Denoising and Chaotic Feature Extraction of Electrocardial Signals for Driver Fatigue Detection by Kolmogorov Entropy

This paper proposes a detection method of driver fatigue by use of electrocardial signals. First, lifting wavelet transform (LWT) was used to reduce signal noise and its effect was confirmed by applying it to the denoising of a white-noise-mixed Lorenz signal. Second, phase space reconstruction was conducted for extracting chaotic features of the measured electrocardial signals. The phase diagrams show fractal geometry features even under a strong noise background. Finally, Kolmogorov entropy, which is a factor reflecting the uncertainty in and the chaotic level of a nonlinear dynamic system, was used as an indicator of driver fatigue. The effectiveness of Kolmogorov entropy in the judgment of driver fatigue was confirmed by comparison with a semantic differential (SD) subjective evaluation experiment. It was demonstrated that Kolmogorov entropy has a strong relationship with driver fatigue. It decreases when fatigue occurs. Furthermore, the influences of delay time and sampling points on Kolmogorov entropy were investigated, since the two factors are important to the actual use of the proposed detection method. Delay time may have significant influence on fatigue determination, but sampling points are relatively inconsequential. This result indicates that real-time detection can be realized by selecting a reasonably small number of sampling points.