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Research of Sleep Staging Algorithms Based on ECG and Body Motion Signals
LIU Zhong, WANG Xin’an, LI Qiuping, ZHAO Tianxia
Acta Scientiarum Naturalium Universitatis Pekinensis    2021, 57 (5): 833-840.   DOI: 10.13209/j.0479-8023.2021.079
Abstract1130)   HTML    PDF(pc) (2389KB)(341)       Save
 In order to study the overnight sleep condition and analyze each stage of the sleep process, polysomnography (PSG) and actigraphy were used to collect the ECG signal and body motion data. The features of ECG signal and heart rate variability (HRV) were extracted and used as the characteristic parameters of the data. In order to improve the recognition rate and prevent over-fitting, the data were divided into training set and test set, and an improved BP neural network model with genetic algorithm was designed to train and predict the samples. The results show that the improved BP neural network can effectively identify the test samples, and the comprehensive recognition accuracy is 86.29%. Wearable devices that detect both ECG and body motion signals with sleep stage classifying algorithms, can be used for family sleep monitoring and as a primary screen method for sleep disorders.
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Research on Analysis of Arrhythmia Based on pRRx Serials Derived from ECG Signals
LI Ran, WANG Xin’an, ZHAO Tianxia, LIU Yanling, LI Qiuping
Acta Scientiarum Naturalium Universitatis Pekinensis    2018, 54 (6): 1166-1172.   DOI: 10.13209/j.0479-8023.2018.081
Abstract1038)   HTML    PDF(pc) (1533KB)(246)       Save

In order to analyze arrhythmia, a noval practical method was proposed. Here the pRRx serials (x ranged from 1 to 100 ms) extracted from ECG data were taken as fundamental signals. There were obvious differences in distribution of the pRRx serials between 20 people with normal arrhythmia (group I) and 20 patients with arrhythmia (group II). By analyzing the linear indexes and nonlinear indexes of pRRx serials, the computed results show that there are significant statistical differences between the two groups. The linear indexes (AVRR, rMSSD, SDSD) are very different (P<0.001). The nonlinear indexes, from the entropy measures (Sdh, Sph, Spf) and the fractal dimension measures (Dsf, Dcf, Dvm, Drms), also maintain apparent differences (P<0.001). Therefore, the proposed pRRx-serial analysis can characterize the linearity and nonlinearity of the cardiac system to some extent, and can be effective in recognizing the arrhythmia and even heart-related diseases.

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Research and Implementation of Multi-component Seismic Monitoring System AETA
WANG Xin’an, YONG Shanshan, XU Boxing, LIANG Yiwen, BAI Zhiqiang, AN Huiyao, ZHANG Xing, HUANG Jipan, XIE Zheng, LIN Ke, HE Chunjiu, LI Qiuping
Acta Scientiarum Naturalium Universitatis Pekinensis    2018, 54 (3): 487-494.   DOI: 10.13209/j.0479-8023.2017.171
Abstract1174)   HTML9)    PDF(pc) (2073KB)(265)       Save

The authors introduce the multi-component seismic monitoring system AETA (acoustic & electromagnetic testing all in one system). The results of experiments in Yunnan, Sichuan, Tibet, Hebei, Beijing and Guangdong prove that the system AETA has the proper sensitivity with low cost and is easy to be installed. Meanwhile, the raw data and feature data refined from raw data have a good indication of earthquake. More subsequent experiments will be organized in west of China, capital circle of China and Taiwan Strait for deep research on effect of prediction.

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