The proposed HRV-based algorithm successfully detected drowsiness in 12 out of 13 pre-N1 episodes prior to sleep onset, with a false positive rate of 1.7 times per hour.
Observational (n=34)
No
Does an HRV-based algorithm accurately detect driver drowsiness compared to EEG-based sleep scoring in driving simulator participants?
An HRV-based anomaly detection algorithm can effectively detect driver drowsiness prior to sleep onset, offering a potential method to prevent drowsy driving accidents.
OBJECTIVE: Driver drowsiness detection is a key technology that can prevent fatal car accidents caused by drowsy driving. The present work proposes a driver drowsiness detection algorithm based on heart rate variability (HRV) analysis and validates the proposed method by comparing with electroencephalography (EEG)-based sleep scoring. METHODS: Changes in sleep condition affect the autonomic nervous system and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control, which is a well known anomaly detection method. RESULT: The performance of the proposed algorithm was evaluated through an experiment using a driving simulator. In this experiment, RRI data were measured from 34 participants during driving, and their sleep onsets were determined based on the EEG data by a sleep specialist. The validation result of the experimental data with the EEG data showed that drowsiness was detected in 12 out of 13 pre-N1 episodes prior to the sleep onsets, and the false positive rate was 1.7 times per hour. CONCLUSION: The present work also demonstrates the usefulness of the framework of HRV-based anomaly detection that was originally proposed for epileptic seizure prediction. SIGNIFICANCE: The proposed method can contribute to preventing accidents caused by drowsy driving.
Fujiwara et al. (Mon,) conducted a observational in Healthy drivers (n=34). HRV-based driver drowsiness detection algorithm vs. EEG-based sleep scoring was evaluated on Detection of drowsiness prior to sleep onset (pre-N1 episodes). The proposed HRV-based algorithm successfully detected drowsiness in 12 out of 13 pre-N1 episodes prior to sleep onset, with a false positive rate of 1.7 times per hour.