Cardiovascular diseases and psychological disorders represent two major threats to human physical and mental health. Research on electrocardiogram (ECG) signals offers valuable opportunities to address these issues. However, existing methods are constrained by limitations in understanding ECG features and transferring knowledge across tasks. To address these challenges, this study developed a multi-resolution feature encoding network based on residual networks, which effectively extracted local morphological features and global rhythm features of ECG signals, thereby enhancing feature representation. Furthermore, a model compression-based continual learning method was proposed, enabling the structured transfer of knowledge from simpler tasks to more complex ones, resulting in improved performance in downstream tasks. The multi-resolution learning model demonstrated superior or comparable performance to state-of-the-art algorithms across five datasets, including tasks such as ECG QRS complex detection, arrhythmia classification, and emotion classification. The continual learning method achieved significant improvements over conventional training approaches in cross-domain, cross-task, and incremental data scenarios. These results highlight the potential of the proposed method for effective cross-task knowledge transfer in ECG analysis and offer a new perspective for multi-task learning using ECG signals.
Humans ; Electrocardiography/methods* ; Mental Health ; Algorithms ; Signal Processing, Computer-Assisted ; Machine Learning ; Arrhythmias, Cardiac/diagnosis* ; Cardiovascular Diseases ; Neural Networks, Computer ; Mental Disorders

Deep learning method can be used to automatically analyze electrocardiogram (ECG) data and rapidly implement arrhythmia classification, which provides significant clinical value for the early screening of arrhythmias. How to select arrhythmia features effectively under limited abnormal sample supervision is an urgent issue to address. This paper proposed an arrhythmia classification algorithm based on an adaptive multi-feature fusion network. The algorithm extracted RR interval features from ECG signals, employed one-dimensional convolutional neural network (1D-CNN) to extract time-domain deep features, employed Mel frequency cepstral coefficients (MFCC) and two-dimensional convolutional neural network (2D-CNN) to extract frequency-domain deep features. The features were fused using adaptive weighting strategy for arrhythmia classification. The paper used the arrhythmia database jointly developed by the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) and evaluated the algorithm under the inter-patient paradigm. Experimental results demonstrated that the proposed algorithm achieved an average precision of 75.2%, an average recall of 70.1% and an average F ₁-score of 71.3%, demonstrating high classification accuracy and being able to provide algorithmic support for arrhythmia classification in wearable devices.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Algorithms ; Electrocardiography/methods* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Deep Learning ; Classification Algorithms

Electrocardiogram (ECG) is a low-cost, simple, fast, and non-invasive test. It can reflect the heart's electrical activity and provide valuable diagnostic clues about the health of the entire body. Therefore, ECG has been widely used in various biomedical applications such as arrhythmia detection, disease-specific detection, mortality prediction, and biometric recognition. In recent years, ECG-related studies have been carried out using a variety of publicly available datasets, with many differences in the datasets used, data preprocessing methods, targeted challenges, and modeling and analysis techniques. Here we systematically summarize and analyze the ECG-based automatic analysis methods and applications. Specifically, we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes. Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications. Finally, we elucidated some of the challenges in ECG analysis and provided suggestions for further research.
Humans ; Arrhythmias, Cardiac/diagnosis* ; Electrocardiography/methods* ; Algorithms

In view of the high incidence of malignant diseases such as malignant arrhythmias in the elderly population, accidental injuries such as falls, and the problem of no witnesses when danger occurs, the study developed a human vital signs and body posture monitoring and positioning alarm system. Through the collection and analysis of electrocardiogram (ECG), respiration (RESP) and acceleration (ACC) signals, the system monitors human vital signs and body posture in real time, automatically judges critical states such as malignant arrhythmias and accidental falls on the local device side, and then issues alarm information, opens the positioning function, and uploads physiological information and patient location information through 4G communication. Experiments have shown that the system can accurately determine the occurrence of ventricular fibrillation and falls, and issue position and alarm information.
Humans ; Aged ; Arrhythmias, Cardiac/diagnosis* ; Ventricular Fibrillation ; Electrocardiography ; Accidental Falls ; Vital Signs ; Posture ; Monitoring, Physiologic

Humans ; COVID-19/complications* ; Arrhythmias, Cardiac/diagnosis* ; Anti-Arrhythmia Agents/therapeutic use*

The judgment of the type of arrhythmia is the key to the prevention and diagnosis of early cardiovascular disease. Therefore, electrocardiogram (ECG) analysis has been widely used as an important basis for doctors to diagnose. However, due to the large differences in ECG signal morphology among different patients and the unbalanced distribution of categories, the existing automatic detection algorithms for arrhythmias have certain difficulties in the identification process. This paper designs a variable scale fusion network model for automatic recognition of heart rhythm types. In this study, a variable-scale fusion network model was proposed for automatic identification of heart rhythm types. The improved ECG generation network (EGAN) module was used to solve the imbalance of ECG data, and the ECG signal was reproduced in two dimensions in the form of gray recurrence plot (GRP) and spectrogram. Combined with the branching structure of the model, the automatic classification of variable-length heart beats was realized. The results of the study were verified by the Massachusetts institute of technology and Beth Israel hospital (MIT-BIH) arrhythmia database, which distinguished eight heart rhythm types. The average accuracy rate reached 99.36%, and the sensitivity and specificity were 96.11% and 99.84%, respectively. In conclusion, it is expected that this method can be used for clinical auxiliary diagnosis and smart wearable devices in the future.
Algorithms ; Arrhythmias, Cardiac/diagnosis* ; Databases, Factual ; Electrocardiography/methods* ; Heart Rate ; Humans

Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.
Algorithms ; Arrhythmias, Cardiac/diagnosis* ; Electrocardiography ; Humans ; Memory, Short-Term ; Neural Networks, Computer ; Signal Processing, Computer-Assisted

Arrhythmias, Cardiac ; Atrial Flutter/diagnosis* ; Electrocardiography ; Humans

OBJECTIVE: To assess the value of wearable 12-lead electrocardiogram (ECG) devices in pre-hospital diagnosis of acute ST segment elevation myocardial infarction (STEMI). METHODS: This analysis was conducted among 441 patients selected from the''National ECG Network'', who used wearable 12-lead ECG device with critical situation warning of ST change between January 2019, and August, 2021.The general characteristics, response time and complaints of the patients with STEMI were analyzed.The accuracy of pre-hospital diagnosis of STEMI was compared between clinician's interpretation of ECGs and AI diagnosis by the wearable ECG device. RESULTS: In 89 of the patients, a pre-hospital diagnosis of STEMI was made by physicians based on ECGs from the wearable devices, and 58 of them sought medical attention after online warning, with a referral rate of 65.17%.The average time for diagnostic assessment of the ECGs was 153.02 s, and the average time for confirmation of the diagnosis was 178.06 s.The sensitivity for pre-hospital diagnosis of STEMI by clinician's interpretation of the ECGs and by AI diagnosis was 100% and 88.37%, respectively, with a specificity of 95.40% and 79.31%, respectively.The pre-hospital diagnosis by clinicians and AI diagnosis of STEMI both showed a high consistency with the subsequent definite clinical diagnosis of STEMI. CONCLUSION Wearable 12-lead ECG devices can accurately record ECG characteristics of STEMI patients outside the hospital and allow immediate data uploading for an early diagnosis.The diagnoses of STEMI made based on AI technology are highly consistent with those by clinicians, demonstrating excellent clinical performance of the wearable ECG devices.
Humans ; ST Elevation Myocardial Infarction/diagnosis* ; Electrocardiography ; Arrhythmias, Cardiac ; Wearable Electronic Devices ; Hospitals

Sudden cardiac death(SCD),a serious public health problem facing China and the world,causes heavy social burden.It is reported that SCD accounts for 15%-20% of all the deaths and the proportion of SCD patients with non-cardiac disease is as high as 50%.Finding effective predictors to identify SCD early is particularly important for clinical practice.To date,non-invasive electrocardiologic examination has been the first choice for predicting the risks of fatal ventricular arrhythmias and SCD because of its safety and feasibility.This review summarizes the available relevant studies to expound the value of non-invasive electrocardiologic examination and indicators in predicting SCD.
Arrhythmias, Cardiac/diagnosis* ; China ; Death, Sudden, Cardiac ; Humans ; Risk Factors