Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing.

Fetal heart monitoring, as a crucial part of fetal monitoring, can accurately reflect the fetus’s health status in a timely manner. To address the issues of high computational cost, inability to observe fetal heart morphology, and insufficient accuracy associated with the traditional method of calculating the fetal heart rate using a four-channel maternal electrocardiogram (ECG), a method for extracting fetal QRS complexes from a single-channel non-invasive fetal ECG based on a multi-feature fusion neural network is proposed. Firstly, a signal entropy data quality detection algorithm based on the blind source separation method is designed to select maternal ECG signals that meet the quality requirements from all channel ECG data, followed by data preprocessing operations such as denoising and normalization on the signals. After being segmented by the sliding window method, the maternal ECG signals are calculated as data in four modes: time domain, frequency domain, time–frequency domain, and data eigenvalues. Finally, the deep neural network using three multi-feature fusion strategies—feature-level fusion, decision-level fusion, and model-level fusion—achieves the effect of quickly identifying fetal QRS complexes. Among the proposed networks, the one with the best performance has an accuracy of 95.85% and sensitivity of 97%.

Premature ventricular contraction (PVC) is a common cardiac arrhythmia that can occur in ordinary healthy people and various heart disease patients. Clinically, cardiologists usually use a long-term electrocardiogram (ECG) as a medium to detect PVC. However, it is time-consuming and labor-intensive for cardiologists to analyze the long-term ECG accurately. To this end, this paper suggests a simple but effective approach to search for PVC from the long-term ECG. The recommended method first extracts each heartbeat from the long-term ECG by applying a fixed time window. Subsequently, the model based on the one-dimensional convolutional neural network (CNN) tags these heartbeats without any preprocessing, such as denoise. Unlike previous PVC detection methods that use hand-crafted features, the proposed plan rationally and automatically extracts features and identify PVC with supervised learning. The proposed PVC detection algorithm acquires 99.64% accuracy, 96.97% sensitivity, and 99.84% specificity for the MIT-BIH arrhythmia database. Besides, when the number of samples in the training set is 3.3 times that of the test set, the proposed method does not misjudge any heartbeat from the test set. The simulation results show that it is reliable to use one-dimensional CNN for PVC recognition. More importantly, the overall system does not rely on complex and cumbersome preprocessing.

Fetal heart monitoring, as a crucial part of fetal monitoring, can timely and accurately reflect the fetus's health status. To address the issues of high computational cost, inability to observe fetal heart morphology, and insufficient accuracy associated with the traditional method of calculating fetal heart rate using a four-channel maternal electrocardiogram (ECG), a method for extracting fetal QRS complexes from a single-channel non-invasive fetal ECG based on a multi-feature fusion neural network is proposed. Firstly, a signal entropy data quality detection algorithm based on the blind source separation method is designed to select maternal ECG signals that meet the quality requirements from all channel ECG data, followed by data preprocessing operations such as denoising and normalization on the signals. After being segmented by the sliding window method, the maternal ECG signals are calculated as data in four modes: time domain, frequency domain, time-frequency domain, and data eigenvalues. Finally, the deep neural network using three multi-feature fusion strategies—feature-level fusion, decision-level fusion, and model-level fusion—achieves the effect of quickly identifying fetal QRS complexes. Among the proposed networks, the one with the best performance has an accuracy of 95.85%, sensitivity of 97%, specificity of 95%, and PPV (Positive Predictive Value) of 95%. This method, employing the sliding window technique and lightweight deep neural networks, can quickly and accurately identify fetal QRS complexes from single-channel maternal ECG signals, laying the foundation for home-based fetal QRS shape recognition and fetal risk prediction.

Physiological signal is readings or measurements that collect from the human body through various sensors. These signals are easily contaminated by noise and usually contain both nonperiodic and periodic components. As a new transform analysis method, wavelet transform can automatically adapt to the requirements of time-frequency signal analysis to focus on any details of the signal. This paper takes ECG as an example to discuss the application of wavelet transform to physiological signal analysis. Also, the experimental results show that the Gaussian wavelets family is suitable for physiological signal analysis.