No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Proceedings: Epileptic seizure prediction
... Compared to the functional MRI, EEG provides a higher temporal insight into neural activity but has a lower spatial resolution. Typically, five frequency bands are analysed for processing EEG signals, Delta (up to 4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26), and Gamma (26-100 Hz). A summary of these frequency bands with their relation to human behavior is presented in Table III, The amplitude of Usually present in posterior region of the brain and in normal relaxed adults Beta 12-26 Present in frontal region of the brain and in alert anxious person Gamma 26-1000 Predominantly found in stressed, happy or aware person EEG range from 10 µV-100 µV while its frequency ranges from 1 Hz-100 Hz. ...
... In the 1970s, early research of ES prediction carried out using linear approaches of feature extraction [18]. While in 1980s, the development of non-linear methods helped researchers to employ these techniques for feature extraction because of the non-linear nature of EEG signals [19] [29] . ...
... With the development of non-linear methods, nonlinear measure have been used to identify pre-ictal patterns [18] 1983: Lange et al. showed the change in spike rate before seizure onset [19]. ...
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.
... In the 1970s, early research of ES prediction carried out using linear approaches of feature extraction [35]. While in [97]. ...
... With the development of non-linear methods, nonlinear measure have been used to identify pre-ictal patterns [35] 1983: Lange et al. showed the change in spike rate before seizure onset [36]. ...
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.
... Liu et al. [19] used Scored Autocorrelation Moment (SAM) analysis, and distinguished EEG epochs containing seizures. The concept of seizure prediction was originally stated in 1975 [20] for the EEG data collected from two electrodes based on spectral analysis. In 1981, Rogowski et al. [21] pole trajectories of an autoregressive model are used to study the preictal periods. ...
... Any training samples that fall on hyperplane H 1 or H 2 satisfy Eq. (20) are called support vectors. They are close to the MMH. ...
In the study of detection of an epileptic seizure using Electroencephalogram (EEG), pattern recognition has been recognised as a valued tool. In this pattern recognition study, the first time the authors have attempted to use time domain (TD) features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived directly from filtered EEG data and from discrete wavelet transform (DWT) of filtered EEG data for the detection of an epileptic seizure. Further, the authors attempted to study the performance of other time domain features such as mean absolute value (MAV), standard deviation (SD), average power (AVP) which had been attempted by other researchers. The performance of the TD features is studied using naïve Bayes (NB) and support vector machines (SVM) classifiers for University of Bonn database with fourteen different combinations of set E with set A to D and clinically inferred with Christian Medical College, Vellore database. The proposed scheme was also compared with other existing scheme in the literature. The implementation results showed that the proposed scheme could attain the highest accuracy of 100% for normal eyes open and epileptic data set with direct as well as DWT based TD features. For other data sets, the highest accuracy are obtained with DWT based TD features using SVM.
... Over the past several decades, investigators have applied a variety of techniques in an attempt to predict seizures. Early approaches, such as that published in 1975 by Viglione et al., utilizing linear analyses of intracranial EEG (iEEG) with pattern recognition techniques to predict seizures was successful only in detecting seizures, but high false-positive rates limited its utility for seizure prediction [9]. ...
... The algorithm was being developed to Brain Sci. 2019, 9,156 4 of 18 simultaneously perform two functions: to predict the occurrence of seizures and to predict the lack of seizures. From this large set of candidate feature extractors, a much smaller set of 288 features were selected, representing a combination of 16 iEEG channels with unique filtering and analysis processes; these comprised the master set of feature-extractors for use in the SAS. ...
This research study is part of a therapy development effort in which a novel approach was taken to develop an implantable electroencephalographic (EEG) based brain monitoring and seizure prediction system. Previous attempts to predict seizures by other groups had not been demonstrated to be statistically more successful than chance. The primary clinical findings from this group were published in a clinical paper; however much of the fundamental technology, including the strategy and techniques behind the development of the seizure advisory system have not been published. Development of this technology comprised several steps: a vast high quality database of EEG recordings was assembled, a structured approach to algorithm development was undertaken, an implantable 16-channel subdural neural monitoring and seizure advisory system was designed and built, preclinical studies were conducted in a canine model, and a First-In-Man study involving implantation of 15 patients followed for two years was conducted to evaluate the algorithm. The algorithm was successfully trained to correctly provide a) notification of a high likelihood of seizure in 11 of 14 patients, and b) notification of a low likelihood of seizure in 5 of 14 patients (NCT01043406). Continuous neural state monitoring shows promise for applications in seizure prediction and likelihood estimation, and insights for further research and development are drawn.
... The pre-ictal is the period before the seizure occurrence when a patient feels visual auras. The inter-ictal is the seizure-free period between two seizure events [12]. The seizure events are detected in the ictal period. ...
... In order to compare the effect of different time-domain and frequency-domain feature sets on classification accuracy, we will also compute the frequency-domain features over the ECoG signals. The frequency-domain set of input features represents the power-in-band properties of the ECoG signals in the commonly studied Berger's frequency bands, Standard Delta (0-4 Hz), Theta (4-8 Hz), Alpha (8-12 Hz), Beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and Gamma (30-100 Hz). We filter 20 s of ECoG data using a band-pass filter (2nd order Butterworth) with five frequency bands and find the power spectral density by squaring the signal (based on Plancheral theorem to skip the calculation of FFT over ECoG signals). ...
In the last decade, seizure prediction systems have gained a lot of attention because of their enormous potential to largely improve the quality-of-life of the epileptic patients. The accuracy of the prediction algorithms to detect seizure in real-world applications is largely limited because the brain signals are inherently uncertain and affected by various factors, such as environment, age, drug intake, etc., in addition to the internal artefacts that occur during the process of recording the brain signals. To deal with such ambiguity, researchers transitionally use active learning, which selects the ambiguous data to be annotated by an expert and updates the classification model dynamically. However, selecting the particular data from a pool of large ambiguous datasets to be labelled by an expert is still a challenging problem. In this paper, we propose an active learning-based prediction framework that aims to improve the accuracy of the prediction with a minimum number of labelled data. The core technique of our framework is employing the Bernoulli-Gaussian Mixture model (BGMM) to determine the feature samples that have the most ambiguity to be annotated by an expert. By doing so, our approach facilitates expert intervention as well as increasing medical reliability. We evaluate seven different classifiers in terms of the classification time and memory required. An active learning framework built on top of the best performing classifier is evaluated in terms of required annotation effort to achieve a high level of prediction accuracy. The results show that our approach can achieve the same accuracy as a Support Vector Machine (SVM) classifier using only 20% of the labelled data and also improve the prediction accuracy even under the noisy condition.
... Les travaux scientifiques sérieux sur la prédiction des crises d'épilepsie ont commencé dès les années 1970 par les idées visionnaires de Viglione et de ses collègues [46], mais la technologie disponible n'a vraiment commencé à concrétiser cette vision que 20 ans plus tard. Le grand intérêt porté à ces travaux depuis 1990 est dû à la confluence de plusieurs facteurs : la découverte d'un état prédictif avant les crises; la large adoption de la technologie électroencéphalographique (EEG) ; l'évolution des méthodes d'enregistrement en utilisant des électrodes intracrâniennes pour localiser les crises et la mise en place de méthodes mathématiques de traitement permettant d'analyser les enregistrements continus des électroencéphalogrammes crâniens et intracrâniens afin de trouver des marqueurs ou des changements dans l'activité neuronale, qui peuvent être indicatifs du début d'une crise. ...
Ce manuscrit rapporte une nouvelle approche pour la prédiction des crises épileptiques basée sur la modélisation autorégressive des signaux EEG et le calcul d'un indice de stabilité (IS) permettant de quantifier l'activité électrique épileptique et ainsi de suivre l'évolution de la dynamique de la crise au cours de ses différents états (précritique, critique et intercritique) et même la prédiction de sa survenue.
Cette approche repose d'abord sur une méthode de seuillage comparant l'IS à un seuil bien défini et déclarant l'apparition d'une crise dès que ce seuil est dépassé. Elle a été validée sur deux types de données : des signaux EEG réels et des signaux EEG modèles générés avec le simulateur " Epileptor " de la plateforme neuro-informatique TVB, qui est un outil d'assimilation de l'activité électrique cérébrale, de modélisation des signaux EEG ainsi que des crises à partir de matrices de connectivité neuronale extraites de l'imagerie IRM.
Dans la continuité, nous avons amélioré le concept par l’intégration d'autres caractéristiques calculées sur les mêmes époques du signal EEG, telles que la densité spectrale de puissance relative (DSPR) et l'entropie de l’échantillon (SampEN). Pour traiter ces nouvelles caractéristiques associées, nous avons implémenté un modèle d'apprentissage profond (CNN) en vue d’améliorer les performances de prédiction et plus particulièrement le délai de prédiction avant la survenue de la crise.
Nos approches ont montré de la robustesse et de meilleures performances en termes de prédiction des crises que celles rapportées dans la littérature, à savoir une excellente précision, un minimum de fausses alarmes et un temps de prédiction élevé.
This manuscript reports a new approach for the prediction of epileptic seizures based on the autoregressive modeling of EEG signals and the calculation of a stability index allowing to quantify the epileptic electrical activity and thus to follow the evolution of the dynamic of the seizure during its different states (preictal, ictal and interictal) and even the prediction of its occurrence.
The approach is based on a thresholding method that compares the stability index to a well- defined threshold and declares the occurrence of a seizure as soon as the threshold is exceeded. It was validated on two types of data: real EEG signals and model EEG signals generated with the "Epileptor" model of the TVB neuroinformatics platform, which is a tool for assimilating cerebral electrical activity and modeling EEG signals and seizures from neuronal connectivity matrices extracted from MRI imaging.
As a continuation, we improved the design by integrating other features computed on the same epochs of the EEG signal, such as relative power spectral density (RPSD) and sample entropy (SampEN). To deal with these new associated features, we implemented a deep learning model (CNN) to improve the prediction performance and more specifically the prediction time delay before seizure onset.
Our approaches showed robustness and better performance in terms of seizure prediction than those reported in the literature, namely excellent accuracy, minimum false alarms and high prediction time.
... Seizure prediction has been studied since the 1980s. Most methods at that time were based on signal processing and pattern recognition, such as time series and spectral analysis [108], [109]. EEG complexity indices, such as Lyapunov exponents [110] and similarity indices [111], have also been used to predict seizures. ...
Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details. Here, we review various types of computational models of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail. A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioral observations. With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.
... An individual with epilepsy typically has the same kind of seizure every other time. Symbols and seizure signs may include: Brief confusion, Spell of Steady Eye, Motion of a Rigid Body, Spasmodic movement that is uncontrollable, Incognizance and ignorance, Indicators of the spirit and such as anxiety or terror [41][42][43]. ...
Epilepsy is a long-term medical disorder that frequently causes unpredictable, unprovoked repeated seizures that have an impact on both physical and mental abilities. It is among the most prevalent neurological conditions. Greek term epilambanein, which is the root of the English word epilepsy, means "to be seized." Both the sickness and the one-time attack were meant by this. The word refers to the magical beliefs of the time, which led to the stigma associated with epilepsy because people with epilepsy were seen to be dirty or bad. A recent study found that nearly 90% of the 70 million epileptics worldwide live in developing countries. Genetic testing has expanded the possibility of figuring out the aetiology of different types of epilepsies. It needs some prior clinical application knowledge to complete this challenging endeavour. Genetic testing techniques include Review Article Srivastav et al.; Asian J. 65 chromosome microarray analysis, karyotyping, single-gene testing, gene panel testing, whole exome sequencing, and whole genome sequencing. The allegedly first documented account of epilepsy, as it was then perceived and understood, may be found in one of the earliest Babylonian medical manuals, Sakikku (English translation: "All Diseases"), which dates from around 1050 BC. The pathogenesis, aetiology, treatment, biomarkers, and risk factors for epilepsy are reviewed in this review article.
... One seizure may not necessarily indicate epilepsy. For an epileptic classification, at least 2 unprovoked seizures (seizures caused by unknown reasons) must be occurred within 24 hours away [24,25]. Any brain-coordinated process can be disturbed by seizures since aberrant brain activity causes Epilepsy. ...
In recent years, the electroencephalography (EEG) signal identification of epileptic seizures has developed into a routine procedure to determine epilepsy. Since physically identifying epileptic seizures by expert neurologists becomes a labor-intensive, time-consuming procedure that also produces several errors. Thus, efficient, and computerized detection of epileptic seizures is required. The disordered brain function that causes epileptic seizures can have an impact on a patient's condition. Epileptic seizures can be prevented by medicine with great success if they are predicted before they start. Electroencephalogram (EEG) signals are utilized to predict epileptic seizures by using machine learning algorithms and complex computational methodologies. Furthermore, two significant challenges that affect both expectancy time and genuine positive forecast rate are feature extraction from EEG signals and noise removal from EEG signals. As a result, we suggest a model that offers trustworthy preprocessing and feature extraction techniques. To automatically identify epileptic seizures, a variety of ensemble learning-based classifiers were utilized to extract frequency-based features from the EEG signal. Our algorithm offers a higher true positive rate and diagnoses epileptic episodes with enough foresight before they begin. On the scalp EEG CHB-MIT dataset on 24 subjects, this suggested framework detects the beginning of the preictal state, the state that occurs before a few minutes of the onset of the detention, resulting in an elevated true positive rate of (91%) than conventional methods and an optimum estimation time of 33 minutes and an average time of prediction is 23 minutes and 36 seconds. Depending on the experimental findings' The maximum accuracy, sensitivity, and specificity rates in this research were 91 %, 98%, and 84%.
... Attempts to develop reliable seizure prediction algorithms have an extensive history, dating back to the 1970s (Viglione and Walsh, 1975) with minimal data sets looking only at pre-seizure (preictal) events minutes to seconds before seizures. Massively evolving over the past 50 years, current methods use mathematical tools to analyze continuous days of multiscale EEG recordings (Lehnertz and Litt, 2005). ...
Self-organized neuronal oscillations rely on precisely orchestrated ensemble activity in reverberating neuronal networks. Chronic, non-malignant disorders of the brain are often coupled to pathological neuronal activity patterns. In addition to the characteristic behavioral symptoms, these disturbances are giving rise to both transient and persistent changes of various brain rhythms. Increasing evidence support the causal role of these “oscillopathies” in the phenotypic emergence of the disease symptoms, identifying neuronal network oscillations as potential therapeutic targets. While the kinetics of pharmacological therapy is not suitable to compensate the disease related fine-scale disturbances of network oscillations, external biophysical modalities (e.g., electrical stimulation) can alter spike timing in a temporally precise manner. These perturbations can warp rhythmic oscillatory patterns via resonance or entrainment. Properly timed phasic stimuli can even switch between the stable states of networks acting as multistable oscillators, substantially changing the emergent oscillatory patterns. Novel transcranial electric stimulation (TES) approaches offer more reliable neuronal control by allowing higher intensities with tolerable side-effect profiles. This precise temporal steerability combined with the non- or minimally invasive nature of these novel TES interventions make them promising therapeutic candidates for functional disorders of the brain. Here we review the key experimental findings and theoretical background concerning various pathological aspects of neuronal network activity leading to the generation of epileptic seizures. The conceptual and practical state of the art of temporally targeted brain stimulation is discussed focusing on the prevention and early termination of epileptic seizures.
... Electroencephalogram (EEG) was first used in humans in the 1920s [51], but it was not until the 1970s did clinicians started utilizing EEG for seizure predictions [52]. Today, it is one of the most commonly used devices for epilepsy prediction and management. ...
Background:
Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduce the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patients occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements in medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases, PubMed, Scopus and EMBASE.
Methods:
Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review.
Results:
These articles revealed ambulatory, non-invasive and wearable medical devices, such as the in-ear EEG devices; the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signalbased devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures.
Conclusion:
The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).
... And the key to accurate identification is to determine the pre-onset stage in combination with nonlinear dynamic changes. Traditional seizure detection and prediction are mostly based on small sample machine learning methods [26]. There is no pre-research on long-term large-scale data. ...
Background
Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions.
Methods
In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network.
Results
Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly.
Conclusion
In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area.
... Beginning in the 1970s, Viglione et al. used pattern recognition principles to develop an automatic epileptic seizure warning system. Their system transforms the signal to extract or detect the basic features from the processed signal, and determine the condition of the person from the detected features [6][7][8]. Since then, various approaches such as threshold-based [9,10], machine learning-based [11,12], and deep learningbased [13][14][15][16] approaches have been applied to this problem. However, some published seizure detection methods are trained on a small EEG data set with a small number of specific patients, resulting in these methods are not suitable for clinical use. ...
... Just like large parts of quantitative EEG methodology and application areas, seizure prediction dates back to the 1970s [213]. The endeavors are stimulated by frequent patient reports about seizure precursors [214]. ...
The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted.
Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity.
Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.
... Various features are extracted from the EEG channels. One of the first attempts to predict seizures investigated spectral properties of EEG data for different states of the patient with epilepsy [12]. It has been shown that multivariate channel features are better than univariate features like lyponov exponents [13], [14], [15], [16], [17], [18], [19], [8]. ...
Electroencephalogram (EEG) is a prominent way to measure the brain activity for studying epilepsy, thereby helping in predicting seizures. Seizure prediction is an active research area with many deep learning based approaches dominating the recent literature for solving this problem. But these models require a considerable number of patient-specific seizures to be recorded for extracting the preictal and interictal EEG data for training a classifier. The increase in sensitivity and specificity for seizure prediction using the machine learning models is noteworthy. However, the need for a significant number of patient-specific seizures and periodic retraining of the model because of non-stationary EEG creates difficulties for designing practical device for a patient. To mitigate this process, we propose a Siamese neural network based seizure prediction method that takes a wavelet transformed EEG tensor as an input with convolutional neural network (CNN) as the base network for detecting change-points in EEG. Compared to the solutions in the literature, which utilize days of EEG recordings, our method only needs one seizure for training which translates to less than ten minutes of preictal and interictal data while still getting comparable results to models which utilize multiple seizures for seizure prediction.
... It is definitely different from the background wave in EEG, which plays a significant role in predicting epileptic seizures [9][10][11][12]. Different forms of spike waves are shown in Figure 1. the 1970s by Viglione and Walsh for the prediction of epileptic seizures [13], they analyzed EEG data of patients with different seizures, but the efficiency and accuracy were not promising, then the study of seizures prediction had been ignored. Until the beginning of the 21st century, Thomas Maiwald et al. [14] used three nonlinear methods to predict seizures. ...
As an auxiliary examination method, electroencephalography (EEG) is widely used in the prediction and analysis of epileptic seizures. In this paper, the research aims to solve the difficulties of automatic prediction of epileptic seizures, the complexity of feature extraction in real-time prediction, and poor generality of the algorithm. We have proposed a target detection model (YOLOV3) in convolutional neural network (CNN) to detect spike waves in EEG to predict epileptic seizures in real time. Firstly, the low-complexity spike waves characteristics in the short-term scalp Bonn EEG database are extracted and labeled. Secondly, the YOLOV3 model is trained. Next, the trained model is used to verify the long-term CHB-MIT scalp EEG database. Four different preictal windows at 30 min, 60 min, 90 min and 120 min are used for real-time prediction of epileptic seizures. Finally, the experimental results show that the sensitivity of different preictal windows are 93.91%, 95.75%, 97.25% and 98.29% respectively, the average prediction time is 43.82 min, the average detection speed is 0.073 s per EEG and the false prediction rate(FPR) is 0.109 times/h. Compared with the traditional methods, the new method of epileptic seizures prediction based on YOLOV3 proposed in this paper can predict epileptic seizures accurately, efficiently and in real time, which has clinical application value.
... The search for the hidden information predictive of an impending seizure has a long history in EEG analysis. The first attempt to predict seizures was made by Viglione and Walsh in 1975. Assuming the existence of a preictal state, these investigators trained a feature extraction and pattern recognition algorithm to distinguish between arbitrarily selected 10-min preictal and other EEG epochs. ...
... It causes the drop in consciousness, fast muscular activity or an unexpected sensation [78]. Many works for detecting the epileptic seizures were performed from 1970 [83]. Even though many noninvasive techniques were invented for identifying activities of the human brain, electroencephalography is evident in representing the electrical movement of the brain with a millisecond temporal resolution. ...
Epilepsy is a nervous disorder occurring in the cerebral cortex location of the brain which is caused by irregular harmonization of neurons. Since the existence of this disorder is between the neurons, it is tedious to diagnose correctly. Research works of epilepsy mostly done on an Electroencephalogram (EEG) signals for analyzing the neuron activity of the brain during seizures. Analyzing the continuing EEG reports manually for a patient affected by epilepsy is time-consuming, and it needs a large storage volume. The proposed paper is based on a unique method for detecting epileptic seizures by Adjustable Analytic Wavelet Transform (AAWT). This work is also focused on testing the practicability of utilizing the Kohonen network maps for predicting the dynamics of the brain states in the form of the trajectory which may provide the occurrence of the seizure event. AAWT is applied on each EEG signal to decompose EEG signals into the sub-band signals. The fractal dimension is applied to these sub-bands signals as a discriminating feature due to its nonlinear chaotic trait. The received solutions are fed into Kohonen self-organizing network map (KSOM) to get a stable performance rate for the categorization of an epileptic seizure. The results proved that the introduced methodology achieved 98.72% sensitivity, 93.90% specificity, 93.03% selectivity, and 94.12% efficiency than the existing models and provided promising classification accuracy.
... Although the research works on predicting epileptic seizures have been carried out since 1970s [7], there are still no highly reliable and practical methods available to predict impending seizures in patients with epilepsy. That is why the development and improvement of seizure detection approaches is crucial [1]. ...
... The analysis is performed in time domain, frequency domain and time-frequency domain. The concept of seizure prediction was originally stated in Ref. [53] for the EEG data collected from two electrodes based on spectral analysis. In Ref. [54] pole trajectories of an autoregressive model are used to study the pre-ictal periods. ...
Over many decades, research is being attempted for the detection of epileptic seizure to support for automatic diagnosis system to help clinicians from burdensome work. In this respect, an enormous number of research papers is published for identification of epileptic seizure. It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection of an epileptic seizure. More than 100 research papers have been discussed to discern the techniques for detecting the epileptic seizure. Further, the literature survey shows that the pattern recognition required to detect epileptic seizure varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG epileptic data from a various condition of EEG data.
... Epileptic seizure prediction is defined as the identification of a time when seizure may soon occur without prior knowledge of the exact time when it will occur (Viglione and Walsh 1975). Methods for seizure prediction include relative spectral features, frequency domain and time frequency analysis, state similarity analysis, spike rate analysis, spatiotemporal correlation, phase synchronization, time domain, and many other methods based on machine learning, statistics, and nonlinear methods (Gadhoumi et al. 2016). ...
Networks are naturally occurring phenomena that are studied across many disciplines. The topological features of a network can provide insight into the dynamics of a system as it evolves, and can be used to predict changes in state. The brain is a complex network whose temporal and spatial behavior can be measured using electroencephalography (EEG). This data can be reconstructed to form a family of graphs that represent the state of the brain over time, and the evolution of these graphs can be used to predict changes in brain states, such as the transition from preictal to ictal in patients with epilepsy. This research proposes objective indications of seizure onset observed from minimally invasive scalp EEG. The approach considers the brain as a complex nonlinear dynamical system whose state can be derived through time-delay embedding of the EEG data and characterized to determine change in brain dynamics related to the preictal state. This method targets phase-space graph spectra as biomarkers for seizure prediction, correlates historical degrees of change in spectra, and makes accurate prediction of seizure onset. A significant trend of normalized dissimilarity over time indicates a departure from the norm, and thus a change in state. Our methods show high sensitivity (90–100%) and specificity (90%) on 241 h of scalp EEG training data, and sensitivity and specificity of 70%–90% on test data. Moreover, the algorithm was capable of processing 12.7 min of data per second on an Intel Core i3 CPU in Matlab, showing that real-time analysis is viable.
... Liu et al. [19] used scored autocorrelation moment (SAM) analysis, and distinguished EEG epochs containing seizures even though signals did not exist changes in their spectral properties. The concept of seizure prediction was originally stated in 1975 [20] for the EEG data collected from two electrodes based on spectral analysis. In 1981, Rogowski et al.'s [21] pole trajectories of an autoregressive model are used to study the preictal periods. ...
Pattern recognition plays an important role in the detection of epileptic seizure from electroencephalogram (EEG) signals. In this pattern recognition study, the effect of filtering with the time domain (TD) features in the detection of epileptic signal has been studied using naive Bayes (NB) and supports vector machines (SVM). It is the first time the authors attempted to use TD features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived from the filtered and unfiltered EEG data, and performance of these features is studied along with mean absolute value (MAV) which has been already attempted by the researchers. The other TD features which are attempted by the researchers such as standard deviation (SD) and average power (AVP) along with MAV are studied. A comparison is made in effect of filtering and without filtering for the University of Bonn database using NB and SVM for the TD features attempted first time along with MAV. The effect of individual and combined TD features is studied and the highest classification accuracy obtained in using direct TD features would be 99.87%, whereas it is 100% with filtered EEG data. The raw EEG data can be segmented and filtered using the fourth-order Butterworth band-pass filter.
... So scientists kept exploring the possibility of accurately predicting the seizure occurrences even a few seconds in advances. Viglione and Walsh carried out the first attempt of predicting epileptic seizures in 1975 [23]. Salant, Y. et al., extracted the spectral features to train an EEG-based epileptic seizure prediction algorithm and successfully predict seizures 1-6 s in advance for 5 patients [24]. ...
The neurological disorder epilepsy causes substantial problems to the patients with uncontrolled seizures or even sudden deaths. Accurate detection and prediction of epileptic seizures will significantly improve the life quality of epileptic patients. Various feature extraction algorithms were proposed to describe the EEG signals in frequency or time domains. Both invasive intracranial and non-invasive scalp EEG signals have been screened for the epileptic seizure patterns. This study extracted a comprehensive list of 24 feature types from the scalp EEG signals and found 170 out of the 2794 features for an accurate classification of epileptic seizures. An accuracy (Acc) of 99.40% was optimized for detecting epileptic seizures from the scalp EEG signals. A balanced accuracy (bAcc) was calculated as the average of sensitivity and specificity and our seizure detection model achieved 99.61% in bAcc. The same experimental procedure was applied to predict epileptic seizures in advance, and the model achieved Acc = 99.17% for predicting epileptic seizures 10 s before happening.
... Epileptic seizure prediction is defined as the identification of a time when seizure may soon occur without prior knowledge of the exact time when it will occur [2]. A reliable seizure prediction algorithm would allow patients to reduce the risk of injury to themselves and others, as well as improve their quality of life. ...
Epilepsy is a chronic disorder characterized by recurrent seizures. Prolonged seizure can evolve into status epilepticus, which can lead to injury or death. We propose a seizure prediction algorithm using a hyper-graph approach to phase-space analysis.
Objective indications of seizure onset are derived via time delay embedding of minimally invasive time serial scalp EEG. The approach considers the brain as a complex nonlinear dynamical system whose states can be characterized to determine change in brain dynamics related to epileptic seizure activity. Our method extracts phase-space graphs via nonlinear time series analysis and time delay embedding and partitions the phase-space graphs to form hypergraphs. The features of the hypergraphs are evaluated using
spectral analysis to form biomarkers for seizure prediction. The algorithm correlates historical degrees of change in hypergraph spectra from repeated measurements and makes accurate predictions of seizure onset. Our method yields statistically significant results on scalp EEG data, with a training accuracy of 93% and testing accuracy of 80%.
Among the many fears associated with seizures, patients with epilepsy are greatly frustrated and distressed over seizure's apparent unpredictable occurrence. However, increasing evidence have emerged over the years to support that seizure occurrence is not a random phenomenon as previously presumed; it has a cyclic rhythm that oscillates over multiple timescales. The pattern in rises and falls of seizure rate that varies over 24 hours, weeks, months, and years has become a target for the development of innovative devices that intend to detect, predict, and forecast seizures. This article will review the different tools and devices available or that have been previously studied for seizure detection, prediction, and forecasting, as well as the associated challenges and limitations with the utilization of these devices. Although there is strong evidence for rhythmicity in seizure occurrence, very little is known about the mechanism behind this oscillation. This article concludes with early insights into the regulations that may potentially drive this cyclical variability and future directions.
Knowing when seizures occur may help patients and can also provide insight into epileptogenesis mechanisms. We recorded seizures over periods of several days in the Genetic Absence Epileptic Rat from Strasbourg (GAERS) model of absence epilepsy, while we monitored behavioral activity with a combined head accelerometer (ACCEL), neck electromyogram (EMG), and electrooculogram (EOG). The three markers consistently discriminated between states of behavioral activity and rest. Both GAERS and control Wistar rats spent more time in rest (55–66%) than in activity (34–45%), yet GAERS showed prolonged continuous episodes of activity (23 vs. 18 min) and rest (34 vs. 30 min). On average, seizures lasted 13 s and were separated by 3.2 min. Isolated seizures were associated with a decrease in the power of the activity markers from steep for ACCEL to moderate for EMG and weak for EOG, with ACCEL and EMG power changes starting before seizure onset. Seizures tended to occur in bursts, with the probability of seizing significantly increasing around a seizure in a window of ±4 min. Furthermore, the seizure rate was strongly increased for several minutes when transitioning from activity to rest. These results point to mechanisms that control behavioral states as determining factors of seizure occurrence.
Diagnostics and prognoses of brain disorders can be greatly aided by machine learning. To bring these tools into clinical routine, we argue that key challenges remain to be addressed by the community. To overcome the limitations of black-box approaches, we need to use interpretable models to overcome the shortcomings of validation and reproducible research practices. Extensive generalization studies are required. Many people die each year from brain diseases, which are the most prevalent. As the death toll continues to rise, it is estimated that it will reach 75 million by 2030. We cannot predict brain diseases with modern technology or an advanced healthcare system. In our paper, we use machine learning algorithms to implement the neurological disease prediction approach since such algorithms are a critical source of data prediction. MIT-BIH repository data comprising a variety of patients were used as the database. Based on the classifiers utilized, the findings have proven that the RDF produced the most accurate result with 96.32% accuracy.
Epilepsy is a common chronic neurological brain disorder with a complex etiology. Epilepsy prediction, one of the most challenging data analysis tasks in chronic brain disorders, has attracted much attention from many researchers. This paper proposes a seizure prediction algorithm based on a multidimensional EEG spatial matrix and residual network. Since EEG signals are random, non-stationary, and non-linear, a single feature extraction method cannot represent EEG signal characteristics in many aspects, while entropy can better describe complex EEG signals and identify the dynamic characteristics of EEG signals. Therefore, this paper proposes using sample entropy (SE), permutation entropy (PE), and fuzzy entropy (FE) for feature extraction of EEG signals. Meanwhile, to better describe the irregularity and non-stationary of EEG signals, this paper uses fractal dimension (FD) and fluctuation index (FI) for feature extraction of EEG signals. To better extract the spatial location information of the EEG signal, this paper constructs the spatial location matrix by an electrode distribution map. It reconstructs the one-dimensional feature vector into a two-dimensional feature matrix. The five two-dimensional matrices are superimposed into a three-dimensional matrix as the input form of the network, and the residual network algorithm is used for seizure prediction. The proposed method achieved good performance on the CHB-MIT dataset.KeywordsSeizure predictionEEGSpatial matrixResidual Network
Epilepsy as a common disease of the nervous system, with high incidence, sudden and recurrent characteristics. Therefore, timely prediction of seizures and intervention treatment can significantly reduce the accidental injury of patients and protect the life and health of patients. Epilepsy seizures is the result of temporal and spatial evolution, Existing deep learning methods often ignore its spatial features, in order to make better use of the temporal and spatial characteristics of epileptic EEG signals. We propose a CBAM-3D CNN-LSTM model to predict epilepsy seizures. First, we apply short-time Fourier transform(STFT) to preprocess EEG signals. Secondly, the 3D CNN model was used to extract the features of preictal stage and interictal stage from the preprocessed signals. Thirdly, Bi-LSTM is connected to 3D CNN for classification. Finally CBAM is introduced into the model. Different attention is given to the data channel and space to extract key information, so that the model can accurately extract interictal and pre-ictal features. Our proposed approach achieved an accuracy of 97.95%, a sensitivity of 98.40%, and a false alarm rate of 0.017 h
<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>
on 11 patients from the public CHB-MIT scalp EEG dataset.
The paper proposes a generalized approach for epileptic seizure prediction rather than a patient-specific approach. The early diagnosis of seizures may assist in reducing the severity of damage and can be utilized to aid in the treatment of epilepsy patients. Developing a patient-independent model is more challenging than a patient-specific model due to the EEG variability across patients. Our objective is to predict seizure accurately by detecting the pre-ictal state that occurs prior to a seizure. We have used the “CHB-MIT Scalp EEG Dataset” for our research and implemented the research work using Butterworth Bandpass Filter and simple 2D Convolutional Neural Network to differentiate pre-ictal and inter-ictal signals. We have achieved accuracy of 89.5%, sensitivity 89.7%, precision 89.0% and AUC, the area under the curve is 89.5% with our proposed model. In addition, we have addressed several researchers’ seizure prediction models, sketched their core mechanism, predictive effectiveness, and compared them with ours.KeywordsBandpass filterChronic neurological disorderConvolutional neural networkCHB-MIT Scalp EEG DatasetDeep learningEpilepsyGeneralized modelPredictionSeizure
Seizure prediction based on electroencephalogram (EEG) has great potential to improve patients’ life quality. Due to the high heterogeneity in distributions of EEG signals among different patients, conventional studies usually show poor generalization ability when transferring the model to new patients, which also leads to difficulties in clinical applications. To alleviate the challenging issue concerning cross-subject domain shift, we propose a transformer-based domain adversarial model. Our model first adopts a pre-trained general neural network to extract common features from the EEG signals of available patients. Then, we design a distiller module and a domain discriminator module to perform domain adaptation training based on a small amount of labeled data from the new-coming patient. During adaptation process, conditional domain adversarial training with the addition of label information is employed to remove patient-related information from the extracted features to learn a common seizure feature space among different patients. Our proposed seizure prediction method is evaluated on the CHB-MIT EEG database. The proposed model achieves a sensitivity of 79.5%, a false alarm rate (FPR) of 0.258/h, and an AUC of 0.814. Experimental results demonstrate that the proposed method can effectively reduce inter-patient domain disparity compared to state-of-the-art methods.
The epilepsies are devastating neurological disorders for which progress developing effective new therapies has slowed over recent decades, primarily due to the complexity of the brain at all scales. This reality has shifted the focus of experimental and clinical practice toward complex systems approaches to overcoming current barriers. Organized by scale from genes to whole brain, the chapters of this book survey the theoretical underpinnings and use of network and dynamical systems approaches to interpreting and modeling experimental and clinical data in epilepsy. The emphasis throughout is on the value of the non-trivial, and often counterintuitive, properties of complex systems, and how to leverage these properties to elaborate mechanisms of epilepsy and develop new therapies. In this essential book, readers will learn key concepts of complex systems theory applied across multiple scales and how each of these scales connects to epilepsy.
Epilepsy is one of the most common neurological disorders worldwide and can cause the brain to stop working properly or even endanger the life of the patient. Epilepsy prediction is a prerequisite for seizure control, allowing preventive measures to mitigate damage or control seizures. It has been found that abnormal brain activity begins some time before a seizure, which is known as the pre-ictal state. In this study, we reconsidered the temporal scope of the pre-ictal period and divided it into multiple temporal windows. A patient-specific seizure prediction method based on deep residual shrinkage network (DRSN) and gated recurrent unit (GRU) was then proposed. The temporal dependency of the signal of different time windows in the pre-ictal period is modeled by introducing GRU into a DRSN. In addition, automatic feature extraction is achieved by applying soft threshold denoising and attention mechanism inside the neural network. The proposed method was tested on four patients reasonably selected from the CHB-MIT scalp EEG dataset, which achieved a sensitivity of 90.54%, an AUC value of 0.88, and a false prediction rate of 0.11/h. The results obtained by our method are compared with the recent epilepsy prediction methods. Compared with the being among the best method, our method still has a little gap, but it also shows a new idea and some advantages.
Epilepsy, one of the most common neurological diseases in China, is notorious for its spontaneous, unprovoked and recurrent seizures. The etiology of epilepsy varies among individual patients, including congenital gene mutation, traumatic injury, infections, etc. This heterogeneity partly hampered the accurate diagnosis and choice of appropriate treatments. Encouragingly, great achievements have been achieved in computational science, making it become a key player in medical fields gradually and bringing new hope for rapid and accurate diagnosis as well as targeted therapies in epilepsy. Here, we historically review the advances of computerized applications in epilepsy—especially those tremendous findings achieved in China—for different purposes including seizure prediction, localization of epileptogenic zone, post‐surgical prognosis, etc. Special attentions are paid to the great progress based on artificial intelligence (AI), which is more “sensitive”, “smart” and “in‐depth” than human capacities. At last, we give a comprehensive discussion about the disadvantages and limitations of current computerized applications for epilepsy and propose some future directions as further stepping stones to embrace “the era of AI” in epilepsy.
Electroencephalogram (EEG) is a prominent way to measure the brain activity for studying epilepsy, thereby helping in predicting seizures. Seizure prediction is an active research area with many deep learning based approaches dominating the recent literature for solving this problem. But these models require a considerable number of patient-specific seizures to be recorded for extracting the preictal and interictal EEG data for training a classifier. The increase in sensitivity and specificity for seizure prediction using the machine learning models is noteworthy. However, the need for a significant number of patient-specific seizures and periodic retraining of the model because of non-stationary EEG creates difficulties for designing practical device for a patient. To mitigate this process, we propose a Siamese neural network based seizure prediction method that takes a wavelet transformed EEG tensor as an input with convolutional neural network (CNN) as the base network for detecting change-points in EEG. Compared to the solutions in the literature, which utilize days of EEG recordings, our method only needs one seizure for training which translates to less than ten minutes of preictal and interictal data while still getting comparable results to models which utilize multiple seizures for seizure prediction.KeywordsSeizure predictionElectroencephalogram (EEG)Siamese learningDeep learning
Epilepsy is a chronic brain disorder and epileptic patients encounter recurrent seizures caused by abnormally synchronous electrical activity in parts of the brain. Over 50 million people spread across the world have epilepsy amongst whom approximately 30% suffer from refractory epilepsy which cannot be controlled by existing treatment protocols. For all epileptic sufferers, the thought that their next seizure could come at any time is agonizing and traumatic. However, if seizures could be predicted reliably, associated dangers and inconveniences will be greatly mitigated. Although the epileptic seizure prediction challenge has been tackled headlong by researchers through different modelling methods the problem of prediction has not yet been satisfactorily solved. In this paper, a systematic literature review of prominent epileptic seizure prediction attempts was carried out. We focus majorly on the two predominant classes of modelling attempts used: physiological mechanism and data based. The review underscores the richness and utility of the diverse modeling strategies as well as the gainful contribution of researchers in the field of epilepsy. It shows that meaningful progress has been made towards discovering the exact mechanism of seizure generation and realization of reliable and consistent seizure prediction algorithm
This book gathers selected high-quality research papers presented at International Conference on Advanced Computing and Intelligent Technologies (ICACIT 2021) held at NCR New Delhi, India, during March 20–21, 2021, jointly organized by Galgotias University, India, and Department of Information Engineering and Mathematics Università Di Siena, Italy. It discusses emerging topics pertaining to advanced computing, intelligent technologies, and networks including AI and machine learning, data mining, big data analytics, high-performance computing network performance analysis, Internet of things networks, wireless sensor networks, and others. The book offers a valuable asset for researchers from both academia and industries involved in advanced studies.
This paper presents a non-patient-specific methodology to offer a comparative analysis of the epileptic seizure prediction techniques using various machine learning classifiers based on the features extracted from electroencephalogram (EEG) signals. This methodology can be divided into subsequent stages of channel selection, feature extraction, feature selection, and prediction phase. The channel selection was implemented by employing the Boruta algorithm. The best performing channels were chosen. In feature extraction we investigated three fundamental roads: extracting statistical features from the raw EEG signals (mean variance, mean skewness, and mean kurtosis), performing empirical mode decomposition on raw EEG data to generate intrinsic mode functions and extracting statistical features from the generated intrinsic mode functions, and calculating the power spectral density of all channels partitioned into five frequency bands. These extracted features were then tested for their efficacy using the Boruta algorithm, resulting in the most desirable ones being selected. At last, in the prediction phase, the resultant dataset is tried on different machine learning classifiers: support vector machines (SVM), random forest, K-nearest neighbors (KNN), and LSTM. The sensitivities were tabulated to offer a comparative analysis.
The comparative analysis of machine learning methods has performed to solve the problem of early detection and prediction of epileptic seizures using electroencephalographic signals. Recent studies has shown that it is possible to predict seizures in prior of its physical appearance. Our goal is to present and analyse different approaches of seizure prediction techniques, particulary in machine learning and deep learning. Seizure prediction has made important advances over the last decade, nevertheless it is still a problem to provide steady algorithm of seizure early detection. Also, within individual patients exhibit distinctive dynamics, is it cruicial to find algorithms providing greater clinical utility. This article focuses of the problem of features development from electroencephalography signals in order to provide the accurate pattern recognition techniques for detection and classification of epilepsy seizures in advance. The mathematical model of the algorithms is constructed and quantitative data presented for estimating the methods efficiency.
In the study of detection of an epileptic seizure using Electroencephalogram (EEG), pattern recognition has been recognized as a valued tool. In this pattern recognition study, the first time the authors have attempted to use time domain (TD) features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived directly from filtered EEG data and from discrete wavelet transform (DWT) of filtered EEG data for the detection of an epileptic seizure. Further, the authors attempted to study the performance of other time domain features such as mean absolute value (MAV), standard deviation (SD), average power (AVP), which had been attempted by other researchers. The performance of the TD features is studied using naïve Bayes (NB) and support vector machines (SVM) classifiers for the university of Bonn database with fourteen different combinations of set E with set A to D. The proposed scheme was also compared with other existing scheme in the literature. The implementation results showed that the proposed scheme could attain the highest accuracy of 100% for normal eyes open and epileptic data set with direct as well as DWT based TD features. For other data sets, the highest accuracy is obtained with DWT based TD features using SVM. However, no significant difference in the classification of 14 data sets with TD features filtered EEG data and from DWT of filtered EEG data.
Over several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians’ encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required. So, an attempt has been made to review on the pattern detection methods for epilepsy seizure detection from EEG signals. More than 150 research papers have been discussed to determine the techniques for detecting epileptic seizures. Further, the literature review confirms that the pattern recognition techniques required to detect epileptic seizures varies across the electroencephalogram (EEG) datasets of different conditions. This is mostly owing to the fact that EEG detected under different conditions have different characteristics. This consecutively necessitates the identification of the pattern recognition technique to efficiently differentiate EEG epileptic data from the EEG data of various conditions.
Electroconvulsive therapy (ECT) is an effective and widely used treatment for major depressive disorder, in which a brief electric current is passed through the brain to trigger a brief seizure. This study aims to identify seizure quality rating by utilizing a set of seizure parameters. We used 750 ECT EEG recordings in this experiment. Four seizure related parameters, (time of slowing, regularity, stereotypy and post-ictal suppression) are used as inputs to two classifiers, decision tree and fuzzy inference system (FIS), to predict seizure quality ratings. The two classifiers produced encouraging results with error rate of 0.31 and 0.25 for FIS and decision tree, respectively. The classification results show that the four seizure parameters provide relevant information about the rating of seizure quality. Automatic scoring of seizure quality may be beneficial to clinicians working in this field.
ResearchGate has not been able to resolve any references for this publication.