In this paper, the ECG de-noising technology based on wavelet neural networks (WNN) is used to deal with the noises in Electrocardiogram (ECG) signal. The structure of WNN, which has the outstanding nonlinear mapping capability, is designed as a nonlinear filter used for ECG to cancel the baseline wander, electromyo-graphical interference and powerline interference. The network training algorithm and de-noising experiments results are presented, and some key points of the WNN filter using ECG de-noising are discussed.
Algorithms ; Electrocardiography ; methods ; Humans ; Neural Networks (Computer) ; Signal Processing, Computer-Assisted ; Wavelet Analysis

According to the characteristics of four basic P morphologies, combining the wavelet transform and the amplitude and slope of transformed P wave, a new P-wave detecting method based on "wavelet-amplitude-slope" algorithm is presented: First search out all modulus maximum pairs to satisfy the threshold after wavelet transform, and then applying the amplitude and slope criterion exclude the interferes and detect the P peak and its shape, last determine the onset and end of P wave respectively which should be separately calculated for single-peak and double-peak P wave (or biphasic P wave). The approach is applied in experiments of data from MIT/BIH database and randomly collected data of clinical ECG. The experimental statistical results shows that the correct detecting rate is as high as 96% compared to manual annotation.
Algorithms ; Electrocardiography ; Humans ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted

T-wave alternans (TWA) refers to a phenomenon appearing in the surface electrocardiograph (ECG) as a consistent fluctuation in morphology and amplitude of the T wave on an "every-other-beat" basis. Correlation method (CM) has a certain ability to detect the non-stationary TWA, but it is very sensitive to noise. In this paper we propose a modified correlation method to ensure a stable and accurate detection of non-stationary TWA. Compared to the CM, the method modifies the judge condition and uses the linear fitting to limit the noise to gain the ability of detecting of non-stationary TWA. Our simulation and clinical data assessment study demonstrates the improved performance of the proposed algorithm.
Algorithms ; Artifacts ; Computer Simulation ; Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted ; Wavelet Analysis

Arrhythmia is a significant cardiovascular disease that poses a threat to human health, and its primary diagnosis relies on electrocardiogram (ECG). Implementing computer technology to achieve automatic classification of arrhythmia can effectively avoid human error, improve diagnostic efficiency, and reduce costs. However, most automatic arrhythmia classification algorithms focus on one-dimensional temporal signals, which lack robustness. Therefore, this study proposed an arrhythmia image classification method based on Gramian angular summation field (GASF) and an improved Inception-ResNet-v2 network. Firstly, the data was preprocessed using variational mode decomposition, and data augmentation was performed using a deep convolutional generative adversarial network. Then, GASF was used to transform one-dimensional ECG signals into two-dimensional images, and an improved Inception-ResNet-v2 network was utilized to implement the five arrhythmia classifications recommended by the AAMI (N, V, S, F, and Q). The experimental results on the MIT-BIH Arrhythmia Database showed that the proposed method achieved an overall classification accuracy of 99.52% and 95.48% under the intra-patient and inter-patient paradigms, respectively. The arrhythmia classification performance of the improved Inception-ResNet-v2 network in this study outperforms other methods, providing a new approach for deep learning-based automatic arrhythmia classification.
Humans ; Arrhythmias, Cardiac/diagnostic imaging* ; Cardiovascular Diseases ; Algorithms ; Databases, Factual ; Electrocardiography