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.
In order to solve the problem that the early onset of paroxysmal atrial fibrillation is very short and difficult to detect, a detection algorithm based on sparse coding of Riemannian manifolds is proposed. The proposed method takes into account that the nonlinear manifold geometry is closer to the real feature space structure, and the computational covariance matrix is used to characterize the heart rate variability (RR interval variation), so that the data is in the Riemannian manifold space. Sparse coding is applied to the manifold, and each covariance matrix is represented as a sparse linear combination of Riemann dictionary atoms. The sparse reconstruction loss is defined by the affine invariant Riemannian metric, and the Riemann dictionary is learned by iterative method. Compared with the existing methods, this method used shorter heart rate variability signal, the calculation was simple and had no dependence on the parameters, and the better prediction accuracy was obtained. The final classification on MIT-BIH AF database resulted in a sensitivity of 99.34%, a specificity of 95.41% and an accuracy of 97.45%. At the same time, a specificity of 95.18% was realized in MIT-BIH NSR database. The high precision paroxysmal atrial fibrillation detection algorithm proposed in this paper has a potential application prospect in the long-term monitoring of wearable devices.
The dynamic electrocardiogram (ECG) collected by wearable devices is often corrupted by motion interference due to human activities. The frequency of the interference and the frequency of the ECG signal overlap with each other, which distorts and deforms the ECG signal, and then affects the accuracy of heart rate detection. In this paper, a heart rate detection method that using coarse graining technique was proposed. First, the ECG signal was preprocessed to remove the baseline drift and the high-frequency interference. Second, the motion-related high amplitude interference exceeding the preset threshold was suppressed by signal compression method. Third, the signal was coarse-grained by adaptive peak dilation and waveform reconstruction. Heart rate was calculated based on the frequency spectrum obtained from fast Fourier transformation. The performance of the method was compared with a wavelet transform based QRS feature extraction algorithm using ECG collected from 30 volunteers at rest and in different motion states. The results showed that the correlation coefficient between the calculated heart rate and the standard heart rate was 0.999, which was higher than the result of the wavelet transform method (r = 0.971). The accuracy of the proposed method was significantly higher than the wavelet transform method in all states, including resting (99.95% vs. 99.14%, P < 0.01), walking (100% vs. 97.26%, P < 0.01) and running (100% vs. 90.89%, P < 0.01). The absolute error [0 (0, 1) vs. 1 (0, 1), P < 0.05] and relative error [0 (0, 0.59) vs. 0.52 (0, 0.72), P < 0.05] of the proposed method were significantly lower than the wavelet transform method during running state. The method presented in this paper shows high accuracy and strong anti-interference ability, and is potentially used in wearable devices to realize real-time continuous heart rate monitoring in daily activities and exercise conditions.
The selection of fiducial points has an important effect on electrocardiogram (ECG) denoise with cubic spline interpolation. An improved cubic spline interpolation algorithm for suppressing ECG baseline drift is presented in this paper. Firstly the first order derivative of original ECG signal is calculated, and the maximum and minimum points of each beat are obtained, which are treated as the position of fiducial points. And then the original ECG is fed into a high pass filter with 1.5 Hz cutoff frequency. The difference between the original and the filtered ECG at the fiducial points is taken as the amplitude of the fiducial points. Then cubic spline interpolation curve fitting is used to the fiducial points, and the fitting curve is the baseline drift curve. For the two simulated case test, the correlation coefficients between the fitting curve by the presented algorithm and the simulated curve were increased by 0.242 and 0.13 compared with that from traditional cubic spline interpolation algorithm. And for the case of clinical baseline drift data, the average correlation coefficient from the presented algorithm achieved 0.972.
In order to reduce the mortality rate of cardiovascular disease patients effectively, improve the electrocardiogram (ECG) accuracy of signal acquisition, and reduce the influence of motion artifacts caused by the electrodes in inappropriate location in the clothing for ECG measurement, we in this article present a research on the optimum place of ECG electrodes in male clothing using three-lead monitoring methods. In the 3-lead ECG monitoring clothing for men we selected test points. Comparing the ECG and power spectrum analysis of the acquired ECG signal quality of each group of points, we determined the best location of ECG electrodes in the male monitoring clothing. The electrode motion artifacts caused by improper location had been significantly improved when electrodes were put in the best position of the clothing for men. The position of electrodes is crucial for ECG monitoring clothing. The stability of the acquired ECG signal could be improved significantly when electrodes are put at optimal locations.
ObjectiveTo investigate the correlation between intima-media thickness (IMT) of carotid artery in color ultrasonography and the heart rate variability. MethodsA retrospective analysis was performed in 64 patients from West China Hospital of Sichuan University between March and May 2013. Carotid intima-media thickness was measured with color ultrasonography and dynamic electrocardiogram, and the heart rate variability was assayed at the same time. ResultsIMT in the cardiovascular disease group, combination group, coronary heart disease group and hypertension group was significantly thicker than the control group (P<0.05). The differences of SDNN and SDANN were statistically significant (P<0.05) between the combination group and the control group. There were 23 cases with IMT ≥ 1.0 mm in the cardiovascular disease group including 8 cases in the combination group, 10 cases in the coronary heart disease group and 5 cases in the hypertension group. IMT in those groups were all significantly higher than that in the control group with only 2 cases having IMT ≥ 1.0 mm (P<0.05). There were 18 cases with SDNN<100 ms in the cardiovascular disease group including 7 cases in the combination group, 6 cases in the coronary heart disease group and 5 cases in the hypertension group, but there was no statistically significant difference compared with that in the control group with only 11 cases (P>0.05). Negative correlation was found between IMT and SDNN, SDANN in the cardiovascular diseases group (r=-0.574, -0.544; P<0.01) and negative correlation was found between IMT and SDANN in the control group (r=-0.392, P<0.05). ConclusionThe carotid artery lesions and autonomic nerve especially sympathetic nerve dysfunction are obvious in patients with cardiovascular diseases and there is a negative correlation between them.
A de-noising method for electrocardiogram (ECG) based on ensemble empirical mode decomposition (EEMD) and wavelet threshold de-noising theory is proposed in our school. We decomposed noised ECG signals with the proposed method using the EEMD and calculated a series of intrinsic mode functions (IMFs). Then we selected IMFs and reconstructed them to realize the de-noising for ECG. The processed ECG signals were filtered again with wavelet transform using improved threshold function. In the experiments, MIT-BIH ECG database was used for evaluating the performance of the proposed method, contrasting with de-noising method based on EEMD and wavelet transform with improved threshold function alone in parameters of signal to noise ratio (SNR) and mean square error (MSE). The results showed that the ECG waveforms de-noised with the proposed method were smooth and the amplitudes of ECG features did not attenuate. In conclusion, the method discussed in this paper can realize the ECG de-noising and meanwhile keep the characteristics of original ECG signal.
Artery stiffness is a main factor causing the various cardiovascular diseases in physiology and pathology. Therefore, the development of the non-invasive detection of arteriosclerosis is significant in preventing cardiovascular problems. In this study, the characterized parameters indicating the vascular stiffness were obtained by analyzing the electrocardiogram (ECG) and pulse wave signals, which can reflect the early change of vascular condition, and can predict the risk of cardiovascular diseases. Considering the coupling of ECG and pulse wave signals, and the association with atherosclerosis, we used the ECG signal characteristic parameters, including RR interval, QRS wave width and T wave amplitude, as well as the pulse wave signal characteristic parameters (the number of peaks, 20% main wave width, the main wave slope, pulse rate and the relative height of the three peaks), to evaluate the samples. We then built an assessment model of arteriosclerosis based on Adaptive Network-based Fuzzy Interference System (ANFIS) using the obtained forty sets samples data of ECG and pulse wave signals. The results showed that the model could noninvasively assess the arteriosclerosis by self-learning diagnosis based on expert experience, and the detection method could be further developed to a potential technique for evaluating the risk of cardiovascular diseases. The technique will facilitate the reduction of the morbidity and mortality of the cardiovascular diseases with the effective and prompt medical intervention.
The validity and reasonableness of emotional data are the key issues in the cognitive affective computing research. Effects of the emotion recognition are decided by the quality of selected data directly. Therefore, it is an important part of affective computing research to build affective computing database with good performance, so that it is the hot spot of research in this field. In this paper, the performance of two classical cognitive affective computing databases, the Massachusetts Institute of Technology (MIT) cognitive affective computing database and Germany Augsburg University emotion recognition database were compared, their data structure and data types were compared respectively, and emotional recognition effect based on the data were studied comparatively. The results indicated that the analysis based on the physical parameters could get the effective emotional recognition, and would be a feasible method of pressure emotional evaluation. Because of the lack of stress emotional evaluation data based on the physiological parameters domestically, there is not a public stress emotional database. We hereby built a dataset for the stress evaluation towards the high stress group in colleges, candidates of postgraduates of Ph.D and master as the subjects. We then acquired their physiological parameters, and performed the pressure analysis based on this database. The results indicated that this dataset had a certain reference value for the stress evaluation, and we hope this research can provide a reference and support for emotion evaluation and analysis.
With the increasing number of electrocardiogram (ECG) data, extensive application requirements of computer-aided ECG analysis have occurred. In the paper, we propose a variety of strategies to improve the performance of clinical ECG classification algorithm based on Lead Convolutional Neural Network (LCNN). Firstly, we obtained two classifiers by using different preprocessing methods and training methods in the study. Then, we applied the multiple output prediction method to both of them independently. Finally, the Bayesian approach was employed to fuse them. Tests conducted using more than 150 000 ECG records showed that the proposed method had an accuracy of 85.04% and the area under receiver operating characteristic curve (AUC) was 0.918 5, which significantly outperforms traditional methods based on feature extraction techniques.