Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals

We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Heinze Christian [verfasserIn] Hütterer Constantin [verfasserIn] Schnupp Thomas [verfasserIn] Lenis Gustavo [verfasserIn] Golz Martin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	rr interval qt interval karolinska sleepiness scale driving simulation lomb-scargle periodogram support vector machine

Übergeordnetes Werk:	In: Current Directions in Biomedical Engineering - De Gruyter, 2016, 3(2017), 2, Seite 563-567
Übergeordnetes Werk:	volume:3 ; year:2017 ; number:2 ; pages:563-567

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1515/cdbme-2017-0117

Katalog-ID:	DOAJ014685647

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spelling	10.1515/cdbme-2017-0117 doi (DE-627)DOAJ014685647 (DE-599)DOAJ1ebf46a05de0469182c2ff985e8d8f8a DE-627 ger DE-627 rakwb eng Heinze Christian verfasserin aut Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness. rr interval qt interval karolinska sleepiness scale driving simulation lomb-scargle periodogram support vector machine Medicine R Hütterer Constantin verfasserin aut Schnupp Thomas verfasserin aut Lenis Gustavo verfasserin aut Golz Martin verfasserin aut In Current Directions in Biomedical Engineering De Gruyter, 2016 3(2017), 2, Seite 563-567 (DE-627)835382605 (DE-600)2835398-5 23645504 nnns volume:3 year:2017 number:2 pages:563-567 https://doi.org/10.1515/cdbme-2017-0117 kostenfrei https://doaj.org/article/1ebf46a05de0469182c2ff985e8d8f8a kostenfrei https://doi.org/10.1515/cdbme-2017-0117 kostenfrei https://doaj.org/toc/2364-5504 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2017 2 563-567
allfields_unstemmed	10.1515/cdbme-2017-0117 doi (DE-627)DOAJ014685647 (DE-599)DOAJ1ebf46a05de0469182c2ff985e8d8f8a DE-627 ger DE-627 rakwb eng Heinze Christian verfasserin aut Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness. rr interval qt interval karolinska sleepiness scale driving simulation lomb-scargle periodogram support vector machine Medicine R Hütterer Constantin verfasserin aut Schnupp Thomas verfasserin aut Lenis Gustavo verfasserin aut Golz Martin verfasserin aut In Current Directions in Biomedical Engineering De Gruyter, 2016 3(2017), 2, Seite 563-567 (DE-627)835382605 (DE-600)2835398-5 23645504 nnns volume:3 year:2017 number:2 pages:563-567 https://doi.org/10.1515/cdbme-2017-0117 kostenfrei https://doaj.org/article/1ebf46a05de0469182c2ff985e8d8f8a kostenfrei https://doi.org/10.1515/cdbme-2017-0117 kostenfrei https://doaj.org/toc/2364-5504 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2017 2 563-567
allfieldsGer	10.1515/cdbme-2017-0117 doi (DE-627)DOAJ014685647 (DE-599)DOAJ1ebf46a05de0469182c2ff985e8d8f8a DE-627 ger DE-627 rakwb eng Heinze Christian verfasserin aut Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness. rr interval qt interval karolinska sleepiness scale driving simulation lomb-scargle periodogram support vector machine Medicine R Hütterer Constantin verfasserin aut Schnupp Thomas verfasserin aut Lenis Gustavo verfasserin aut Golz Martin verfasserin aut In Current Directions in Biomedical Engineering De Gruyter, 2016 3(2017), 2, Seite 563-567 (DE-627)835382605 (DE-600)2835398-5 23645504 nnns volume:3 year:2017 number:2 pages:563-567 https://doi.org/10.1515/cdbme-2017-0117 kostenfrei https://doaj.org/article/1ebf46a05de0469182c2ff985e8d8f8a kostenfrei https://doi.org/10.1515/cdbme-2017-0117 kostenfrei https://doaj.org/toc/2364-5504 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2017 2 563-567
allfieldsSound	10.1515/cdbme-2017-0117 doi (DE-627)DOAJ014685647 (DE-599)DOAJ1ebf46a05de0469182c2ff985e8d8f8a DE-627 ger DE-627 rakwb eng Heinze Christian verfasserin aut Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness. rr interval qt interval karolinska sleepiness scale driving simulation lomb-scargle periodogram support vector machine Medicine R Hütterer Constantin verfasserin aut Schnupp Thomas verfasserin aut Lenis Gustavo verfasserin aut Golz Martin verfasserin aut In Current Directions in Biomedical Engineering De Gruyter, 2016 3(2017), 2, Seite 563-567 (DE-627)835382605 (DE-600)2835398-5 23645504 nnns volume:3 year:2017 number:2 pages:563-567 https://doi.org/10.1515/cdbme-2017-0117 kostenfrei https://doaj.org/article/1ebf46a05de0469182c2ff985e8d8f8a kostenfrei https://doi.org/10.1515/cdbme-2017-0117 kostenfrei https://doaj.org/toc/2364-5504 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 3 2017 2 563-567
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author	Heinze Christian
spellingShingle	Heinze Christian misc rr interval misc qt interval misc karolinska sleepiness scale misc driving simulation misc lomb-scargle periodogram misc support vector machine misc Medicine misc R Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals
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topic_title	Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals rr interval qt interval karolinska sleepiness scale driving simulation lomb-scargle periodogram support vector machine
topic	misc rr interval misc qt interval misc karolinska sleepiness scale misc driving simulation misc lomb-scargle periodogram misc support vector machine misc Medicine misc R
topic_unstemmed	misc rr interval misc qt interval misc karolinska sleepiness scale misc driving simulation misc lomb-scargle periodogram misc support vector machine misc Medicine misc R
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title	Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals
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author_browse	Heinze Christian Hütterer Constantin Schnupp Thomas Lenis Gustavo Golz Martin
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title_sort	drowsiness discrimination in an overnight driving simulation on the basis of rr and qt intervals
title_auth	Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals
abstract	We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness.
abstractGer	We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness.
abstract_unstemmed	We examined if ECG-based features are discrimi-native towards drowsiness. Twenty-five volunteers (19–32 years) completed 7×40 minutes of monotonous overnight driving simulation, designed to induce drowsiness. ECG (512 s-1) was recorded continuously; subjective ratings of drowsiness on the Karolinska sleepiness scale (KSS) were polled every five minutes. ECG recordings were divided into 5-min segments, each associated with the mean of one self- and two observer-KSS ratings. Those mean KSS values were binarized to obtain two classes not drowsy and drowsy. The Q-, R- and T-waves in the recordings were detected; R-peak positions were manually reviewed; the Q- and T-detection method was tested against the manual annotations of Physio-net’s QT database. Power spectral densities of RR intervals (RR-PSD) and quantiles of the empirical distribution of heart-rate corrected QTc intervals were estimated. Support-vector machines and random-holdout cross-validation were used for the estimation of the classification error. Using either RR-PSD or QTc features yielded mean test errors of 79.3 ± 0.3 % and 82.7 ± 0.5 %, respectively. Merging RR and QTc features improved the accuracy to 88.3 ± 0.2 %. QTc intervals of the class drowsy were generally prolonged com-pared to not drowsy. Our findings indicate that the inclusion of QT intervals contribute to the discrimination of driver sleepiness.
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title_short	Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals
url	https://doi.org/10.1515/cdbme-2017-0117 https://doaj.org/article/1ebf46a05de0469182c2ff985e8d8f8a https://doaj.org/toc/2364-5504
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author2	Hütterer Constantin Schnupp Thomas Lenis Gustavo Golz Martin
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Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals

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