Epileptic Seizures Prediction Using Deep Learning Techniques

Usman, Syed Muhammad; Khalid, Shehzad; Aslam, Muhammad

doi:10.1109/access.2020.2976866

Cited by 139 publications

(21 citation statements)

References 90 publications

(116 reference statements)

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“…An important factor in the prediction of epilepsy is the prediction time because it enables the delivery of warning signals to patients in a timely manner. The proposed combined system of MM-mDistEn and an ANN can send an alarm on average 26.73 min before the actual seizure starts according to the results from the experiments in all 24 subjects; therefore, the prediction time of the proposed method is earlier than that with the method using a CNN with SVM (Usman, Khalid & Aslam, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…The rectified linear unit (ReLU) activation function (Hahnloser et al, 2000) is used for the hidden layers to add nonlinearity and make strong robustness to clear the noise from the input data. The softmax activation function is selected for the output layer to classify the multiclass outputs, and interictal, preictal, and ictal states of the epileptic EEG signals (Usman, Khalid & Aslam, 2020). The networks for each patient are trained individually for all 24 subjects.…”

Section: Classification Of Epileptic Seizures From the Extracted Featuresmentioning

confidence: 99%

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Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

Aung¹,

Wongsawat²

2021

PeerJ Computer Science

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Epilepsy is a common neurological disease that affects a wide range of the world population and is not limited by age. Moreover, seizures can occur anytime and anywhere because of the sudden abnormal discharge of brain neurons, leading to malfunction. The seizures of approximately 30% of epilepsy patients cannot be treated with medicines or surgery; hence these patients would benefit from a seizure prediction system to live normal lives. Thus, a system that can predict a seizure before its onset could improve not only these patients’ social lives but also their safety. Numerous seizure prediction methods have already been proposed, but the performance measures of these methods are still inadequate for a complete prediction system. Here, a seizure prediction system is proposed by exploring the advantages of multivariate entropy, which can reflect the complexity of multivariate time series over multiple scales (frequencies), called multivariate multiscale modified-distribution entropy (MM-mDistEn), with an artificial neural network (ANN). The phase-space reconstruction and estimation of the probability density between vectors provide hidden complex information. The multivariate time series property of MM-mDistEn provides more understandable information within the multichannel data and makes it possible to predict of epilepsy. Moreover, the proposed method was tested with two different analyses: simulation data analysis proves that the proposed method has strong consistency over the different parameter selections, and the results from experimental data analysis showed that the proposed entropy combined with an ANN obtains performance measures of 98.66% accuracy, 91.82% sensitivity, 99.11% specificity, and 0.84 area under the curve (AUC) value. In addition, the seizure alarm system was applied as a postprocessing step for prediction purposes, and a false alarm rate of 0.014 per hour and an average prediction time of 26.73 min before seizure onset were achieved by the proposed method. Thus, the proposed entropy as a feature extraction method combined with an ANN can predict the ictal state of epilepsy, and the results show great potential for all epilepsy patients.

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Section: Discussionmentioning

confidence: 99%

Section: Classification Of Epileptic Seizures From the Extracted Featuresmentioning

confidence: 99%

Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

Aung¹,

Wongsawat²

2021

PeerJ Computer Science

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“…A new automatic feature fusion CNN model for epilepsy detection based on dilated convolution kernel was proposed in [140]. A DL method to detect interictal and preictal states of a patient was investigated in [141] to help in preventing epilepsy. A novel method of classification using an unsupervised FCM multiview clustering algorithm was proposed in [142] to make the system more efficient and robust than existing methods.…”

Section: ) Dl-based Approaches In Epilepsy Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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“…Welch's method was used to calculate the power spectral density (PSD) from which 12 features were extracted that were nourished into two classifiers, such as the SVM and the RF classifier, to refine the precision of 94% [80]. In [81], epileptic seizures were predicted by employing deep learning approaches combined with SVM classification. In [82], the recurrent CNN was applied to long-term scalp EEG signals to detect the epileptogenic region.…”

Section: Introductionmentioning

confidence: 99%

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

Prasanna

Subathra

Mohammed

et al. 2021

JPM

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Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.

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Epileptic Seizures Prediction Using Deep Learning Techniques

Cited by 139 publications

References 90 publications

Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

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