KEYWORDS: Electroencephalography, Feature extraction, Signal detection, Electromyography, Spindles, Fuzzy logic, Polysomnography, Signal processing, Linear filtering, Data modeling
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.
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