by antiepileptic drugs. As a result, a suitable implantable and portable medical device is a solution for epilepsy seizure control. To address these issues, an ASIC-based seizure detector and a RISC-based seizure detector are proposed to realize real-time closed-loop seizure detection algorithm. The ASIC-based seizure detector with wireless signal transmission is implemented in 0.18μm CMOS process and verified by animal experiment. The RISC-based seizure detector with FIR filter is implemented in 90 nm CMOS process and dissipates lower power compared with ASIC-based SoC when multi-channel seizure detection is realized.
Shih, Yi-Hsin, et 施誼欣. « Design and Implementation of an Energy-Efficient Fast Independent Component Analysis Processor for Epileptic Seizure Detection ». Thesis, 2013. http://ndltd.ncl.edu.tw/handle/96325112812665069211.
Texte intégral國立交通大學
電信工程研究所
102
To improve the performance of epileptic seizure detection, independent component analysis (ICA) is applied to multi-channel signals to separate artifacts and signals of interest. FastICA is an efficient algorithm to compute ICA. To reduce the energy dissipation, eigenvalue decomposition (EVD) is utilized in the pre-processing stage to reduce the convergence time of iterative calculation of ICA components. EVD is computed efficiently through an array structure of processing elements running in parallel. Area-efficient EVD architecture is realized by leveraging the approximate Jacobi algorithm, leading to a 77.2% area reduction. By choosing proper memory element and reduced wordlength, the power and area of storage memory are reduced by 95.6% and 51.7%, respectively. The chip area is minimized through architectural transformations. Given the latency of 0.1s, an 86.5% area reduction is achieved compared to the direct-mapped architecture. Fabricated in 90nm CMOS, the core area of the chip is 0.40mm^2. The FastICA processor, part of an integrated epileptic control SoC, dissipates 81.6W at 0.32V. The computation delay of a frame of 256 samples for 8 channels is 84.2ms. Compared to prior work, 0.5% power dissipation, 26.7% silicon area, and 3.4 computation speedup are achieved. The performance of the chip was verified by human dataset.
Paiva, Carlos André Almeida. « Epileptic seizure detection and prediction based on spatiotemporal EEG data and deep machine learning (EPI-DEEP) ». Master's thesis, 2019. http://hdl.handle.net/10316/88118.
Texte intégralConhecida como uma das doenças neurológicas mais comuns, a epilepsia afeta 1% da população mundial. Para 30% dos pacientes diagnosticados com epilepsia, não há tratamento ou medicação viável para evitar a ocorrência de crises. Esse tipo de epilepsia é chamado de epilepsia resistente a medicamentos. Isso significa que integridade física desses pacients pode estar comprometida a qualquer momento, o que pode colocar suas vidas em risco. Para essas pessoas, todo esforço bem-sucedido em prever ou detectar crises epilépticas tem o potencial de melhorar significativamente suas vidas.Esse assunto tem sido explorado na literatura bastante extensivamente, principalmente quando se trata de analisar características temporais e dados extraídos de vários electrodos. No entanto, existem muito poucos estudos que se concentram também em explorar as relações espaciais entre electrodos. A nossa ideia é explorar o potencial das relações espaciais entre os electrodos, através de mapas de electrodos, a fim de criar um modelo que possa prever ou detectar crises, possivelmente com melhor desempenho do que as já existentes. Nesse sentido, este projecto de tese aplicará técnicas de aprendizagem computacional profunda ao problema de detecção ou previsão de crises epilépticas, usando mapas de eletrodos derivados de dados do eletroencefalograma (EEG).Nosso modelo final é um detector de crises realista, que deve produzir alertas para crises em tempo real, usando redes neurais convolucionais em dados brutos de EEG. Esta tese explora os vários desafios da construção de um modelo capaz de alta sensibilidade e detecção precoce de um sistema como esse.Esta tese foi conduzida usando dados de EEG incluídos na European Epilepsy Database (banco de dados EPILEPSIA).
Known as one of the most common neurological disorders, epilepsy affects 1% of the world’s population. For 30% of epilepsy diagnosed patients, there is no viable treatment or medication to prevent the occurrence of seizures. This type of epilepsy is called drug resistant epilepsy. This means that their physical integrity is compromised, which eventually may put their lives at risk. For these people, every successful effort in predicting or detecting seizure events has the potential to significantly improve their lives.This subject has been explored in literature quite extensively, particularly when it comes to analyzing temporal features and data retrieved from various electrodes. However, there aver very few studies that focus also on exploring the spatial relations between electrodes. Our idea is to explore the potential of spatial relations between electrodes, through electrode maps, in order to create a model that can predict or detect seizures, possibly with better performance than already existing ones. Towards that end, this thesis project will be applying deep learning techniques to the problem of detecting or predicting epileptic seizures, using electrode maps derived from electroencephalogram (EEG) data.Our final model is a realistic seizure detector that is expected to produce alerts for seizures in real time using convolutional neural networks on raw EEG data. This thesis explores the various challenges of building a model capable of high sensitivity and early detection for a system like this.This thesis was conducted using scalp EEG data comprised in the European Epilepsy Database (EPILEPSIA database).
Jeng, Chi, et 鄭錡. « Design and Implementation of a Low-power Multi-channel Closed-loop Epileptic Seizure Detector ». Thesis, 2011. http://ndltd.ncl.edu.tw/handle/71563141487643979297.
Texte intégral國立交通大學
電信工程研究所
100
Epilepsy is one of the most common neurological disorders, by which around 1% of the people in the world are affected. Unfortunately, 25% of the epilepsy patients cannot be treated sufficiently by antiepileptic drugs and epilepsy surgery. If seizures cannot be well controlled, the patients experience major limitations in their lives. In recent years, open-loop seizure controllers, such as vagus nerve and deep brain stimulation devices, have been proposed, but the effective rates of these devices are limited to 45%. In addition, low power and small hardware area are two important targets for implantable and portable devices. To overcome these issues, a real-time closed-loop seizure detection method is proposed. A multi-channel closed-loop epileptic seizure detector (MCESD) receives EEG signals of rats through ADC and delivers a stimulus at seizure. The seizure detection algorithm is realized by MCESD. The MCESD is implemented in a TSMC 0.18μm CMOS process. The seizure detection accuracy of device is above 94.6% from seizure detection algorithm with MCESD implementation, and the power of chip consumes 114.4μW.
Chen, Tsan-Jieh, et 陳燦杰. « The Design and Implementation of a Power-Efficient Bio-Signal Processing System-on-Chip for Epileptic Seizure Detection ». Thesis, 2012. http://ndltd.ncl.edu.tw/handle/stn794.
Texte intégral國立交通大學
電信工程研究所
101
In recent years, integrated circuits play an important role in today’s personal medical applications. These medical devices are usually battery-powered and their limited power budget imposes design challenges on signal acquisition and processing. Epileptic seizure control is one of the emerging applications. In this application, closed-loop neurostimulation is the most important method for seizure control devices. A real-time seizure detector is the kernel of a closed-loop seizure controller. In this dissertation, several low-power high-performance techniques from software to hardware level are applied for real-time power-efficient seizure detection. To demonstrate the proposed ideas, three works are designed and implemented. Long-Evans rats with spontaneous absence seizures are used as animal models for long-term continuous verification. In the first work, a bio-signal processor (BSP) core based on 32-bit reduced instruction set computer (RISC) architecture for seizure detection is implemented to achieve low-power consumption and continuous real-time processing. The proposed BSP core consists of 5-stage integer pipeline, 32×32 multiply-accumulator (MAC) unit, and a 32-bit tick timer. These features can enable high-performance signal processing and task scheduling for many biomedical applications. The floating-point seizure detection algorithm is approximated and rescheduled for short latency. The high-performance BSP core is implemented in 0.18 ?慆 complementary-metal-oxide semiconductor (CMOS) technology to verify functionality and capability. The measurement results show that the implemented processor can reduce over 90% power consumption compared with our previous prototype, which is implemented on an enhanced 8051 microcontroller. In the second work, a power-efficient BSP based on the first work is proposed to utilize for diverse physiological signals. Tens of kilobytes memory is embedded for efficient program execution in the proposed processor. The multi-mode analog-to-digital converter (ADC) is also integrated for physiological signals acquisition. Several serial and parallel ports are integrated with RISC processor for system expansion. Significant performance improvement is achieved through instruction optimization. Voltage and frequency scaling as well as clock gating are applied to reduce dynamic power on this work. The proposed BSP is implemented in 0.18 ?慆 CMOS technology. The measurement results show that the BSP consumes hundreds of microwatts to perform real-time seizure detection. The highly integrated and power-efficient BSP can be applied for excessive portable medical devices. The last work presents a power-efficient seizure detection system-on-chip (SoC). The FFT and entropy coding engines with direct memory access (DMA) feature are designed to reduce dynamic power through high-performance computation. The sample buffer and data control unit for signal acquisition is proposed to reduce context switching overhead. The seizure detection SoC is implemented in 0.18 ?慆 CMOS technology. The simulation results show that the implemented SoC consumes tens of microwatts to perform real-time seizure detection. The ultra-low power consumption of the proposed SoC enables implantable closed-loop seizure suppression in the future. Combining with efficient hardware architecture and software optimization, the real-time processing capability, design flexibility, portability, and versatility of the proposed platform and its design methodology can be applied on closed-loop seizure controller and many biomedical implants.
張舜婷. « Software-hardware Co-implementation for Real-time Epileptic Seizure Detection Using OpenRISC Processor Core on Absence Animal Models ». Thesis, 2011. http://ndltd.ncl.edu.tw/handle/98739836096579754995.
Texte intégral國立交通大學
電信工程研究所
100
Epilepsy is one of the most common neurological disorders. Approximately 1% of people in the world suffer from epilepsy, and 25% of epilepsy patients cannot be healed by today’s available treatments. In past years, open-loop seizure controllers have been proposed, such us vagus nerve stimulation and deep brain stimulation devices; however, the device drives a stimulator continuously or intermittently that causes high power consumption and the likelihood of neuronal damage. In contrast, the closed-loop implementation of hardware prototypes or biomedical signal processors has been proposed recently. Nevertheless, the average of seizure detection delay is either longer than 5 seconds or often not mentioned in these works, and it is insufficient to validate the robustness of detection algorithm. Moreover, most of studies often use the discontinuous electroencephalogram (EEG) signal fragments to validate seizure detection algorithm. As a result, a portable wireless online closed-loop seizure controller in freely moving rats was proposed, which validated seizure detection algorithm by using continuous online EEG signals. In this thesis, the fast parameter determination method, which determines a fitting model for each rat, is proposed to improve our previous work. The proposed parameter determination method is 416*10E6 times faster than our previous work, and it can attain the same detection accuracy (92-99%) and detection delay (0.63-0.79 s). Additionally, a low-power biomedical signal processor which bases on reduced instruction set computer (RISC) technology consumes only 6 mW for real-time epileptic seizure detection algorithm. Compared with our previous prototype, the measurement results show that the implemented processor can reduce 93.8% power consumption. The developed seizure detector can be applied to monitor the online EEG signals and integrate with analog front-end circuitries and an electrical stimulator to perform a closed-loop seizure controller in the future.
Branco, Maria José Mateus. « Aplicação Computacional para o processamento e análise de sinais de O2 recolhidos de modelos animais de epilepsia ». Master's thesis, 2018. http://hdl.handle.net/10316/86220.
Texte intégralEpilepsy is a non-communicable brain disease that affects approximately 50 million people of all ages worldwide. It is estimated that about 2.4 million people are diagnosed with epilepsy per year. As a way to improve their lives, it is important to understand the changes occurred during and especially before crises to enable their correct prediction.The purpose of this study was the development of a computational application able to analyze and process electrochemical signals recorded in vivo using platinum microelectrodes arrays microfabricated in ceramic support chronically implanted into the brain of rats treated with pilocarpine as temporal lobe epilepsy model and displaying suitable electroanalytical properties for the measurement of oxygen with high resolution in the brain extracellular space.In this study, extraction of univariated features of the collected signal is executed based on the analysis of electroencephalograms (EEG). The developed application implements a processing chain composed of: pre-processing; extraction of characteristics; reduction / selection of characteristics; classification and analysis of results.Four Wistar rats were studied at eight weeks of age, showing that it is possible to identify the moments in which seizures occur through analyzed features. The mean sensitivity and specificity in detection test data were 66.41% and 50.4%, respectively. However, for some animals a sensitivity and specificity above 80% have been detected.Through the application, it was possible to observe that different pre-processing options introduce changes on data classification performance, although a concrete pattern is not visible, probably due the small number of analyzed animals. To the best of our knowledge it appears to be no seizure prediction/detection studies using pO2 signals, which makes the results presented in this thesis innovative.
A epilepsia é uma doença cerebral crónica não transmissível, que afeta aproximadamente 50 milhões de pessoas de todas as idades em todo o mundo. Estima-se que, por ano, são diagnosticadas cerca de 2,4 milhões de pessoas com epilepsia. Como forma de melhorar a vida destas pessoas é importante perceber as alterações ocorridas durante e principalmente antes das crises, para possibilitar a sua previsão no futuro e consequentemente a melhoria da qualidade de vida.Esta tese descreve o desenvolvimento de uma aplicação computacional com a capacidade de analisar e processar sinais eletroquímicos recolhidos através de microeléctrodos de platina em matriz microfabricados num suporte cerâmico e que possuem as propriedades eletroanalíticas adequadas para a medição de oxigénio in vivo, no espaço extracelular no cérebro, com elevada resolução espacial e temporal, depois de implantados no cérebro de ratos tratados com pilocarpina como modelo de epilepsia do lobo temporal.No estudo é realizada a extração de características univariadas dos sinais recolhidos com base em estudos prévios realizados com eletroencefalogramas (EEG). A aplicação desenvolvida implementa uma cadeia de processamento composta por: pré-processamento; extração de características; redução/seleção de características; classificação e análise de resultados.Foram estudados quatro ratos Wistar com 8 semanas de vida, mostrando que é possível identificar os momentos em que ocorrem crises através das características analisadas. A sensibilidade e especificidade médias em dados de teste de deteção foram de 66,41% e 50,04%, respetivamente. No entanto, para alguns animais obtiveram-se sensibilidades e especificidades acima de 80%.Foi possível perceber que diferentes opções de processamento introduzem alterações no desempenho da classificação dos dados, apesar de não ser visível um padrão concreto, provavelmente devido ao reduzido número de animais analisados.No melhor do nosso conhecimento parece não haver estudos de previsão/deteção de crises usando sinais relativos à pO2, o que torna os resultados apresentados nesta tese inovadores.