Cardiovascular disease diagnosis using cross-domain transfer learning

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Medicine
• Cardiology

Background:

• Cardiovascular diseases (CVDs) diagnosis relies on electrocardiogram (ECG) interpretation, often manual.
• Automated CVD detection using ECGs faces challenges with feature generalization due to varying acquisition parameters.
• Current deep learning (DL) methods for ECG analysis require extensive data and computational resources for training from scratch.

Purpose of the Study:

• To develop an automated, data-driven approach for CVD classification from ECG waveforms.
• To overcome limitations of hand-crafted features and resource-intensive DL models.
• To propose an end-to-end trainable cross-domain transfer learning framework for CVD detection.

Main Methods:

• Utilized pre-existing Convolutional Neural Network (CNN) frameworks, originally designed for vision tasks, as feature extractors.
• Employed a stacked spectrogram representation of multi-lead ECG waveforms as a preprocessing step for CNN compatibility.
• Developed a fusion strategy for multiple ECG leads by stacking spectrograms to capture spatial relationships.

Main Results:

• Achieved competitive performance in CVD classification across multiple ECG datasets.
• Demonstrated the efficacy of cross-domain transfer learning for ECG analysis.
• Validated the proposed stacked spectrogram and multi-lead fusion approach.

Conclusions:

• The proposed cross-domain transfer learning method offers an efficient and effective approach for automated CVD classification from ECGs.
• This method addresses the limitations of traditional feature engineering and training deep learning models from scratch.
• The use of stacked spectrograms and lead fusion provides a robust way to encode ECG information for improved diagnostic accuracy.