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Vol. 211, Issue 4, April 2017, pp. 43-50




Chebyshev and Modified Wavelet Algorithm Based Sleep Arousals Detection Using EEG Sensor Database

Mahalaxmi U. S. B. K. and Ramesh Patnaik M.

Department of Instrument Technology, Andhra University, India
Tel.: 9490950823

E-mail: aumahalakshmi@gmail.com


Received: 7 April 2017 /Accepted: 28 April 2017 /Published: 30 April 2017

Digital Sensors and Sensor Sysstems


Abstract: Electroencephalographic (EEG) arousals are generally observed in EEG recordings as an awakening response of the human brain. Sleep apnea is a major sleep disorder. The patients, with Severe Sleep Apnea (SAS) suffers from frequent interruptions in their sleep which brings about EEG arousals. In this paper, a new method for Segmentation and Filtering process of EEG sensor database signals for finding sleep arousals using Chebyshev and Modified Wavelet Algorithm is proposed. The Segmentation Algorithm appears as various features extracted from EEG Data’s and PSG Recordings. The Chebyshev Equiripple Filter is used in Filtering algorithm and then MSVM [M-Support Vector Machine] was utilized as Classification Tool. Algorithms are performed and different features are extracted and the ROC characteristics are performed. The extracted features are Delta, Gama, Beta, Alpha, Sigma of the EEG signal, EEG Signal Mean, EEG Signal Standard Deviation, EEG Signal Peak Signal to Noise Ratio [PSNR], and EEG Signal Normalization. MSVM tool showing EEG signals results.


Keywords: M-wavelet, Chebyshev Equiripple Filter, MW Coefficients, EEG database, MSVM classification tool, CELM, Confusion Matrix.


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