Sleep stage classification for child patients using DeConvolutional Neural Network

作者：

Highlights：

• Our sleep stage classification targets can be used for sleep disorders detection and therapy for child patients, compared to many existing works that target adult patients.

• Our method using a DeConvolutional Neural Network (DCNN) can predict sleep stage labels on the timestamp level.

• The utilization of multiple channels of Polysomnography (PSG) recordings can improve the performance of sleep stage classification.

摘要

•Our sleep stage classification targets can be used for sleep disorders detection and therapy for child patients, compared to many existing works that target adult patients.•Our method using a DeConvolutional Neural Network (DCNN) can predict sleep stage labels on the timestamp level.•The utilization of multiple channels of Polysomnography (PSG) recordings can improve the performance of sleep stage classification.

论文关键词：Sleep stage classification,Child patients’ sleep data,Biomedical multivariate signal processing,Timestamp-based segmentation,Deconvolutional Neural Network

论文评审过程：Received 23 April 2020, Revised 8 October 2020, Accepted 27 October 2020, Available online 2 November 2020, Version of Record 16 November 2020.

论文官网地址：https://doi.org/10.1016/j.artmed.2020.101981