A new epileptic seizure prediction model based on maximal overlap discrete wavelet packet transform, homogeneity index, and machine learning using ECG signals

Epilepsy, a complex pathology with various etiological origins, is characterized by producing hyperexcitability in the brain, which can have multiple disruptive symptoms. It impacts about 40 million people worldwide, of which 20 to 30% have chronic and intractable seizures. Each seizure can create hazardous situations for patients resulting from fractures, burns, submersion accidents, and soft-tissue injuries. Therefore, a method capable of predicting a seizure with sufficient window time before its onset is highly desirable because it will allow the patient to locate a safe place or take appropriate precautionary actions. In this article, a novel method is pre-sented through adroit integration of maximal overlap wavelet packet transform, homogeneity index, and a K-Nearest Neighbors classifier to predict an epileptic event twenty minutes before its onset using electrocardiogram (ECG) signals. The method's effectiveness for predicting an epileptic seizure is verified by employing a database provided by the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH), which includes seven patients with ten epileptic seizures. The results show that the proposed method effectively predicts an epileptic seizure 20 min prior to its onset with an accuracy of 93.25%.

Automated summary

Epilepsy, a complex neurological disorder, affects millions of people worldwide. Predicting epileptic seizures is crucial for patient safety. This article presents a novel method using electrocardiogram signals to predict seizures with 93.25% accuracy, twenty minutes before their onset.