A5058025411- Heart Rate Variability and Autonomic Control
- EEG and Brain-Computer Interfaces
- Sleep and Work-Related Fatigue
- Non-Invasive Vital Sign Monitoring
- ECG Monitoring and Analysis
- Complex Systems and Time Series Analysis
- Time Series Analysis and Forecasting
- Functional Brain Connectivity Studies
Southwest University
2019-2022
Chongqing University
2019-2020
The matching of cognitive load and working memory is the key for effective learning, effort in learning process has nervous responses which can be quantified various physiological parameters. Therefore, it meaningful to explore automatic pattern recognition by using measures. Firstly, this work extracted 33 commonly used features quantify autonomic central activities. Secondly, we selected a critical feature subset sequential backward selection particle swarm optimization algorithms....
The complexity of physiological system can be well represented by the time series. In this paper, analysis was performed on inter-beat-interval(IBI) apnea syndrome and normal sleep population. Hurst exponent, sample entropy fractal dimension were used as three indicators measurement. results show that optimal combination statistical nonlinear features with Support Vector Machine (SVM) achieve recognition accuracy up to 75.51%. measurement, t-test shows average or group significantly larger...
Sleep deprivation (SD) is harmful to human health. This paper analyzed the autonomic nervous patterns (ANP) of subjects before sleep (BSD) and during (DSD). Electrocardiogram (ECG) data were obtained from 60 subjects, collected for half an hour SD all subjects. Twenty-five heart rate variability features RR intervals ECG classification. The results classification showed that BSD DSD distinguishable in their system (ANS) activities. optimal feature subset was selected by sequential backward...