Real-Time ECG-Based Detection of Fatigue Driving Using Sample Entropy

Abstract

In present work, the heart rate variability (HRV) characteristics, calculated by sample entropy (SampEn), were used to analyze the driving fatigue state at successive driving stages. Combined with the relative power spectrum ratio β/(θ + α), subjective questionnaire, and brain network parameters of electroencephalogram (EEG) signals, the relationships between the different characteristics for driving fatigue were discussed. Thus, it can conclude that the HRV characteristics (RR SampEn and R peaks SampEn), as well as the relative power spectrum ratio β/(θ + α) of the channels (C3, C4, P3, P4), the subjective questionnaire, and the brain network parameters, can effectively detect driving fatigue at various driving stages. In addition, the method for collecting ECG signals from the palm part does not need patch electrodes, is convenient, and will be practical to use in actual driving situations in the future.

EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical network

“…52 In the study of nerve activity, the synchronization algorithms for network reconstruction were effectively used. 53,54 In our study, the activities of the neuronal clusters suppressed when a driver was in a state of mental fatigue. At this time, their activities were relatively consistent.…”

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

“…Whereas in [28], the authors used non-visuals EEG signals to develop the DDD system. The EEG signals were also used in paper [20] to detect fatigue. In that study, the authors used a combined approach of SVM and deep-learning algorithms.…”

Research on Home-Auxiliary Robot System Based on Characteristics of Human Physiological and Motion Signals

“…Research by Stam and Reijneveld showed that the modern complex network theory was widely used to simulate human brain function [35]. Some studies have proved that the brain network connection could effectively express brain function and related neural activities [36][37][38][39][40]. In this paper, the authors use the brain network features to recognize eye movements.…”