6533b853fe1ef96bd12ac232
RESEARCH PRODUCT
The role of expert evaluation for microsleep detection
David SommerAxel MuttrayB. GeißlerMartin GolzA. Schenkasubject
driving simulationmicrosleepMicrosleepArtificial neural networkmedicine.diagnostic_testComputer sciencebusiness.industryBiomedical EngineeringRElectroencephalographysupport-vector machinesMachine learningcomputer.software_genresleepinessneural networksSupport vector machineeogExpert evaluationmedicineDriving simulationMedicineArtificial intelligenceeegbusinesscomputerdescription
Abstract Recently, it has been shown by overnight driving simulation studies that microsleep density is the only known sleepiness indicator which rapidly increases within a few seconds immediately before sleepiness related crashes. This indicator is based solely on EEG and EOG and subsequent adaptive pattern recognition. Accurate microsleep recognition is very important for the performance of this sleepiness indicator. The question is whether expensive evaluations of microsleep events by a) experts are necessary or b) non-experts provide sufficient evaluations. Based on 11,114 microsleep events in case a) and 12,787 in case b) recognition accuracies were investigated utilizing (i) artificial neural networks and (ii) support-vector machines. Cross validated classification accuracies ranged between 92.2 % for (i,b) and 99.3 % for (ii, a). It is concluded that expert evaluations are very important to provide independent information for detecting microsleep.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2015-09-01 | Current Directions in Biomedical Engineering |