The study aims to assess the changes in Heart Rate Variability (HRV) indexes in healthy subjects whi le driving in a real environment in order to detect drowsiness. The ECG of ten professional drivers was acquired while driving on routes familiar to the subjects. RR time series were quantified using a sliding window of 300 beats. Mean (mRR), standard devi ation (SDNN), standard deviation of the differentiated time series (rmsDD), power of the low (PLF) and high (PHF) frequency bands as well as the ratio LF/HF were computed. In addition, the median frequency of the power spectrum (MEDF), the bandwidth that c ontains the 50% of the power (BW50) and a measure of the asymmetry of the spectrum (AFS) were obtained. Moreover, the Hurst exponent estimated by fractional differintegration (HFDI) and the short scaling exponent obtained by detrended fluctuation analysis (α 1 ) were computed. Two observers classified the state of the drivers minute by minute by inspection of video recordings as alert or drowsy driver. Five subjects were alert for the whole recording while the others presented one or more periods of drowsines s in seven recordings between resting stops. There are not significant differences between groups for all indexes but BW50 (p<0.05). Nevertheless paired tests comparing drowsy and alert periods show significant differences (p<0.05) for SDNN, HFDI, mRR, BW 50 , AFS, α 1 , LF/HF and MEDF.

Peer Reviewed