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

Heart rate variability time and frequency indices are widely used in functional assessment for autonomic nervous system (ANS). However, this method merely analyzes the effect of cardiac dynamics, overlooking the effect of cardio-pulmonary interplays. Given this, the present study proposes a novel cardiopulmonary coupling (CPC) algorithm based on cross-wavelet transform to quantify cardio-pulmonary interactions, and establish an assessment system for ANS aging effects using wearable electrocardiogram (ECG) and respiratory monitoring devices. To validate the superiority of the proposed method under nonstationary and low signal-to-noise ratio conditions, simulations were first conducted to demonstrate the performance strength of the proposed method to the traditional one. Next, the proposed CPC algorithm was applied to analyze cardiac and respiratory data from both elderly and young populations, revealing that young populations exhibited significantly stronger couplings in the high-frequency band compared with their elderly counterparts. Finally, a CPC assessment system was constructed by integrating wearable devices, and additional recordings from both elderly and young populations were collected by using the system, completing the validation and application of the aging effect assessment algorithm and the wearable system. In conclusion, this study may offers methodological and system support for assessing the aging effects on the ANS.
Humans ; Autonomic Nervous System/physiology* ; Algorithms ; Aging/physiology* ; Electrocardiography/methods* ; Heart Rate/physiology* ; Wavelet Analysis ; Aged ; Signal Processing, Computer-Assisted ; Wearable Electronic Devices

Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
Humans ; Heart Failure/physiopathology* ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Remote Sensing Technology ; Early Diagnosis ; Electrocardiography ; Hemodynamics

In recent years, cardiovascular disease has become a common disease. With the development of machine learning and big data technologies, the processing ability of electrocardiogram (ECG) signals has been greatly enhanced through new computer technologies, enabling the auxiliary diagnosis technology for cardiovascular disease (CVD) to achieve new improvements. This article discusses the application of machine learning in ECG processing, especially in the auxiliary diagnosis of diseases. Firstly, the conventional signal preprocessing methods are introduced, and then the EEG signal processing methods based on feature extraction and fuzzy classification are explored. Secondly, the application of auxiliary diagnosis in CVD is further summarized. Finally, the advantages and disadvantages of the two methods are analyzed, and based on this, a design of an auxiliary diagnostic system compatible with the two methods is proposed, providing a new perspective for similar applied researches in the future.
Machine Learning ; Cardiovascular Diseases/diagnosis* ; Humans ; Electrocardiography ; Signal Processing, Computer-Assisted ; Diagnosis, Computer-Assisted ; Fuzzy Logic ; Electroencephalography

Hemodynamic monitoring can reflect cardiac function and blood perfusion and is an indispensable monitoring method in clinical practice. Invasive hemodynamic monitoring methods represented by the thermodilution method are limited in their clinical application scope because they require vascular cannulation. Non-invasive hemodynamic monitoring has attracted extensive attention from medical companies and clinicians at home and abroad in recent years due to its advantages such as safety, non-invasiveness, continuous monitoring, simple operation, and low cost. This paper designs a non-invasive hemodynamic monitoring system based on the impedance cardiography, including hardware, algorithm, software design, and performance parameter evaluation. Among them, the hardware part mainly includes a differential high-frequency constant current source stimulation circuit, impedance cardiogram signal acquisition, and ECG signal acquisition circuit. Signal processing includes wave filtering, impedance cardiogram signal calibration, and ECG signal and impedance cardiogram signal feature point recognition. According to the collected impedance cardiogram and ECG signals, hemodynamic parameters such as heart rate (HR), stroke volume (SV), cardiac output (CO), stroke index (SI), cardiac index (CI), and cardiac contractility index (ICON) are calculated based on the Nyboer thoracic cylinder model. After testing, the key technical indicators of the system hardware are better than that of the relevant medical device standards. The system was used to collect impedance cardiogram and ECG signal data from 40 volunteers. The calculated HR, SV, and CO, three important hemodynamic indicators, were compared with the ICONCore non-invasive cardiac output monitor of OSYPKA Medical in Germany. Their Pearson correlation coefficients were 0.992 ( P<0.001), 0.948 ( P<0.001), and 0.933 ( P<0.001), respectively, verifying that the designed system has high accuracy and reliability.
Cardiography, Impedance/methods* ; Humans ; Hemodynamic Monitoring/methods* ; Equipment Design ; Signal Processing, Computer-Assisted ; Hemodynamics ; Algorithms ; Monitoring, Physiologic/methods* ; Electrocardiography

OBJECTIVE: To investigate cisplatin-induced cardiovascular toxicity and explore the protective effects and potential mechanism of curcumin co-treatment. METHODS: Forty adult male Sprague-Dawley rats were numbered and randomly divided into control group, cisplatin group (7.5 mg/kg, once a week, for 2 weeks), curcumin group (200 mg/kg per day, for 2 weeks) and cisplatin+curcumin group (cisplatin 7.5 mg/kg, once a week, and curcumin 200 mg/kg per day for 2 weeks) by a random number table method, with 10 rats in each group. Cardiac and vascular morphology and functions were assessed using hematoxylin-eosin and Masson's trichrome staining, serum indexes detection, echocardiography, electrocardiogram (ECG), blood pressure monitoring, vascular ring isometric tension measurement, and left ventricular pressure evaluation. The expressions of extracellular signal-regulated kinases (ERK) and phosphorylated-ERK (p-ERK) were analyzed by immunohistochemical staining. RESULTS: Cisplatin treatment induced notable cardiac alteration, as evidenced by changes in cardiac morphology, elevated serum enzymes (P<0.05), ECG abnormalities, and increased left ventricular end-diastolic pressure (P<0.05). Meanwhile, cisplatin significantly increased arterial pulse pressure (P<0.01), primarily due to a decrease in diastolic blood pressure. Severe fibrosis was also observed in the thoracic aorta wall. In vascular ring experiments, cisplatin treatment led to a significant reduction in phenylephrine-induced contraction (P<0.05) and acetylcholine-induced relaxation (P<0.01). Notably, Curcumin co-administration significantly alleviated cisplatin-induced cardiovascular damages, as demonstrated by improvement in these parameters. Furthermore, ERK expression in the myocardium and p-ERK expression in vascular smooth muscle cells were significantly upregulated following curcumin co-treatment. CONCLUSIONS Curcumin protects the heart and vasculature from cisplatin-induced damages, likely by upregulating ERK/p-ERK expression. These findings suggest that curcumin may serve as a promising therapeutic strategy for mitigating cisplatin-associated cardiovascular toxicity during tumor chemotherapy. In vitro cell culture experiments are needed to clarify the underlying mechanism.
Animals ; Curcumin/therapeutic use* ; Cisplatin/adverse effects* ; Rats, Sprague-Dawley ; Male ; Up-Regulation/drug effects* ; Extracellular Signal-Regulated MAP Kinases/metabolism* ; Phosphorylation/drug effects* ; Electrocardiography ; Blood Pressure/drug effects* ; Rats ; MAP Kinase Signaling System/drug effects*

BACKGROUND AND OBJECTIVE

COVID-19 has been associated with cardiac injury, often detectable through electrocardiographic (ECG) changes. This study seeks to characterize the cardiovascular and electrocardiographic profiles of adult patients diagnosed with COVID-19.

This study included adult patients with confirmed COVID-19 from June 2021 to June 2022. Clinical profiles and 12-lead ECG tracings were obtained from electronic medical records and reviewed independently by three cardiologists. Descriptive analysis was performed to summarize the cardiovascular and electrocardiographic findings in this population.

The study included 998 COVID-19 patients (mean age: 50 years; 53.7% male). The most common comorbidities were hypertension, diabetes, and dyslipidemia. A majority (31.36%) presented with severe COVID-19 infection. The most frequent significant ECG abnormalities observed at admission were sinus tachycardia (22.8%), and atrial fibrillation (11.02%). Additional ischemic findings included ST segment depression (2.91%), T-wave inversion (1.70%), and ST segment elevation (2.71%).

The baseline ECG findings among COVID-19 patients were predominantly normal; however, significant abnormalities were also identified. The most frequent abnormalities included sinus tachycardia, atrial fibrillation, and ischemic changes, all of which may have clinical implications.

In the field of medicine and cardiology, there is perhaps no other condition or situation that stimulates an adrenalin rush for the healthcare team than a patient presenting with wide QRS complex tachycardia. These cases may be potentially fatal and are usually associated with worse outcomes. While the real-world experience in the evaluation and management of these cases can be chaotic situations, a careful, systematic and organized scrutiny of the electrocardiographic tracing is key to obtaining a correct diagnosis and proceeding with the right therapeutic management. An understanding of the physiological mechanisms of arrhythmia, the appreciation of scientific basis for electrocardiographic features and recognition of different criteria for diagnosis provides endless opportunities and “teachable moments” in medicine. For both learners and teachers, the academic discussion of these points and features can be an exciting journey and electrifyingly educational experience. This article provides a simplified yet beautifully complicated approach to diagnosing wide complex tachycardia.

BACKGROUND AND OBJECTIVE

COVID-19 has been associated with cardiac injury, often detectable through electrocardiographic (ECG) changes. This study seeks to characterize the cardiovascular and electrocardiographic profiles of adult patients diagnosed with COVID-19.

This study included adult patients with confirmed COVID-19 from June 2021 to June 2022. Clinical profiles and 12-lead ECG tracings were obtained from electronic medical records and reviewed independently by three cardiologists. Descriptive analysis was performed to summarize the cardiovascular and electrocardiographic findings in this population.

The study included 998 COVID-19 patients (mean age: 50 years; 53.7% male). The most common comorbidities were hypertension, diabetes, and dyslipidemia. A majority (31.36%) presented with severe COVID-19 infection. The most frequent significant ECG abnormalities observed at admission were sinus tachycardia (22.8%), and atrial fibrillation (11.02%). Additional ischemic findings included ST segment depression (2.91%), T-wave inversion (1.70%), and ST segment elevation (2.71%).

The baseline ECG findings among COVID-19 patients were predominantly normal; however, significant abnormalities were also identified. The most frequent abnormalities included sinus tachycardia, atrial fibrillation, and ischemic changes, all of which may have clinical implications.