Calculation of linear parameters, such as time-domain and frequency-domain analysis of heart rate variability (HRV), is a conventional method for assessment of autonomic nervous system activity. Nonlinear phenomena are certainly involved in the genesis of HRV. In a seemingly random signal the Poincaré plot can easily demonstrate whether there is an underlying determinism in the signal. Linear and nonlinear analysis methods were applied in the computer words inputting experiments in this study for physiological measurement. This study therefore demonstrated that Poincaré plot was a simple but powerful graphical tool to describe the dynamics of a system.
Heart rate variability (HRV) is the difference between the successive changes in the heartbeat cycle, and it is produced in the autonomic nervous system modulation of the sinus node of the heart. The HRV is a valuable indicator in predicting the sudden cardiac death and arrhythmic events. Traditional analysis of HRV is based on a multi-electrocardiogram (ECG), but the ECG signal acquisition is complex, so we have designed an HRV analysis system based on photoplethysmography (PPG). PPG signal is collected by a microcontroller from human’s finger, and it is sent to the terminal via USB-Serial module. The terminal software not only collects the data and plot waveforms, but also stores the data for future HRV analysis. The system is small in size, low in power consumption, and easy for operation. It is suitable for daily care no matter whether it is used at home or in a hospital.
The linear analysis for heart rate variability (HRV), including time domain method, frequency domain method and timefrequency analysis, has reached a lot of consensus. The nonlinear analysis has also been widely applied in biomedical and clinical researches. However, for nonlinear HRV analysis, especially for shortterm nonlinear HRV analysis, controversy still exists, and a unified standard and conclusion has not been formed. This paper reviews and discusses three shortterm nonlinear HRV analysis methods (fractal dimension, entropy and complexity) and their principles, progresses and problems in clinical application in detail, in order to provide a reference for accurate application in clinical medicine.
In this paper, a heart rate variability analysis system is presented for short-term (5 min) applications, which is composed of an electrocardiogram signal acquisition unit and a heart rate variability analysis unit. The electrocardiogram signal acquisition unit adopts various digital technologies, including the low-gain amplifier, the high-resolution analog-digital converter, the real-time digital filter and wireless transmission etc. Meanwhile, it has the advantages of strong anti-interference capacity, small size, light weight, and good portability. The heart rate variability analysis unit is used to complete the R-wave detection and the analyses of time domain, frequency domain and non-linear indexes, based on the Matlab Toolbox. The preliminary experiments demonstrated that the system was reliable, and could be applied to the heart rate variability analysis at resting, motion states. etc.
At present, the potential hazards of infrasound on heart health have been identified in previous studies, but a comprehensive review of its mechanisms is still lacking. Therefore, this paper reviews the direct and indirect effects of infrasound on cardiac function and explores the mechanisms by which it may induce cardiac abnormalities. Additionally, in order to further study infrasound waves and take effective preventive measures, this paper reviews the mechanisms of cardiac cell damage caused by infrasound exposure, including alterations in cell membrane structure, modulation of electrophysiological properties, and the biological effects triggered by neuroendocrine pathways, and assesses the impact of infrasound exposure on public health.
Heart rate variability (HRV) analysis technology based on an autoregressive (AR) model is widely used in the assessment of autonomic nervous system function. The order of AR models has important influence on the accuracy of HRV analysis. This article presents a method to determine the optimum order of AR models. After acquiring the ECG signal of 46 healthy adults in their natural breathing state and extracting the beat-to-beat intervals (RRI) in the ECG, we used two criteria, i.e. final prediction error (FPE ) criterion to estimate the optimum model order for AR models, and prediction error whiteness test to decide the reliability of the model. We compared the frequency domain parameters including total power, power in high frequency (HF), power in low frequency (LF), LF power in normalized units and ratio of LF/HF of our HRV analysis to the results of Kubios-HRV. The results showed that the correlation coefficients of the five parameters between our methods and Kubios-HRV were greater than 0.95, and the Bland-Altman plot of the parameters was in the consistent band. The results indicate that the optimization algorithm of HRV analysis based on AR models proposed in this paper can obtain accurate results, and the results of this algorithm has good coherence with those of the Kubios-HRV software in HRV analysis.
Photoplethysmography (PPG) is a non-invasive technique to measure heart rate at a lower cost, and it has been recently widely used in smart wearable devices. However, as PPG is easily affected by noises under high-intensity movement, the measured heart rate in sports has low precision. To tackle the problem, this paper proposed a heart rate extraction algorithm based on self-adaptive heart rate separation model. The algorithm firstly preprocessed acceleration and PPG signals, from which cadence and heart rate history were extracted respectively. A self-adaptive model was made based on the connection between the extracted information and current heart rate, and to output possible domain of the heart rate accordingly. The algorithm proposed in this article removed the interference from strong noises by narrowing the domain of real heart rate. From experimental results on the PPG dataset used in 2015 IEEE Signal Processing Cup, the average absolute error on 12 training sets was 1.12 beat per minute (bpm) (Pearson correlation coefficient: 0.996; consistency error: ?0.184 bpm). The average absolute error on 10 testing sets was 3.19 bpm (Pearson correlation coefficient: 0.990; consistency error: 1.327 bpm). From experimental results, the algorithm proposed in this paper can effectively extract heart rate information under noises and has the potential to be put in usage in smart wearable devices.
Heart rate is the most common index to directly monitor the level of physical stress by comparing the subject's heart rate with an appropriate "target heart rate" during exercise. However, heart rate only reveals the cardiac rhythm of the complex cardiovascular changes that take place during exercise. It is essential to get the dynamic response of the heart to exercise with various indices instead of only one single measurement. Based on the rest-workload alternating pattern, this paper screens the sensitive indices of exercise load from electrocardiogram (ECG) rhythm and waveform, including 4 time domain indices and 4 frequency domain indices of heart rate variability (HRV), 3 indices of waveform similarity and 2 indices of high frequency noise. In conclusion, RR interval (heart rate) is a reliable index for the realtime monitoring of exercise intensity, which has strong linear correlation with load intensity. The ECG waveform similarity and HRV indices are useful for the evaluation of exercise load.
In order to realize sleep staging automatically and conveniently, we used support vector machine (SVM) to analyze the correlation between heart rate variability and sleep stage experimentally. R-R intervals (RRIs) from 33 cases of sleep clinical data of Tianjin Thoracic Hospital were extracted and analyzed by principal component analysis (PCA). The SVM method was used to establish the model and predict the five sleep stages. The prediction accuracy of three-sleep-stage was higher than 80%, in contrast to sleep scoring annotations marked by physiological experts based on electroencephalogram (EEG) golden standard. The result showed that there was a good correlation between heart rate variability and sleep staging. This method is an important supplement to the traditional sleep staging method and has a great value for clinical application.
The present paper is to analyze the trend of sinus heart rate RR interphase sequence after a single ventricular premature beat and to compare it with the two parameters, turbulence onset (TO) and turbulence slope (TS). Based on the acquisition of sinus rhythm concussion sample, we in this paper use a piecewise linearization method to extract its linear characteristics, following which we describe shock form with natural language through cloud model. In the process of acquisition, we use the exponential smoothing method to forecast the position where QRS wave may appear to assist QRS wave detection, and use template to judge whether current cardiac is sinus rhythm. And we choose some signals from MIT-BIH Arrhythmia Database to detect whether the algorithm is effective in Matlab. The results show that our method can correctly detect the changing trend of sinus heart rate. The proposed method can achieve real-time detection of sinus rhythm shocks, which is simple and easily implemented, so that it is effective as a supplementary method.
