Sleep Apnea Detection through ECG Signal Features for Polysomnography

Nishanth, T and Kamath, Surekha and Bhat, Devadas (2018) Sleep Apnea Detection through ECG Signal Features for Polysomnography. Journal of Advanced Research in Dynamical and Control Systems. pp. 127-133. ISSN 1943-023X

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In present era large number of people suffers from sleep disorders. Polysomnography (PSG) is the gold standard for sleep apnea detection. But due to the large number of wired connections the construction will disturb the patients those who are admitted. And also due to high cost and large size said sleep apnea devices are not suitable for sleep recording at home. This paper proposes the design of a low cost PSG system that is used to capture the ECG parameter and also the obtained ECG parameters can be used to detect sleep apnea. The features of recorded ECG as well as ECG database are used to detect the sleep apnea. The QRS detection algorithm is implemented and extracted some features of ECG signal. The KNN classifier was implemented and successfully trained and tested with normal and abnormal ECG features. The KNN classifier was found to work The KNN classifier was found to work with an accuracy of 93.75%.

Item Type: Article
Uncontrolled Keywords: Comfortableness, Polysomnography (PSG), Sleep Disorder, Wireless Synchronization
Subjects: Engineering > MIT Manipal > Biomedical
Engineering > MIT Manipal > Instrumentation and Control
Depositing User: MIT Library
Date Deposited: 06 Jul 2018 11:06
Last Modified: 06 Jul 2018 11:06

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