Significant Low-dimensional Spectral-temporal Features for Seizure Detection

TL;DR

This paper introduces a novel low-dimensional spectral-temporal feature extraction method for seizure detection in EEG signals, achieving high accuracy and efficiency compared to existing approaches.

Contribution

The paper proposes the MS-WTC feature extraction technique, significantly reducing feature dimensionality while maintaining high detection performance in seizure classification.

Findings

Achieved 99.8% to 100% accuracy on benchmark datasets.

Obtained 94.7% mean accuracy on clinical EEG data.

Demonstrated reliability and efficiency of the MS-WTC features.

Abstract

Seizure onset detection in electroencephalography (EEG) signals is a challenging task due to the non-stereotyped seizure activities as well as their stochastic and non-stationary characteristics in nature. Joint spectral-temporal features are believed to contain sufficient and powerful feature information for absence seizure detection. However, the resulting high-dimensional features involve redundant information and require heavy computational load. Here, we discover significant low-dimensional spectral-temporal features in terms of mean-standard deviation of wavelet transform coefficient (MS-WTC), based on which a novel absence seizure detection framework is developed. The EEG signals are transformed into the spectral-temporal domain, with their low-dimensional features fed into a convolutional neural network. Superior detection performance is achieved on the widely-used benchmark dataset as well as a clinical dataset from the Chinese 301 Hospital. For the former, seven classification tasks were evaluated with the accuracy from 99.8% to 100.0%, while for the latter, the method achieved a mean accuracy of 94.7%, overwhelming other methods with low-dimensional temporal and spectral features. Experimental results on two seizure datasets demonstrate reliability, efficiency and stability of our proposed MS-WTC method, validating the significance of the extracted low-dimensional spectral-temporal features.