Artificial neural networks and ECG interpretation. Use and abuse

Area of Science:

• Cardiology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Computerized interpretation of resting electrocardiograms (ECG) has reached a mature stage, balancing sensitivity and specificity.
• Emerging computational techniques, including expert systems and artificial neural networks (ANNs), are being explored for ECG analysis.

Purpose of the Study:

• To evaluate the current state and future potential of advanced computational methods in electrocardiogram interpretation.
• To highlight progress and challenges in applying techniques like neural networks to ECG data.

Main Methods:

• Review of current advancements in computerized ECG interpretation.
• Exploration of applications of artificial neural network technology in specific ECG subdomains.
• Discussion of factors influencing user acceptance of automated ECG analysis tools.

Main Results:

• Stable performance achieved in current computerized resting ECG interpretation.
• Demonstrated progress in ANN applications for ECG signal filtering, classification, and stress ECG compression.
• Identified user acceptance as a key barrier to widespread adoption.

Conclusions:

• While ANNs offer advanced capabilities in ECG analysis, their complex nature presents challenges.
• Further rigorous studies are necessary to build confidence and encourage the adoption of these new technologies by clinicians.