This paper studied the rule for the change of vigilance based on pulse wave. 10 participants were recruited in a 95-minute Mackworth clock test (MCT) experiment. During the experiment, the vigilance of all participants were evaluated by Karolinska sleepiness scale (KSS) and Stanford sleepiness scale (SSS), and behavior data (the reaction time and the accuracy of target) and pulse wave signal of the participants were recorded simultaneously. The result indicated that vigilance of the participants can be divided into 3 classes: the first 30 minutes for high vigilance level, the middle 30 minutes for general vigilance level, and the last 30 minutes for low vigilance level. Besides, time domain features such as amplitude of secondary peak, amplitude of peak and the latency of secondary peak decreased with the decrease of vigilance, while the amplitude of troughs increased. In terms of frequency domain features, the energy of 4 frequency band including 8.600 ~ 9.375 Hz, 11.720 ~ 12.500 Hz, 38.280 ~ 39.060 Hz and 39.060 ~ 39.840 Hz decreased with the decrease of vigilance. Finally, under the recognition model established by the 8 characteristics mentioned above, the average accuracy of three-classification results over the 10 participants was as high as 88.7%. The results of this study confirmed the feasibility of pulse wave in the evaluation of vigilance, and provided a new way for the real-time monitoring of vigilance.
Sleep is a complex physiological process of great significance to physical and mental health, and its research scope involves multiple disciplines. At present, the quantitative analysis of sleep mainly relies on the “gold standard” of polysomnography (PSG). However, PSG has great interference to the human body and cannot reflect the hemodynamic status of the brain. Functional near infrared spectroscopy (fNIRS) is used in sleep research, which can not only meet the demand of low interference to human body, but also reflect the hemodynamics of brain. Therefore, this paper has collected and sorted out the related literatures about fNIRS used in sleep research, concluding sleep staging research, clinical sleep monitoring research, fatigue detection research, etc. This paper provides a theoretical reference for scholars who will use fNIRS for fatigue and sleep related research in the future. Moreover, this article concludes the limitation of existing studies and points out the possible development direction of fNIRS for sleep research, in the hope of providing reference for the study of sleep and cerebral hemodynamics.