Ensemble empirical mode decomposition (EEMD) is an effective method for non-stationary signal analysis, such as electrocardiogram (ECG) signals. However, the precision and correctness of EEMD are affected by the two parameters, ratio of the added noise and ensemble number. The values of two parameters are set relying on experience and lacking of adaptability for uncertain signals. In order to solve these problems, we proposed a method based on white noise decomposed by EEMD in the present study shown in this paper. Empirical mode decomposition (EMD) was applied to decompose the signal to different intrinsic mode functions (IMFs) in the de-noising process. The white noise IMFs were selected to constitute high frequency part based on the character that the product of the energy density of white noise and its average period tended to be a constant. Then the two parameters of EEMD were adaptively obtained according to the criterion which was used to avoid modal aliasing. Experimental results showed that the method was an effective one for ECG signal de-noising.
Electrocardiogram (ECG) signals are susceptible to be disturbed by 50 Hz power line interference (PLI) in the process of acquisition and conversion. This paper, therefore, proposes a novel PLI removal algorithm based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD). Firstly, according to the morphological differences in ECG waveform characteristics, the noisy ECG signal was decomposed into the mutated component, the smooth component and the residual component by MCA. Secondly, intrinsic mode functions (IMF) of PLI was filtered. The noise suppression rate (NSR) and the signal distortion ratio (SDR) were used to evaluate the effect of de-noising algorithm. Finally, the ECG signals were re-constructed. Based on the experimental comparison, it was concluded that the proposed algorithm had better filtering functions than the improved Levkov algorithm, because it could not only effectively filter the PLI, but also have smaller SDR value.
As a novel technology, wearable physiological parameter monitoring technology represents the future of monitoring technology. However, there are still many problems in the application of this kind of technology. In this paper, a pilot study was conducted to evaluate the quality of electrocardiogram (ECG) signals of the wearable physiological monitoring system (SensEcho-5B). Firstly, an evaluation algorithm of ECG signal quality was developed based on template matching method, which was used for automatic and quantitative evaluation of ECG signals. The algorithm performance was tested on a randomly selected 100 h dataset of ECG signals from 100 subjects (15 healthy subjects and 85 patients with cardiovascular diseases). On this basis, 24-hour ECG data of 30 subjects (7 healthy subjects and 23 patients with cardiovascular diseases) were collected synchronously by SensEcho-5B and ECG Holter. The evaluation algorithm was used to evaluate the quality of ECG signals recorded synchronously by the two systems. Algorithm validation results: sensitivity was 100%, specificity was 99.51%, and accuracy was 99.99%. Results of controlled test of 30 subjects: the median (Q1, Q3) of ECG signal detected by SensEcho-5B with poor signal quality time was 8.93 (0.84, 32.53) minutes, and the median (Q1, Q3) of ECG signal detected by Holter with poor signal quality time was 14.75 (4.39, 35.98) minutes (Rank sum test, P=0.133). The results show that the ECG signal quality algorithm proposed in this paper can effectively evaluate the ECG signal quality of the wearable physiological monitoring system. Compared with signal measured by Holter, the ECG signal measured by SensEcho-5B has the same ECG signal quality. Follow-up studies will further collect physiological data of large samples in real clinical environment, analyze and evaluate the quality of ECG signals, so as to continuously optimize the performance of the monitoring system.
Due to the characteristics and environmental factors, electrocardiogram (ECG) signals are usually interfered by noises in the course of signal acquisition, so it is crucial for ECG intelligent analysis to eliminate noises in ECG signals. On the basis of wavelet transform, threshold parameters were improved and a more appropriate threshold expression was proposed. The discrete wavelet coefficients were processed using the improved threshold parameters, the accurate wavelet coefficients without noises were gained through inverse discrete wavelet transform, and then more original signal coefficients could be preserved. MIT-BIH arrythmia database was used to validate the method. Simulation results showed that the improved method could achieve better denoising effect than the traditional ones.
In recent years, wearable devices grew up gradually and developed increasingly. Aiming at the problems of skin sensibility and the change of electrode impedance of Ag/AgCl electrode in the process of long-term electrocardiogram (ECG) signal monitoring and acquisition, this paper discussed in detail a new sensor technology–fabric electrode, which is used for ECG signal acquisition. First, the concept and advantages of fabric electrode were introduced, and then the common substrate materials and conductive materials for fabric electrode were discussed and evaluated. Next, we analyzed the advantages and disadvantages from the aspect of textile structure, putting forward the evaluation system of fabric electrode. Finally, the deficiencies of fabric electrode were analyzed, and the development prospects and directions were prospected.
In the extraction of fetal electrocardiogram (ECG) signal, due to the unicity of the scale of the U-Net same-level convolution encoder, the size and shape difference of the ECG characteristic wave between mother and fetus are ignored, and the time information of ECG signals is not used in the threshold learning process of the encoder’s residual shrinkage module. In this paper, a method of extracting fetal ECG signal based on multi-scale residual shrinkage U-Net model is proposed. First, the Inception and time domain attention were introduced into the residual shrinkage module to enhance the multi-scale feature extraction ability of the same level convolution encoder and the utilization of the time domain information of fetal ECG signal. In order to maintain more local details of ECG waveform, the maximum pooling in U-Net was replaced by Softpool. Finally, the decoder composed of the residual module and up-sampling gradually generated fetal ECG signals. In this paper, clinical ECG signals were used for experiments. The final results showed that compared with other fetal ECG extraction algorithms, the method proposed in this paper could extract clearer fetal ECG signals. The sensitivity, positive predictive value, and F1 scores in the 2013 competition data set reached 93.33%, 99.36%, and 96.09%, respectively, indicating that this method can effectively extract fetal ECG signals and has certain application values for perinatal fetal health monitoring.
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.
Using LabVIEW programming and highspeed multifunction data acquisition card PCI6251, we designed an electrocardiogram (ECG) signal generator based on Chinese typical ECG database. When the ECG signals are given off by the generator, the generator can also display the ECG information annotations at the same time, including waveform data and diagnostic results. It could be a useful assisting tool of ECG automatic diagnose instruments.
Electrocardiogram (ECG) is easily submerged in noise of the complex environment during remote medical treatment, and this affects the intelligent diagnosis of cardiovascular diseases. Considering this situation, this paper proposes an echo state network (ESN) denoising algorithm based on recursive least square (RLS) for ECG signals. The algorithm trains the ESN through the RLS method, and can automatically learn the deep nonlinear and differentiated characteristics in the noisy ECG data, and then the network can use these characteristic to separate out clear ECG signals automatically. In the experiment, the proposed method is compared with the wavelet transform with subband dependent threshold and the S-transform method by evaluating the signal-to-noise ratio and root mean square error. Experimental results show that the denoising accuracy is better and the low frequency component of the signal is well preserved. This method can effectively filter out complex noise and effectively preserve the effective information of ECG signals, which lays a foundation for the recognition of ECG signal feature waveform and the intelligent diagnosis of cardiovascular disease.
Sleep apnea (SA) detection method based on traditional machine learning needs a lot of efforts in feature engineering and classifier design. We constructed a one-dimensional convolutional neural network (CNN) model, which consists in four convolution layers, four pooling layers, two full connection layers and one classification layer. The automatic feature extraction and classification were realized by the structure of the proposed CNN model. The model was verified by the whole night single-channel sleep electrocardiogram (ECG) signals of 70 subjects from the Apnea-ECG dataset. Our results showed that the accuracy of per-segment SA detection was ranged from 80.1% to 88.0%, using the input signals of single-channel ECG signal, RR interval (RRI) sequence, R peak sequence and RRI sequence + R peak sequence respectively. These results indicated that the proposed CNN model was effective and can automatically extract and classify features from the original single-channel ECG signal or its derived signal RRI and R peak sequence. When the input signals were RRI sequence + R peak sequence, the CNN model achieved the best performance. The accuracy, sensitivity and specificity of per-segment SA detection were 88.0%, 85.1% and 89.9%, respectively. And the accuracy of per-recording SA diagnosis was 100%. These findings indicated that the proposed method can effectively improve the accuracy and robustness of SA detection and outperform the methods reported in recent years. The proposed CNN model can be applied to portable screening diagnosis equipment for SA with remote server.