Atrial fibrillation (AF) is the most frequently encountered cardiac arrhythmia and is often associated with other cardiovascular and cerebrovascular diseases, such as ischemic heart disease, chronic heart failure, and stroke. Automatic detection of AF by analyzing electrocardiogram (ECG) signals has an important application value. Using the contaminated and actual ECG signals, it is not enough to only analyze the atrial activity of disappeared P wave and appeared F wave in the TQ segment. Moreover, the best analysis method is to combine nonlinear features analyzing ventricular activity based on the detection of R peak. In this paper, to utilize the information of the P-QRS-T waveform generated by atrial and ventricular activity, frequency slice wavelet transform (FSWT) is adopted to conduct time-frequency analysis on short-term ECG segments from the MIT-BIH Atrial Fibrillation Database. The two-dimensional time-frequency matrices are obtained. Furthermore, an average sliding window is used to convert the two-dimensional time-frequency matrices to the one-dimensional feature vectors, which are classified using five machine learning (ML) techniques. The experimental results show that the classification performance of the Gaussian-kernel support vector machine (GKSVM) based on the Bayesian optimizer is better. The accuracy of the training set and validation set are 100% and 93.4%. The accuracy, sensitivity, and specificity of the test set without training are 98.15%, 96.43%, and 100%, respectively. Compared with previous research results, our proposed FSWT-GKSVM model shows stability and robustness, and it could achieve the purpose of automatic detection of AF.

Arrhythmia is one of the most persistent chronic heart diseases in the elderly and is associated with high morbidity and mortality such as stroke, cardiac failure, and coronary artery diseases. It is significant for patients with arrhythmias to automatically detect and classify arrhythmia heartbeats using electrocardiogram (ECG) signals. In this paper, we develop three robust deep convolutional neural network (DCNN) models, including a plain-CNN network and two MSF(multi-scale fusion)-CNN architectures (A and B), to aid in better feature extraction for the detection of arrhythmia and thus significantly improve the performance metrics. The proposed models are trained and tested with a public MIT-BIH arrhythmia database on five types of signals. Six groups of ablation experiments are conducted to analyze the performance of the models. The accuracy, sensitivity, and specificity obtained from MSF-CNN architecture A are higher than those from the plain-CNN model, demonstrating that the different parallel group convolution blocks (1×3, 1×5, and 1×7) dramatically improve a model’s performance. Additionally, the best model MSF-CNN architecture B achieves an average accuracy, sensitivity, and specificity of 98.00%, 96.17%, and 96.38%, respectively. This illustrates the method with residual learning and concatenation group convolution blocks has a profound effect on the feature learning of the model. The results of ablation experiments show that our proposed biometric recognition and diagnosis network with residual learning (MSF-CNN B) achieves a rapid and reliable diagnosis approach on ECG signal classification, which has the potential for introduction into clinical practice as an excellent tool for aiding cardiologists in reading ECG heartbeat signals.

Electrocardiogram (ECG) is the most extensively applied diagnostic approach for heart diseases. However, an ECG signal is a weak bioelectrical signal and is easily disturbed by baseline wander, powerline interference, and muscle artefacts, which make detection of heart diseases more difficult. Therefore, it is very important to denoise the contaminated ECG signal in practical application. In this article, an effective ECG segments denoising method combining the ensemble empirical mode decomposition (EEMD), empirical mode decomposition (EMD), and wavelet packet (WP) is designed. The ECG signal is decomposed using the EEMD for the first time, and then the highest frequency component is decomposed by the EMD for the second time, and the high frequency components obtained from the second time are decomposed and reconstructed by the WP for the third time. Finally, the processed signal components are fused to obtain the denoised ECG signal. Furthermore, the signal‐to‐noise ratio (SNR), mean square error (MSE), root mean square error (RMSE), and normalised cross correlation coefficient (R) are used to evaluate the noise reduction algorithm. The mean SNR, MSE, RMSE, and R are 5.7427, 0.0071, 0.0551, and 0.9050 in the China Physiological Signal Challenge 2018 dataset. Compared with others denoising methods, the experimental results not only exhibit that the SNR of the ECG signal is effectively improved, but also show that the details of the ECG signal are fully retained, laying a solid foundation for the automatic detection of ECG segments.

With the aggravation of population aging, the number of atrial fibrillation (AF) has increased dramatically, seriously endangering human health. Therefore, in order to detect AF on wearable devices accurately, parametric power spectrum estimation (PSD) based on autoregressive (AR) model was used to extract and analyze the frequency-domain features of short-term AF recordings. By comparing classical PSD with parameter PSD based on AR model for short-term AF signals with baseline wandering removed, it was found that this algorithm can estimate real signals, and the PSD results of AF signals and sinus rhythm signals are distinct. Moreover, the influence of model order for PSD was analyzed. Simulation results show that the parametric PSD based on AR model can accurately extract the frequency-domain characteristics of AF signals and is an effective method.

At present, the application of gas insulated metal enclosed switchgear (GIS) in power system is used more and more widely, but the detection of GIS is often disturbed by various kinds of noise, which affects the effective location of the fault point. Commonly it can not reflect the frequency characteristics of the signal locally for using Fourier denoising method, moreover, it is difficult to meet the requirements of the actual detection. Based on this problem, the system used the soft and hard threshold method to set the threshold value of the vibration signal. And the method of wavelet analysis with multi resolution characteristics was used to reduce the noise effectively. Through the Matlab simulation and the actual test results to verify its accuracy, it proves that denoising method based on wavelet analysis is a suitable way to extract useful signals, improve Signal to Noise Ratio and locating accuracy.