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Sleep apnea diagnosis via single channel ECG feature selection

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4 Author(s)
Guruler, H. ; Biomed. Eng., New Jersey Inst. of Technol., Newark, NJ, USA ; Sahin, M. ; Ordek, G. ; Ferikoglu, A.

This study presents the classification of obstructive sleep apnea (OSA) disease using commonly used features belonging to time-frequency and non-linear domain of heart rate variability (HRV) analysis and then proposes a better selection of features using correlation matrices (CMs).

Published in:

Bioengineering Conference (NEBEC), 2012 38th Annual Northeast

Date of Conference:

16-18 March 2012