EOG feature relevance determination for microsleep detection

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Abstract: Automatic relevance determination (ARD) was applied to two-channel EOG recordings for microsleep event (MSE) recognition. 10 s immediately before MSE and also before counterexamples of fatigued, but attentive driving were analysed. Two type of signal features were extracted: the maximum cross correlation (MaxCC) and logarithmic power spectral densities (PSD) averaged in spectral bands of 0.5 Hz width ranging between 0 and 8 Hz. Generalised learn-ing vector quantisation (GRLVQ) was used as ARD method to show the potential of feature reduction. This is compared to support-vector machines (SVM), in which the feature reduction plays a much smaller role. Cross validation yielded mean normalised relevancies of PSD features in the range of 1.6 - 4.9 % and 1.9 - 10.4 % for horizontal and vertical EOG, respectively. MaxCC relevancies were 0.002 - 0.006 % and 0.002 - 0.06 %, respectively. This shows that PSD features of vertical EOG are indispensable, whereas MaxCC can be neglected. Mean classification accuracies were estimated at 86.6±b 1.3 % and 92.3±b 0.2 % for GRLVQ and SVM, respec-tively. GRLVQ permits objective feature reduction by inclu-sion of all processing stages, but is not as accurate as SVM.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: EOG feature relevance determination for microsleep detection ; volume:3 ; number:2 ; year:2017 ; pages:815-818 ; extent:4
Current directions in biomedical engineering ; 3, Heft 2 (2017), 815-818 (gesamt 4)

Creator: Golz, Martin
Wollner, Sebastian
Sommer, David
Schnieder, Sebastian

DOI: 10.1515/cdbme-2017-0172

URN: urn:nbn:de:101:1-2023030713172106019353

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:47 AM CEST

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Golz, Martin
Wollner, Sebastian
Sommer, David
Schnieder, Sebastian

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EOG feature relevance determination for microsleep detection

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