TY - JOUR
PY - 2019//
TI - Heart rate variability-based driver drowsiness detection and its validation with EEG
JO - IEEE transactions on bio-medical engineering
A1 - Fujiwara, Koichi
A1 - Abe, Erika
A1 - Kamata, Keisuke
A1 - Nakayama, Chikao
A1 - Suzuki, Yoko
A1 - Yamakawa, Toshitaka
A1 - Hiraoka, Toshihiro
A1 - Kano, Manabu
A1 - Sumi, Yukiyoshi
A1 - Masuda, Fumi
A1 - Matsuo, Masahiro
A1 - Kadotani, Hiroshi
SP - 1769
EP - 1778
VL - 66
IS - 6
N2 - 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 (ANS) and then HRV, which is defined as an RR interval (RRI) fluctuation on an electrocardiogram (ECG) trace. Eight HRV features are monitored for detecting changes in HRV by using multivariate statistical process control (MSPC) 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 drowsy 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 prevent accidents caused by drowsy driving. Language: en
LA - en
SN - 0018-9294
UR - http://dx.doi.org/10.1109/TBME.2018.2879346
ID - ref1
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