An Electrocardiogram Multi-Task Benchmark with Comprehensive Evaluations and Insightful Findings.

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Cardiology

Background:

• Non-invasive measurements like Electrocardiogram (ECG) are crucial for patient diagnosis.
• Analyzing ECG data typically requires specialized domain expertise, limiting AI applications in healthcare.
• Foundation models offer a potential solution by acquiring domain knowledge without extensive human input.

Purpose of the Study:

• To evaluate the effectiveness of foundation models for ECG analysis.
• To compare the performance of language, general time-series, and ECG-specific foundation models against traditional deep learning models.
• To address the gap in comprehensive performance analyses of foundation models on ECG data.

Main Methods:

• Evaluation of various foundation models (language, general time-series, ECG-specific) on ECG datasets.
• Comparison of foundation model performance against established time-series deep learning models.
• In-depth analysis of experimental results to understand model capabilities and limitations.

Main Results:

• General time-series and ECG-specific foundation models achieved a top performance rate of 80% in ECG analysis.
• Foundation models demonstrated significant effectiveness, comparable to or exceeding traditional methods.
• Comprehensive experimental results and insights into model performance were provided.

Conclusions:

• Foundation models are effective tools for ECG analysis, offering a path to overcome domain expertise barriers.
• These models hold significant potential for advancing the analysis of physiological waveforms.
• Further research is needed to fully explore the limitations and potential of foundation models in this domain.