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- W2753092257 abstract "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." @default.
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- W2753092257 date "2017-09-01" @default.
- W2753092257 modified "2023-09-25" @default.
- W2753092257 title "Drowsiness discrimination in an overnight driving simulation on the basis of RR and QT intervals" @default.
- W2753092257 doi "https://doi.org/10.1515/cdbme-2017-0117" @default.
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