The role of expert evaluation for microsleep detection

Gölz, Martin; Schenka, A.; Sommer, David; Geißler, B.; Muttray, Axel

doi:10.1515/cdbme-2015-0024

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…Usually, microsleep episodes are detected by more than one human experts analysing EEGs or video streams of the participant's face [7]. Thus, the retrospective detection of MSE by loss of the pupillary signal might be limitation of Figure 1 Example pupil diameter time course (PD) before signal loss due to MSE.…”

Section: Discussionmentioning

confidence: 99%

Forecasting transient sleep episodes by pupil size variability

Schumann

Ebel

Bär

2017

Current Directions in Biomedical Engineering

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Abstract:The ability to predict when a person is about to fall asleep is an important challenge in recent biomedical research and has various possible applications. Sleepiness and fatigue are known to increase pupillary fluctuations and the occurrence of eye blinks. In this study, we evaluated the use of the pupil diameter to forecast sleep episodes of short duration (>1s). We conducted multi-channel physiological and pupillometric recordings (diameter, gaze position) in 91 healthy volunteers at rest in supine position. Although they were instructed to keep their eyes open, short sleep episodes were detected in 20 participants (16 males, age: 26.2±5.6 years), 53 events in total. Before each sleep event, pupil size was extracted in a window of 30s (without additional sleep event). Mean pupil diameter and its standard deviation, Shannon entropy and wavelet entropy in the first half (15s) were compared to the second half of the window (15s). Linear and nonlinear measures demonstrated an elevation of pupil size variability before sleep onset. Most obviously, WE and SD increased significantly from 0.054±0.056 and 0.38±0.16 mm to 0.113±0.103 (T(102)=2.44, p<0.001) and 0.46±0.18 mm (T(104)=3.67, p<0.05) in the second half of each analysis window. We were able to identify 83% of the pre-sleep segments by linear discriminant analysis. Although our data was acquired in an experimental condition, it suggests that pupillary unrest might be a suitable predictor of events related to transient sleep or inattentiveness. In the future, we are going to involve the other recorded physiological signals into the analysis.

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Section: Discussionmentioning

confidence: 99%

Forecasting transient sleep episodes by pupil size variability

Schumann

Ebel

Bär

2017

Current Directions in Biomedical Engineering

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“…MSE have always been determined based on observable behavioral characteristics, in particular prolonged eyelid closure and slow eye movement. This visual assessment was performed by a trained person with long experience in this field [3]. A total of 7903 events consisting of 3859 MSE and of 4044 counterexamples was drawn from recordings.…”

Section: Methodsmentioning

confidence: 99%

“…Machine learning methods have been successfully used for this purpose [1,2]. Because these methods are based solely on the given data set and on assumptions about the underlying data generating, stochastic processes, great care must be taken when selecting data [3]. Therefore, one research focus should be in validation.…”

Section: Introductionmentioning

confidence: 99%

Inter-individual variability of EEG features during microsleep events

Gölz

Schenka

Haselbeck

et al. 2019

Current Directions in Biomedical Engineering

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This paper examines the question of how strongly the spectral properties of the EEG during microsleep differ between individuals. For this purpose, 3859 microsleep examples were compared with 4044 counterexamples in which drivers were very drowsy but were able to perform the driving task. Two types of signal features were compared: logarithmic power spectral densities and entropy measures of wavelets coefficient series. Discriminant analyses were performed with the following machine learning methods: support-vector machines, gradient boosting, learning vector quantization. To the best of our knowledge, this is the first time that results of the leave-one-subject-out cross-validation (LOSO CV) for the detection of microsleep are presented. Error rates lower than 5.0 % resulted in 17 subjects and lower than 13 % in another 11 subjects. In 3 individuals, EEG features could not be explained by the pool of EEG features of all other individuals; for them, detection errors were 15.1 %, 17.1 %, and 27.0 %. In comparison, cross validation by means of repeated random subsampling, in which individuality is not considered, yielded mean error rates of 5.0 ± 0.5 %. A subsequent inspection of raw EEG data showed that in two individuals a bad signal quality due to poor electrode attachment could be the cause and in one individual a very unusual behavior, a high and long-lasting eyelid activity which interfered the recorded EEG in all channels.

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The role of expert evaluation for microsleep detection

Cited by 2 publications

References 9 publications

Forecasting transient sleep episodes by pupil size variability

Forecasting transient sleep episodes by pupil size variability

Inter-individual variability of EEG features during microsleep events

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