Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics.

Area of Science:

• Cardiology
• Artificial Intelligence
• Signal Processing

Background:

• Automatic diagnosis of electrocardiogram (ECG) signals is crucial for clinical practice.
• The PhysioNet/Computing in Cardiology Challenge 2020 provided a large dataset for evaluating diagnostic algorithms.
• Accurate ECG interpretation requires expertise and can be time-consuming.

Purpose of the Study:

• To develop and validate simple techniques for automatic ECG diagnosis.
• To compare a classical rule-based method with a convolutional deep learning architecture.
• To classify 24 different diagnostic classes using 12-lead ECG recordings.

Main Methods:

• A rule-based method using morphological and time-frequency ECG descriptors.
• A deep learning method employing convolutional neural networks (CNNs) with GoogLeNet topology.
• ECG signal processing via continuous wavelet transform to generate time-frequency images (scalograms) for CNN training.

Main Results:

• The rule-based method achieved a challenge validation score of 0.325 (35 min CPU time).
• The deep learning method achieved a challenge validation score of 0.426 (1664 min CPU time).
• The deep learning method secured 12th place among 1395 submitted algorithms.

Conclusions:

• Both rule-based and deep learning methods show promise for automatic ECG diagnosis.
• Deep learning architectures, particularly CNNs, demonstrate superior diagnostic accuracy for complex ECG classification tasks.
• The study highlights the potential of advanced AI techniques in augmenting cardiological diagnostics.