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Detecting Early Signs of Drowsy Driving using an Artificial Neural Network on Electrocardiography Signals

Method: The ECG signals for forty-three participants (mean age: 23.1, SD: 1.6) were recorded while performing a simulator-based monotonous driving for 20 minutes, and the ECG for twenty participants (mean age: 23.2, SD: 1.3) who suffered drowsiness were used in further analysis. The three driver states (normal, fighting-off drowsiness, and drowsy) were determined through participant's subjective report and video recording analysis. Six ECG measures in time and frequency domains were derived from the ECG and pre-processed to compensate individual variations in heart response.