A Machine-Learning-Based Detection Method for Snoring and Coughing

Chun-Hung Yang,
Yung-Ming Kuo,
I-Chun Chen,
Fan-Min Lin,
Pau-Choo Chung,

Abstract


Poor sleep quality is a common disease for modern people. Snoring is one of the essential indicators to measure Obstructive Sleep Apnea (OSA). When sleeping, the number of episodes of snoring and coughing are related to the estimated sleep quality. This study proposes a method to detect snoring and coughing in patients when sleeping. The proposed method includes three stages. Firstly, the nightly sound data for a patient are segmented to each independent event. Secondly, the time domain signal is changed to a frequency domain signal by Fourier Transform, and then the features are extracted from the snoring and coughing episodes. Lastly, the Support Vector Machine (SVM) and the Hidden Markov Model (HMM) are used to recognize snoring and coughing. The result of our experiment demonstrates that this method has good detection performance.

Keywords


Coughing detection, Snoring detection, Machine learning, Hidden Markov Model, Support Vector Machine

Citation Format:
Chun-Hung Yang, Yung-Ming Kuo, I-Chun Chen, Fan-Min Lin, Pau-Choo Chung, "A Machine-Learning-Based Detection Method for Snoring and Coughing," Journal of Internet Technology, vol. 23, no. 6 , pp. 1233-1244, Nov. 2022.

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