Review of Deep Learning Methods for Automated Sleep Staging

被引：10

作者：

Malekzadeh, Masoud ^{[1
]}

Hajibabaee, Parisa ^{[1
]}

Heidari, Maryam ^{[2
]}

Berlin, Brett ^{[2
]}

机构：

[1] Univ Massachusetts, Lowell, MA 01854 USA

[2] George Mason Univ, Fairfax, VA 22030 USA

来源：

2022 IEEE 12TH ANNUAL COMPUTING AND COMMUNICATION WORKSHOP AND CONFERENCE (CCWC) | 2022年

关键词：

Automated sleep staging; deep learning; transformer; convolutional layer; RESEARCH RESOURCE; COMPONENTS; AASM;

D O I：

10.1109/CCWC54503.2022.9720875

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In order to diagnose sleep problems, it is critical to correctly identify sleep stages which is a labor-intensive task. Due to rising data volumes, advanced algorithms, and improvements in computational power and storage, artificial intelligence has been more popular in recent years. Automated sleep staging through cardiac rhythm is one of the active research areas that has gained attention over the last decade. In this study, we review four recent state-of-the-art deep learning methods for automated sleep staging, datasets developed in recent years, and discuss their performance evaluations.

引用

页码：80 / 86

页数：7

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