Classification of snoring sound based on a recurrent neural network

作者：

Highlights：

• The length, frequency, and period of snoring episodes (SE) differ per individual.

• We proposed a classifier for snoring based on a recurrent neural network.

• The classifier differentiated SEs from non-SEs by learning episode features.

• The method can be divided to segmentation, feature extraction, and classification.

• Despite the small dataset, the proposed classifier showed extremely high accuracy.

摘要

•The length, frequency, and period of snoring episodes (SE) differ per individual.•We proposed a classifier for snoring based on a recurrent neural network.•The classifier differentiated SEs from non-SEs by learning episode features.•The method can be divided to segmentation, feature extraction, and classification.•Despite the small dataset, the proposed classifier showed extremely high accuracy.

论文关键词：Snoring/non-snoring episodes,Recurrent neural network,Sleep disorder

论文评审过程：Received 4 June 2018, Revised 8 December 2018, Accepted 5 January 2019, Available online 7 January 2019, Version of Record 21 January 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.01.020