In this paper,heart rate (HR) and heart rate variability (HRV) are extracted from ECG,and the five indexes in both time and frequency domains are analyzed.The result shows that the standard deviation (SDNN) of R-R intervals,the value of total power (TP),the power value of low frequency (LF) of HRV and the value of LF/HF increase obviously,while the power value of high frequency (HF) decreases markedly after fatigue.The physical fatigue level is classified according to the rate of increase and decrease of the indexes above.It is suggested that these five ECG indexes be used to reflect and evaluate the degree of physical fatigue quantitatively.

Objective Chronic diseases are recognized as a major health problem in the 21st century.This project was to assess the effects of self-awareness health management service on optimization of life-style of community residents so as to effectively control the occurrence and development of chronic diseases.Method A survey of health information,an evaluation of health status,health intervention were conducted among 231 people aged between 29-65 years in a community for a half year,and the health management was effectively assessed of them.38 subjects with chronic diseases were subjected to diet friends used and exercise quantitative management model.Results The way of life of the 231 people has been optimized,and in the 38 patients with chronic diseases who had adopted the management mode of friends six months later their average body weight dropped by 1.3 kg,body mass index dropped by 0.5,and waist circumference reduced by 1.8 cm(P<0.05).Conclusion It is feasible to conduct health management in residents of communities,especially in patients with chronic diseases,which COnforms to the basic principles and requirements of prevention and treatment of chronic diseases advocated by WHO.

OBJECTIVEExtraction of cepstral coefficients combined with Gaussian Mixture Model (GMM) is used to propose a biometric method based on heart sound signal.

METHODSFirstly, the original heart sounds signal was preprocessed by wavelet denoising. Then, Linear Prediction Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) are compared to extract representative features and develops hidden Markov model (HMM) for signal classification. At last, the experiment collects 100 heart sounds from 50 people to test the proposed algorithm.

RESULTSThe comparative experiments prove that LPCC is more suitable than MFCC for heart sound biometric, and by wavelet denoising in each piece of heart sound signal, the system achieves higher recognition rate than traditional GMM.

CONCLUSIONThose results show that this method can effectively improve the recognition performance of the system and achieve a satisfactory effect.

Algorithms ; Biometry ; Heart ; physiology ; Humans ; Markov Chains ; Models, Biological ; Phonocardiography ; methods ; Wavelet Analysis