Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means

Abstract

Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) MSK-means algorithm to distinguish epileptic EEG signals and identify epileptic zones. The random initialization of the K-means algorithm can lead to wrong clusters. Based on the characteristics of EEGs, the MSK-means MSK-means algorithm initializes the coarse-scale centroid of a cluster with a suitable scale factor. In this chapter, the MSK-means algorithm is proved theoretically superior to the K-means algorithm on efficiency. In addition, three classifiers: the K-means, MSK-means MSK-means and support vector machine (SVM), are used to identify seizure and localize epileptogenic zone using delay permutation entropy features. The experimental results demonstrate that identifying seizure with the MSK-means algorithm and delay permutation entropy achieves 4. 7 % higher accuracy than that of K-means, and 0. 7 % higher accuracy than that of the SVM.

Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation

“…Moreover, PE is more robust than the zero-crossing rate to the signal length, although both have relatively similar theoretically notions [10]. These characteristics make the PE an appealing tool used in a large number of real world signal and image processing applications [14][15][16]. PE-based approaches have been used in various studies, such as distinguishing noise from chaos [17], dependences between time series [18], and econophysics [19] and physiological applications [20][21][22].…”

Differentiating Interictal and Ictal States in Childhood Absence Epilepsy through Permutation Rényi Entropy

“…Zhu et al [10] demonstrated, by experimental results, that PE is able to identify epileptic seizures in EEG and intracranial EEG (iEEG). The same authors proposed an unsupervised multi-scale K-means (MSK-means) algorithm to distinguish epileptic EEG signals and to identify epileptic zones [11]. Mateos et al [12] developed a method based on PE to characterize EEG from different stages in the treatment of a chronic epileptic patient.…”

Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time–Frequency EEG Images

“…EEG signals contain significant features that detail both regular and irregular brain activities, particularly epileptic seizures. In addition, high-temporal-resolution EEG data from the scalp, spanning multiple input channels, can be acquired through distributed continuous sensing techniques [7]. Traditionally, diagnosing epilepsy through visual analysis of EEG recordings, both clinically and conventionally, is labor-intensive and prone to error, with varying consistency among experts, because of its heavy reliance on human expertise and skill [8,9].…”