Sleep spindle detection using time-frequency sparsity

Ankit Parekh, Ivan W. Selesnick, David M. Rapoport, Indu Ayappa

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

Abstract

This paper proposes an EEG processor for sleep spindle detection algorithms. It non-linearly separates the raw EEG signal into non-oscillatory transient and sustained rhythmic oscillation components using long and short windows for the short-time Fourier transform. The processor utilizes the fact that sleep spindles can be sparsely represented via the inverse of a short-time Fourier transform. Five sleep spindle detectors were tested on the EEG database with and without the proposed EEG processor. We achieved an improvement of 13.3% in the by-sample F1 score, and 13.9% in the by-sample Matthews Correlation Coefficient score of these algorithms when the processed EEG was used for spindle detection. The processor was able to improve the scores by reducing the number of false positive spindles and increasing the number of true positive spindles detected.

Original languageEnglish (US)
Title of host publication2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479981847
DOIs
StatePublished - Jan 6 2015
Event2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 - Philadelphia, United States
Duration: Dec 13 2014Dec 13 2014

Publication series

Name2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 - Proceedings

Other

Other2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014
CountryUnited States
CityPhiladelphia
Period12/13/1412/13/14

Keywords

  • Pursuit algorithms
  • Short time Fourier transform
  • convex optimization
  • spectrogram

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering

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