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Purpose: Wavelet denoising is one of the denoising methods commonly used for ECG signals. However, due to the frequency overlap between the EMG and ECG, the feeble characteristics of ECG signals exists the risk of being weakened in the process of filtering noise. This paper presents a method of modified wavelet design and applies it to the denoising of ECG signals. Materials and methods: The optimized filter coefficients are obtained by approximating the amplitude-frequency response of the ideal filter, and the wavelet is constructed with the optimized filter coefficients. The algorithm is tested by clinical ECG data. Results: The results show that the proposed denoising method can remove the high-frequency noise effectively and enhance the characteristic information of P waves and T waves, and retain the characteristic information of the atrial fibrillation signals simultaneously. Compared with db4 and sym4 wavelets, the proposed wavelet can improve the signal to noise ratio and reduce the mean square error effectively at the same time. Conclusion: The modified wavelet design method proposed in this paper can effectively remove high-frequency noise while retaining and enhancing weak features. It provides a theoretical guidance for the de-noising of ECG signals in mobile medicine and also provides a way for other types of weak feature signal denoising.

As one of the key parts of rotary machine, the fault diagnosis and running condition monitoring of rolling bearings are of great importance for normal working and safe production of rotary machine. However, the traditional diagnosis approaches merely count on artificial feature extraction and domain expertise. Meanwhile, the existing convolutional neural networks (CNNs) have the problem of low fault recognition rates. This paper proposes a novel convolutional neural network with one-dimensional structure (ODCNN) for the automatical fault diagnosis of rolling bearings, which adopts six sets of convolutional and max-pooling layers to extract signal features and applies a flattening convolutional layer followed by two fully-connected layers for feature classification. The architectures of one-dimensional LeNet-5, AlexNet, and the proposed ODCNN are illustrated in detail, followed by the obtaining of training and testing samples, which is pre-processed by overlapping the vibration signals of rolling bearings. Finally, the classification experiment is carried out. The experimental results show that the ODCNN has higher fault diagnosis rates and can achieve high accuracy with load variant. Additionally, the extracted features of three CNNs are visualized, which illustrate that the new CNN has a better classification capacity.

(1) Background and objective: Cardiovascular disease is one of the most common causes of death in today’s world. ECG is crucial in the early detection and prevention of cardiovascular disease. In this study, an improved deep learning method is proposed to diagnose abnormal and normal ECG accurately. (2) Methods: This paper proposes a CNN-FWS that combines three convolutional neural networks (CNN) and recursive feature elimination based on feature weights (FW-RFE), which diagnoses abnormal and normal ECG. F1 score and Recall are used to evaluate the performance. (3) Results: A total of 17,259 records were used in this study, which validated the diagnostic performance of CNN-FWS for normal and abnormal ECG signals in 12 leads. The experimental results show that the F1 score of CNN-FWS is 0.902, and the Recall of CNN-FWS is 0.889. (4) Conclusion: CNN-FWS absorbs the advantages of convolutional neural networks (CNN) to obtain three parts of different spatial information and enrich the learned features. CNN-FWS can select the most relevant features while eliminating unrelated and redundant features by FW-RFE, making the residual features more representative and effective. The method is an end-to-end modeling approach that enables an adaptive feature selection process without human intervention.

Noise in ECG signals will affect the result of post-processing if left untreated. Since ECG is highly subjective, the linear denoising method with a specific threshold working well on one subject could fail on another. Therefore, in this Letter, sparse-based method, which represents every segment of signal using different linear combinations of atoms from a dictionary, is used to denoise ECG signals, with a view to myoelectric interference existing in ECG signals. Firstly, a denoising model for ECG signals is constructed. Then the model is solved by matching pursuit algorithm. In order to get better results, four kinds of dictionaries are investigated with the ECG signals from MIT-BIH arrhythmia database, compared with wavelet transform (WT)-based method. Signal–noise ratio (SNR) and mean square error (MSE) between estimated signal and original signal are used as indicators to evaluate the performance. The results show that by using the present method, the SNR is higher while the MSE between estimated signal and original signal is smaller.

Feature extraction is a crucial aspect of computer-aided arrhythmia diagnosis using an electrocardiogram (ECG). A location, width and magnitude (LWM) model is proposed for extracting each wave's features in the ECG. The model is a stream of Gaussian function in which three parameters (the expected value, variance and amplitude) are applied to approximate the P wave, QRS wave and T wave. Moreover, the features such as the P-Q intervals, S-T intervals, and so on are easily obtained. Then, a mixed approach is presented for estimating the parameters of a real ECG signal. To illustrate this model's associated advantages, the extracted parameters combined with R-R intervals are fed to three classifiers for arrhythmia diagnoses. Two kinds of arrhythmias, including the premature ventricular contraction (PVC) heartbeats and the atrial premature complexes (APC) heartbeats, are diagnosed from normal beats using the data from the MIT-BIH arrhythmia database. The results in this study demonstrate that using these parameters results in more accurate and universal arrhythmia diagnoses.

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