Epileptic seizure detection in EEG signals using tunable Q factor wavelet transform and bootstrap aggregating
BACKGROUND AND OBJECTIVE Epileptic seizure detection is traditionally performed by expert clinicians based on visual observation of EEG signals. This process is time consuming, burdensome, reliant on expensive human resources, and subject to error and bias. In epilepsy research, on the other hand, manual detection is unsuitable for handling large data sets. A computerized seizure identification scheme can eradicate the aforementioned problems, aid clinicians, and benefit epilepsy research. METHODS In this work, a new automated epilepsy diagnosis scheme based on Tunable Q factor wavelet transform (TQWT) and bootstrap aggregating (Bagging) using Electroencephalogram (EEG) signals is proposed. Until now, this is the first time spectral features in the TQWT domain in conjunction with Bagging are employed for epilepsy seizure identification to the best of the authors' knowledge. At first, we decompose the EEG signal segments into sub bands using TQWT. We then extract various spectral features from the TQWT sub bands. The suitability of spectral features in the TQWT domain is established through statistical measures and graphical analyses. Afterwards, Bagging is employed for epileptic seizure classification. The efficacy of Bagging in the proposed detection scheme is also studied in this research. The effects of various TQWT and Bagging parameters are investigated. The optimal choices of these parameters are also determined. The performance of the proposed scheme is studied using a publicly available benchmark EEG database for various classification cases that include inter ictal (seizure free interval) ictal (seizure) and healthy; seizure and non seizure; ictal and inter ictal; and seizure and healthy. RESULTS In comparison with the state of the art algorithms, the performance of the proposed method is superior in terms of sensitivity, specificity, and accuracy. CONCLUSION The seizure detection method proposed herein therefore can alleviate the burden of medical professionals of analyzing a large bulk of data by visual inspection, speed up epilepsy diagnosis and benefit epilepsy research.
Comput. Methods Programs Biomed.