An automatic sleep spindle detector based on wavelets and the teager energy operator.

Beena Ahmed, Amira Redissi, Reza Tafreshi

Research output: Contribution to journalArticle

Abstract

Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel approach for the automatic detection of sleep spindles based upon the Teager Energy Operator and wavelet packets has been presented. The Teager operator was found to accurately enhance periodic activity in epochs of the EEG containing spindles. The wavelet packet transform proved effective in accurately locating spindles in the time-frequency domain. The autocorrelation function of the resultant Teager signal and the wavelet packet energy ratio were used to identify epochs with spindles. These two features were integrated into a spindle detection algorithm which achieved an accuracy of 93.7%.

Fingerprint

sleep
wavelet
Sleep
Detectors
energy
Electroencephalography
Wavelet Analysis
Sleep Stages
Eye Movements
Workload
Eye movements
Autocorrelation
autocorrelation
transform
detector

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

@article{2aa4b706a3aa49ac856297a63c02a4d0,
title = "An automatic sleep spindle detector based on wavelets and the teager energy operator.",
abstract = "Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel approach for the automatic detection of sleep spindles based upon the Teager Energy Operator and wavelet packets has been presented. The Teager operator was found to accurately enhance periodic activity in epochs of the EEG containing spindles. The wavelet packet transform proved effective in accurately locating spindles in the time-frequency domain. The autocorrelation function of the resultant Teager signal and the wavelet packet energy ratio were used to identify epochs with spindles. These two features were integrated into a spindle detection algorithm which achieved an accuracy of 93.7{\%}.",
author = "Beena Ahmed and Amira Redissi and Reza Tafreshi",
year = "2009",
language = "English",
pages = "2596--2599",
journal = "JAPCA",
issn = "1073-161X",
publisher = "Taylor and Francis Ltd.",

}

TY - JOUR

T1 - An automatic sleep spindle detector based on wavelets and the teager energy operator.

AU - Ahmed, Beena

AU - Redissi, Amira

AU - Tafreshi, Reza

PY - 2009

Y1 - 2009

N2 - Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel approach for the automatic detection of sleep spindles based upon the Teager Energy Operator and wavelet packets has been presented. The Teager operator was found to accurately enhance periodic activity in epochs of the EEG containing spindles. The wavelet packet transform proved effective in accurately locating spindles in the time-frequency domain. The autocorrelation function of the resultant Teager signal and the wavelet packet energy ratio were used to identify epochs with spindles. These two features were integrated into a spindle detection algorithm which achieved an accuracy of 93.7%.

AB - Sleep spindles are one of the most important short-lasting rhythmic events occurring in the EEG during Non-Rapid Eye Movement sleep. Their accurate identification in a polysomnographic signal is essential for sleep professionals to help them mark Stage 2 sleep. Visual spindle scoring however is a tedious workload, as there are often a thousand spindles in an all-night recording. In this paper a novel approach for the automatic detection of sleep spindles based upon the Teager Energy Operator and wavelet packets has been presented. The Teager operator was found to accurately enhance periodic activity in epochs of the EEG containing spindles. The wavelet packet transform proved effective in accurately locating spindles in the time-frequency domain. The autocorrelation function of the resultant Teager signal and the wavelet packet energy ratio were used to identify epochs with spindles. These two features were integrated into a spindle detection algorithm which achieved an accuracy of 93.7%.

UR - http://www.scopus.com/inward/record.url?scp=84903876259&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84903876259&partnerID=8YFLogxK

M3 - Article

C2 - 19965220

SP - 2596

EP - 2599

JO - JAPCA

JF - JAPCA

SN - 1073-161X

ER -