@inproceedings{8b8547b0da3f4cd389f2cd2f46421f6e,
title = "Sleep spindle detection using time-frequency sparsity",
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.",
keywords = "Pursuit algorithms, Short time Fourier transform, convex optimization, spectrogram",
author = "Ankit Parekh and Selesnick, {Ivan W.} and Rapoport, {David M.} and Indu Ayappa",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 ; Conference date: 13-12-2014 Through 13-12-2014",
year = "2014",
doi = "10.1109/SPMB.2014.7002965",
language = "English (US)",
series = "2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2014 IEEE Signal Processing in Medicine and Biology Symposium, IEEE SPMB 2014 - Proceedings",
}