A Review on Intelligent Systems for ECG Analysis: From Flexible Sensing Technology to Machine Learning

Area of Science:

• Biomedical Engineering
• Materials Science
• Data Science

Background:

• Flexible cardiac sensing devices are crucial for continuous electrocardiogram (ECG) monitoring.
• Key device characteristics include flexibility, durability, biocompatibility, sensitivity, and stretchability.
• Advancements in flexible electrode fabrication are essential for improved performance.

Purpose of the Study:

• To conduct an extensive review of flexible cardiac sensing devices for ECG acquisitions.
• To emphasize their application in cardiac health monitoring.
• To explore the integration of flexible sensing technology with machine learning (ML).

Main Methods:

• Comprehensive literature review of flexible cardiac sensing devices and ECG analysis.
• Analysis of fabrication methods and materials for flexible electrodes.
• Review of machine learning algorithms applied to ECG data.

Main Results:

• Flexible cardiac sensors exhibit promising properties for wearable health monitoring.
• Machine learning, particularly deep learning, significantly enhances ECG analysis for disease classification and other applications.
• The integration of flexible sensing and ML offers advanced ECG-based health monitoring.

Conclusions:

• Flexible cardiac sensing technology and ML are pivotal for the future of cardiac health monitoring.
• Further research is needed to optimize device performance and ML algorithms.
• The synergy between these technologies promises significant advancements in non-invasive cardiac diagnostics.