Key result
A Support Vector Machine classifier using combined Approximate Entropy and Sample Entropy measures from EEG signals achieved a classification accuracy of 91.28% for detecting driving fatigue at the P3 electrode.
A combined entropy measure using EEG signals and SVM can effectively classify driving fatigue states with high accuracy.
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May support EEG fatigue monitoring in drivers; leaves open prospective validation before adoption.
Xiong et al. (2016) studied Driving fatigue (n=60). Combined Approximate Entropy (AE) and Sample Entropy (SE) with Support Vector Machine (SVM) vs. Single complexity measure was evaluated on Classification accuracy of driving fatigue (drowsy vs alert states). A Support Vector Machine classifier using combined Approximate Entropy and Sample Entropy measures from EEG signals achieved a classification accuracy of 91.28% for detecting driving fatigue at the P3 electrode.
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