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EEG Seizure Detection from Compressive Measurements

  • Meenu RaniEmail author
  • S. B. Dhok
  • R. B. Deshmukh
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 587)

Abstract

Electroencephalogram (EEG) signal is a measure of electrical activity across the brain. For patients suffering from brain disorders like epilepsy, coma, sleep disorders, etc., this electrical activity is continuously monitored. For this, a minimum of 21 electrodes are required, which are placed across the scalp. These electrodes generate a lot of data to be processed for diagnosing the brain disease. Compressive sensing (CS), which is a newer sensing modality, has proved itself to be a better candidate for handling large amount of data to be as compared to the traditional sampling mechanism. CS generates far fewer samples than that suggested by Nyquist rate and still allows faithful reconstruction. The CS reconstruction employs complex nonlinear methods, which are very costly. Compressed signal processing (CSP), which is an advancement over CS, gives a direction to solve certain signal processing tasks from compressive measurements itself, without the need for reconstructing the original signal at all. In this paper, CSP has been used for detecting the presence or absence of epileptic seizure in the EEG signal. For this purpose, a feature extraction method is proposed for extracting the features from compressed EEG measurements. The performance of proposed method has been found to be surprisingly effective in this regard. All the experiments are done on the EEG database taken from physionet CHB-MIT using MATLAB.

Keywords

Compressive sensing Compressed signal processing EEG monitoring Random demodulator Feature extraction SVM 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Visvesvaraya National Institute of TechnologyNagpurIndia

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