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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1233-1244
Number of pages12
JournalJournal of Internet Technology
Volume23
Issue number6
DOIs
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'A Machine-Learning-Based Detection Method for Snoring and Coughing'. Together they form a unique fingerprint.

Cite this