Generalized Epileptic Seizure Prediction using Machine Learning Method

Abstract

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%.

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

“…Increasing scientific advancements in optimization methods, signal processing methods, machine learning and transfer learning, and deep learning in seizure and epilepsy classification have been observed (Chavan & Desai, 2023;Luo et al, 2022;Rasheed et al, 2020;Shoeibi et al, 2021;Singh & Malhotra, 2022c;Supriya et al, 2020;Tang et al, 2023). Newer studies have also focused on automatic epilepsy prediction (Altaf et al, 2023;Shuaicong et al, 2023;Singh & Malhotra, 2021a;Singh & Malhotra, 2021b;Wang et al, 2022;Xin et al, 2023). Recent concepts in meta-learning, short learning and meta-transfer learning have not been explored in this field.…”

Automatic Diagnosis of Epileptic Seizures using Entropy-based Features and Multimodel Deep Learning Approaches

“…Therefore, accurate diagnosis of epilepsy is crucial for effective therapy. Research into automatic systems for epilepsy detection [19][20][21] and seizure episode prediction [22,23] has been intensive throughout the past decade.…”

Detection of Epileptic Seizures Based-on Channel Fusion and Transformer Network in EEG Recordings

“…Over the past decade, many studies have been carried out with the aim of developing automatic systems for the detection of epilepsy [16], [17], [18] and towards the prediction of seizure episodes [19], [20], [21], [22]. Most studies exploit machine learning (ML) and deep learning (DL) algorithms to build classification models capable of detecting seizure patterns in EEG records.…”

“…2 illustrates the seizure segment which is red shaded, showing variability of the signals between EEG channels. Aiming to build a classifier for seizure detection, most researchers use interictal and ictal signals as input data [26], [30], [31], [32], [33], and other investigators use preictal and ictal stages as input data [34], [35], which is also used for researchers that intend to predict a seizure attack [28], [22]. Either using interictal and ictal or preictal and ictal to develop a seizure detector, the classifier is trained to learn how to discriminate between normal and abnormal signals, or nonseizure and seizure segments.…”