Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis
A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying m...
Main Authors: | , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2015-07-01
|
Series: | Frontiers in Human Neuroscience |
Subjects: | |
Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00414/full |
id |
doaj-c6272a5260e5435f8dafaf3f3b8e56fe |
---|---|
record_format |
Article |
spelling |
doaj-c6272a5260e5435f8dafaf3f3b8e56fe2020-11-25T03:12:39ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612015-07-01910.3389/fnhum.2015.00414137727Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysisTarek eLajnef0Sahbi eChaibi1Jean-Baptiste eEichenlaub2Perrine Marie Ruby3Pierre-Emmanuel eAguera4Mounir eSamet5Abdennaceur eKachouri6Karim eJerbi7Karim eJerbi8University of SfaxUniversity of SfaxHarvard Medical SchoolINSERM U1028, UMR 5292, University Lyon IINSERM U1028, UMR 5292, University Lyon IUniversity of SfaxUniversity of SfaxUniversity of MontrealINSERM U1028, UMR 5292, University Lyon IA novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse representation of EEG segments obtained by the recently introduced discrete tunable Q-factor wavelet transform (TQWT). Tuning the Q-factor provides a convenient and elegant tool to naturally decompose the signal into an oscillatory and a transient component. The actual detection step relies on thresholding (i) the transient component to reveal K-complexes and (ii) the time-frequency representation of the oscillatory component to identify sleep spindles. Optimal thresholds are derived from ROC-like curves (sensitivity versus FDR) on training sets and the performance of the method is assessed on test data sets. We assessed the performance of our method using full-night sleep EEG data we collected from 14 participants. In comparison to visual scoring (Expert 1), the proposed method detected spindles with a sensitivity of 83.18% and false discovery rate (FDR) of 39%, while K-complexes were detected with a sensitivity of 81.57% and an FDR of 29.54%. Similar performances were obtained when using a second expert as benchmark. In addition, when the TQWT and MCA steps were excluded from the pipeline the detection sensitivities dropped down to 70% for spindles and to 76.97% for K-complexes, while the FDR rose up to 43.62% and 49.09% respectively. Finally, we also evaluated the performance of the proposed method on a set of publicly available sleep EEG recordings. Overall, the results we obtained suggest that the TQWT-MCA method may be a valuable alternative to existing spindle and K-complex detection methods. Paths for improvements and further validations with large-scale standard open-access benchmarking data sets are discussed.http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00414/fullSleepElectroencephalography (EEG)Neural oscillationsK-complexSensitivitySleep Spindles |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tarek eLajnef Sahbi eChaibi Jean-Baptiste eEichenlaub Perrine Marie Ruby Pierre-Emmanuel eAguera Mounir eSamet Abdennaceur eKachouri Karim eJerbi Karim eJerbi |
spellingShingle |
Tarek eLajnef Sahbi eChaibi Jean-Baptiste eEichenlaub Perrine Marie Ruby Pierre-Emmanuel eAguera Mounir eSamet Abdennaceur eKachouri Karim eJerbi Karim eJerbi Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis Frontiers in Human Neuroscience Sleep Electroencephalography (EEG) Neural oscillations K-complex Sensitivity Sleep Spindles |
author_facet |
Tarek eLajnef Sahbi eChaibi Jean-Baptiste eEichenlaub Perrine Marie Ruby Pierre-Emmanuel eAguera Mounir eSamet Abdennaceur eKachouri Karim eJerbi Karim eJerbi |
author_sort |
Tarek eLajnef |
title |
Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis |
title_short |
Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis |
title_full |
Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis |
title_fullStr |
Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis |
title_full_unstemmed |
Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis |
title_sort |
sleep spindle and k-complex detection using tunable q-factor wavelet transform and morphological component analysis |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Human Neuroscience |
issn |
1662-5161 |
publishDate |
2015-07-01 |
description |
A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse representation of EEG segments obtained by the recently introduced discrete tunable Q-factor wavelet transform (TQWT). Tuning the Q-factor provides a convenient and elegant tool to naturally decompose the signal into an oscillatory and a transient component. The actual detection step relies on thresholding (i) the transient component to reveal K-complexes and (ii) the time-frequency representation of the oscillatory component to identify sleep spindles. Optimal thresholds are derived from ROC-like curves (sensitivity versus FDR) on training sets and the performance of the method is assessed on test data sets. We assessed the performance of our method using full-night sleep EEG data we collected from 14 participants. In comparison to visual scoring (Expert 1), the proposed method detected spindles with a sensitivity of 83.18% and false discovery rate (FDR) of 39%, while K-complexes were detected with a sensitivity of 81.57% and an FDR of 29.54%. Similar performances were obtained when using a second expert as benchmark. In addition, when the TQWT and MCA steps were excluded from the pipeline the detection sensitivities dropped down to 70% for spindles and to 76.97% for K-complexes, while the FDR rose up to 43.62% and 49.09% respectively. Finally, we also evaluated the performance of the proposed method on a set of publicly available sleep EEG recordings. Overall, the results we obtained suggest that the TQWT-MCA method may be a valuable alternative to existing spindle and K-complex detection methods. Paths for improvements and further validations with large-scale standard open-access benchmarking data sets are discussed. |
topic |
Sleep Electroencephalography (EEG) Neural oscillations K-complex Sensitivity Sleep Spindles |
url |
http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00414/full |
work_keys_str_mv |
AT tarekelajnef sleepspindleandkcomplexdetectionusingtunableqfactorwavelettransformandmorphologicalcomponentanalysis AT sahbiechaibi sleepspindleandkcomplexdetectionusingtunableqfactorwavelettransformandmorphologicalcomponentanalysis AT jeanbaptisteeeichenlaub sleepspindleandkcomplexdetectionusingtunableqfactorwavelettransformandmorphologicalcomponentanalysis AT perrinemarieruby sleepspindleandkcomplexdetectionusingtunableqfactorwavelettransformandmorphologicalcomponentanalysis AT pierreemmanueleaguera sleepspindleandkcomplexdetectionusingtunableqfactorwavelettransformandmorphologicalcomponentanalysis AT mouniresamet sleepspindleandkcomplexdetectionusingtunableqfactorwavelettransformandmorphologicalcomponentanalysis AT abdennaceurekachouri sleepspindleandkcomplexdetectionusingtunableqfactorwavelettransformandmorphologicalcomponentanalysis AT karimejerbi sleepspindleandkcomplexdetectionusingtunableqfactorwavelettransformandmorphologicalcomponentanalysis AT karimejerbi sleepspindleandkcomplexdetectionusingtunableqfactorwavelettransformandmorphologicalcomponentanalysis |
_version_ |
1724649231867707392 |