Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Heart diseases, including myocardial ischemia (MI), are leading causes of mortality.
• Early detection and treatment of MI and arrhythmia are crucial for patient outcomes.
• Electrocardiogram (ECG) is a primary diagnostic tool for heart conditions.

Purpose of the Study:

• To review the progress of portable ECG monitoring equipment and associated detection algorithms.
• To explore the application of polymer material sensors and deep learning in ECG analysis.
• To identify challenges and future directions in ECG-based heart disease diagnosis.

Main Methods:

• Review of current portable ECG monitoring devices and polymer material sensors.
• Analysis of deep learning algorithms applied to ECG data for disease detection.
• Comparison of prediction results from various classifiers using recognized ECG datasets.

Main Results:

• Portable ECG devices are advancing with new sensor technologies.
• Deep learning methods show promise in ECG diagnosis, but face challenges.
• Key issues identified include data imbalance and high computational demands.

Conclusions:

• Generative adversarial networks (GANs) can enhance ECG database quality.
• Development of lightweight ECG diagnostic algorithms is essential for portable devices.
• Future research should focus on improving data quality and algorithmic efficiency for real-time monitoring.