Statistical analysis of 100 successive RR interval time in ECG, mean heart rate was carried out in 3 groups. The 1st group consisted of 23 healthy men, aged 19.3. The 2nd group consisted of 8 men, aged 21,6 and suffered from inconciousness during working time. The 3rd group consisted of 6 men, aged 19.2 with bad occupational adaptation. Results: heart rate index of 2nd and 3rd groups at rest clearly represented functional strain in comparison with that of 1st group were decreased, mean HR and SI were increased. After 30 and 60 minutes of stricly standing, all index of 3 groups were significantly greater than that at rest, but the index variation of the 1st group were higher than in 2nd and 3rd groups.
Analysis ; Heart Rate ; physiology

OBJECTIVETo compare the heart rate variability (HRV) measurements between ballistocardiogram (BCG) and electrocardiography (ECG).

METHODSThe signals of BCG and ECG of 21 patients were collected synchronously. JJ intervals of BCG and RR intervals of ECG were used to calculate the cardiac periods. The parameters of HRV analysis were calculated in time domain analysis, frequency domain analysis and nonlinear analysis. The results derived from BCG and ECG were compared.

RESULTSThe parameters of HRV analysis calculated from BCG and ECG had high similarity. The correlation coefficients of SDNN, TP, LF, HF and SD2 between the BCG and ECG methods were high (r = 1). The correlation coefficients of rMSSD and SD2 were 0.99 and of PNN50 and LF/HF were 0.98 between the two methods. HRV analysis results derived from the two methods were similar (P > 0.05).

CONCLUSIONHRV could also be measured reliably by calculating the JJ interval from BCG.

Based on the heart rate measurement method using time-lapse image of human cheek, this paper proposes a novel measurement algorithm based on Artificial Intelligence. The algorithm combining with fuzzy logic theory acquires the heart beat point by using the defined fuzzy membership function of each sampled point. As a result, it calculates the heart rate by counting the heart beat points in a certain time period. Experiment shows said algorithm satisfies in operability, accuracy and robustness, which leads to constant practical value.
Algorithms ; Artificial Intelligence ; Heart Rate ; physiology ; Humans

OBJECTScientific guided breathing technology is to be studied based on the cardiopulmonary interaction.

METHODSHeart rate variability was used as the target function to study the smoothly respiratory relaxation procedure in order to acquire the common pattern of regular and slow breathing.

RESULTSMusic based on the acquired common pattern was created and a musical pattern temporally-related to the breathing movement monitored by a sensor could be chosen to guide the breathing interactively.

To determine the role of vagi in heart rate variability, conscious rabbits were employed and electrocardiogram was recorded under conditions of bilateral vagi intact, unilateral vagotomy, and bilateral vagotomy. The variability of RR intervals (RRI) was analyzed using power spectrum and approximate entropy (ApEn). The results showed that the values of high frequency power (HF) component, low frequency power (LF) component and ApEn in animals with bilateral vagi intact were the highest, but the LF/HF ratio was the lowest; unilateral vagotomy decreased ApEn, right vagotomy increased LF/HF ratio but left vagotomy did not; the LF/HF ratio increased while ApEn decreased significantly in animals with bilateral vagotomy. It is suggested that the variability of RRI is mainly regulated by the vagi and the role of right vagi is more important than that of the left. When measuring heart rate variability, the results obtained with conventional method are consistent with those with nonlinear method.
Animals ; Entropy ; Heart Rate ; physiology ; Male ; Rabbits ; Vagus Nerve ; physiology

Altitude ; Exercise/physiology* ; Heart Rate/physiology* ; Humans ; Lung ; Respiration

Animals ; Chick Embryo ; physiology ; Electrocardiography ; methods ; Heart ; embryology ; physiology ; Heart Rate ; physiology

Animals ; Electrocardiography ; Heart ; physiology ; physiopathology ; Heart Rate ; physiology ; Humans ; Noise ; adverse effects ; Rats

Angiotensin II ; pharmacology ; Animals ; Heart Rate ; drug effects ; physiology ; Rabbits

The detection of R-wave of ECG is essential to the analysis of the heart rate variability (HRV). In this paper, an R-wave detection method using wavelet transform(WT) is presented in line with the principle of discrete wavelet transform(DWT) and multi-resolution technique (MRT). We made use of the special properties of dbl wavelet in time-domain, decomposed the original ECG signals into 3-level detailed signals on different frequency bands by using DWT with Mallat algorithm, and got appropriate threshold values in different high frequency bands to distinguish R-wave. It is concluded that the algorithm had significant effects on it, which is verified by MIT/BIH (Massachusetts Institute of Technology/Boston's Beth Israel Hospital) ECG Database. The results show that R-wave could be detected accurately and localized precisely by this method, even when the patient was seriously sick or the signal was disturbed by noise. Consequently the method has a quite high locating precision (its error is not more than two sampled points and about 85 percent of the points of R-wave in ECG signal are localized precisely) and the correct detection rate of R-wave is 99.8% by using wavelet transform, so this method is quite feasible.
Algorithms ; Electrocardiography ; Heart Rate ; physiology ; Humans ; Signal Processing, Computer-Assisted