Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders

Chao Chen, Xuequan Zhu, Abdelkader Nasreddine Belkacem, Lin Lu, Long Hao, Jia You, Duk Shin, Wenjun Tan, Zhaoyang Huang, Dong Ming

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nowadays, Sleep disorder is a common disease, and spindle spindles are important features of the second stage non-rapid eye movement (NREM) sleep. In this paper, we propose an improved automatic detection method of spindles based on wavelet transform. The spindles automatic detector is mainly composed of wavelet transform and clustering. We collected the electroencephalography (EEG) signals of six patients with sleep disorders all night for ten hours, and then preprocessed the data and other operations, and then used our improved method to detect the sleep EEG signals by spindles. By comparing with the previous automatic detection method not improved and another automatic detection method, the results show that the accuracy of sleep spindles detection can be effectively improved. The accuracy of the improved detector is 5.19% higher than before, and 9.7% higher than that of another method based on amplitude threshold. Finally, we made a simple comparison between people with sleep disorders and normal people. We found that there were significant differences in spindle density between people with sleep disorders and people without sleep disorders. The average spindle density in the normal population averaged 2.59 spindles per minute. People with sleep disorders had an average spindle density of 1.32 spindles per minute. In future research, our research direction is to improve the accuracy of spindles automatic detection by improving the spindles detector and study the difference of spindles between patients with sleep disorders and normal people in a large number of samples, so that the difference of spindles can be used as the basis for the diagnosis of sleep disorders.

Original languageEnglish
Title of host publicationHuman Brain and Artificial Intelligence - Second International Workshop, HBAI 2020, Held in Conjunction with IJCAI-PRICAI 2020, Revised Selected Papers
EditorsYueming Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages113-124
Number of pages12
ISBN (Print)9789811612879
DOIs
Publication statusPublished - 2021
Event2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020 - Yokohama, Japan
Duration: Jan 7 2021Jan 7 2021

Publication series

NameCommunications in Computer and Information Science
Volume1369 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020
Country/TerritoryJapan
CityYokohama
Period1/7/211/7/21

Keywords

  • Automatic detection
  • EEG
  • Sleep disorders
  • Sleep spindles
  • Wavelet transform and clustering

ASJC Scopus subject areas

  • General Computer Science
  • General Mathematics

Fingerprint

Dive into the research topics of 'Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders'. Together they form a unique fingerprint.

Cite this