Analysis on power spectrum and base-scale entropy for heart rate variability signals modulated by reversed sleep state

Da-Zhao, Liu; Wang, Jun; Li, Jin; Yu, Liang; Xu, Weiting; Zhao, Xin

doi:10.7498/aps.63.198703

Cited by 8 publications

(2 citation statements)

References 20 publications

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“…Ning et al proposed the basic scale entropy, which is simple, fast and has strong anti-interference ability, and can effectively analyze short-term, non-stationary, and noisy data (Li and Ning 2005). At present, people have successfully used the basic scale entropy to conduct a large number of studies on physiological electrical signals (Huang et al 2009, Yan and Zhao 2011, Liu et al 2013, Liu et al 2014. Therefore, this paper uses the basic scale entropy method to analyze the complexity of drivers' EEG signals and detect drivers' fatigue state in real time.…”

Section: Introductionmentioning

confidence: 99%

Research on driving fatigue detection based on basic scale entropy and MVAR-PSI

Wang

Kang

et al. 2022

Biomed. Phys. Eng. Express

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In long-term continuous driving, driving fatigue is the main cause of traffic accidents. Therefore, accurate and rapid detection of driver mental fatigue is of great significance to traffic safety. In our study, the electroencephalogram (EEG) signals of subjects were preprocessed to remove interference signals. The Butterworth band-pass filter is used to extract the EEG signals of α and β rhythms, and then the basic scale entropy of α and β rhythms is used as driving fatigue characteristics. In addition, combined with the fast multiple autoregressive (MVAR) model and phase slope index (PSI), short-term data is used to accurately estimate the effective connectivity of EEG signals between different channels, and analyzed the causality flow direction in the left and right prefrontal regions of drivers at different driving stages. Further comprehensive analysis of the driver’s driving fatigue state in the continuous driving phase. Finally, the correlation coefficient value between the parameter pairs (basic scale entropy, clustering coefficient, global efficiency) is calculated. The results showed that the causality flow outflow degree of prefrontal lobe decreased during the transition from sober driving state to tired driving state. The left and right prefrontal lobes were the source of causality in sober driving state, and gradually became the target of causality with the occurrence of driving fatigue. The results showed that when transitioning from a waking state to a fatigued driving state, the causal flow direction out-degree value of the prefrontal cortex on a declining curve, and the left and right prefrontal cortex exhibited the causal source in the awake driving state, which gradually changed into the causal target along with the occurrence of driving fatigue. The three parameters of basic scale entropy, clustering coefficient and global efficiency are used as driving fatigue characteristics, and every two parameters have strong correlation. It shows that the combination of basic scale entropy and MVAR-PSI method can effectively detect the driver’s long-term driving fatigue state in continuous driving mode.

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Section: Introductionmentioning

confidence: 99%

Research on driving fatigue detection based on basic scale entropy and MVAR-PSI

Wang

Kang

et al. 2022

Biomed. Phys. Eng. Express

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“…A human body can be abstracted into a complex nonlinear system. Nevertheless, the time domain and frequency domain features of HRV signals are unable to express the nonlinear characteristics of HRV signals completely [19,20]. At present, relevant studies have used nonlinear analysis methods to analyze HRV signals for mental workload.…”

Section: Introductionmentioning

confidence: 99%

Research of HRV as a Measure of Mental Workload in Human and Dual-Arm Robot Interaction

Shao

Wang

et al. 2020

Electronics

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Robots instead of humans work in unstructured environments, expanding the scope of human work. The interactions between humans and robots are indirect through operating terminals. The mental workloads of human increase with the lack of direct perception to the real scenes. Thus, mental workload assessment is important, which could effectively avoid serious accidents caused by mental overloading. In this paper, the operating object is a dual-arm robot. The classification of operator’s mental workload is studied by using the heart rate variability (HRV) signal. First, two kinds of electrocardiogram (ECG) signals are collected from six subjects who performed tasks or maintained a relaxed state. Then, HRV data is obtained from ECG signals and 20 kinds of HRV features are extracted. Last, six different classifications are used for mental workload classification. Using each subject’s HRV signal to train the model, the subject’s mental workload is classified. Average classification accuracy of 98.77% is obtained using the K-Nearest Neighbor (KNN) method. By using the HRV signal of five subjects for training and that of one subject for testing with the Gentle Boost (GB) method, the highest average classification accuracy (80.56%) is obtained. This study has implications for the analysis of HRV signals characteristic of mental workload in different subjects, which could improve operators’ well-being and safety in the human-robot interaction process.

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