Методы анализа ЭЭГ для прогнозирования эпилептических приступов

В роботі розглянуто проблематику прогнозування епілептичних нападів на основі аналізу сигналів електроенцефалограм. Розглянуто основні методи аналізу сигналів, які застосовуються при прогнозуванні та наведено результати використання цих методів. Основну увагу приділено методам аналізу сигналів в часовій області (енергія сигналу та довжина кривої), методам, в яких аналізують результати перетворення (спектрально-часовий та вейвлет-аналіз), нелінійним методам (ентропійний аналіз та аналіз синхронізації). Визначено, що основними проблемами, які виникають при прогнозуванні епілептичних нападів є низька завадостійкість методів та неможливість їх застосування для сигналів ЕЕГ, виміряних неінвазивно. Наведено рекомендації щодо напрямку подальших досліджень в сфері прогнозування епілептичних нападів.This paper considers epileptic seizures prediction methods based on electroencephalograms analysis. Basic methods of signal analysis for seizure prediction and the results of their application are presented. Methods of signal analysis in time domain (signal energy and curve length), methods based on signal transformation (spectral and wavelet analysis) and nonlinear methods (entropy analysis and synchronization analysis) were considered. Main problems that arise in epileptic seizures prediction are low noise resistance of methods and their unsuitability for EEG signals measured noninvasively. Recommendations for future research directions in seizure prediction are given.В работе рассмотрена проблематика прогнозирования эпилептических приступов на основе анализа сигналов электроэнцефалограм. Рассмотрены основные методы анализа сигналов, которые применяются при прогнозировании и описаны результаты использования этих методов. Основное внимание уделено методам анализа сигналов во временной области (энергия сигнала и длина кривой), методам, в которых анализируют результаты преобразования (спектрально-временной и вейвлет-анализ), нелинейным методам (энтропийный анализ и анализ синхронизации). Определено, что основными проблемами, которые возникают при прогнозировании эпилептических приступов являются низкая помехоустойчивость методов и невозможность их использования для сигналов ЭЭГ, измеренных неинвазивно. Приведены рекомендации по направлениям дальнейших исследований в сфере прогнозирования эпилептических приступов

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions

Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy

The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions