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Automated detection of arousal event with fuzzy entropy using physiological signals
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Paper Abstract

Analyzing physiological signals during sleep can assist experts in diagnosing sleep arousal. To overcome this timeconsuming manual work for medical technologists, in this work a multi task algorithm for automatic identifying sleep arousal events proposed. The algorithm contains two parts: feature extractions and classification. The feature extractions are made of two regular features of arousal and one proposed feature (fuzzy entropy). Fuzzy entropy highlights the possibilities of events. With this contribution and the rest, our result reaches a sensitivity of 0.903 and a specificity of 0.834.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117925 (14 August 2019); doi: 10.1117/12.2539930
Show Author Affiliations
Wenhai Tang, Tsinghua Univ. (China)
Zongqing Lu, Tsinghua Univ. (China)
Qingmin Liao, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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