Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifier

TL;DR

This paper introduces a k-nearest neighbors classifier utilizing a t-location-scale distribution to detect spike-and-wave events in EEG signals, aiming to improve early epileptic seizure detection.

Contribution

It presents a novel combination of t-distribution-based feature extraction with KNN for EEG spike-and-wave detection, validated on real data.

Findings

High classification accuracy achieved

Improved sensitivity and specificity

Effective detection of epileptic spike-and-wave patterns

Abstract

Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, particularly the early detection of epileptic seizures. In this paper, we propose a k-nearest neighbors classification for epileptic EEG signals based on a t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity, and specificity.