Physics-Based Explainable AI for ECG Segmentation: A Lightweight Model

TL;DR

This paper presents a lightweight, physics-based explainable AI model for ECG segmentation that combines spectral analysis with probabilistic predictions, achieving high accuracy and interpretability while reducing computational complexity.

Contribution

The study introduces a simplified, physics-inspired architecture that enhances ECG segmentation accuracy and interpretability compared to complex existing models.

Findings

Achieved 97.00% accuracy for QRS wave segmentation

Attained 93.33% accuracy for T wave segmentation

Reached 96.07% accuracy for P wave segmentation

Abstract

The heart's electrical activity, recorded through Electrocardiography (ECG), is essential for diagnosing various cardiovascular conditions. However, many existing ECG segmentation models rely on complex, multi-layered architectures such as BiLSTM, which are computationally intensive and inefficient. This study introduces a streamlined architecture that combines spectral analysis with probabilistic predictions for ECG signal segmentation. By replacing complex layers with simpler ones, the model effectively captures both temporal and spectral features of the P, QRS, and T waves. Additionally, an Explainable AI (XAI) approach is applied to enhance model interpretability by explaining how temporal and frequency-based features contribute to ECG segmentation. By incorporating principles from physics-based AI, this method provides a clear understanding of the decision-making process, ensuring reliability and transparency in ECG analysis. This approach achieves high segmentation accuracy: 97.00% for the QRS wave, 93.33% for the T wave, and 96.07% for the P wave. These results indicate that the simplified architecture not only improves computational efficiency but also provides precise segmentation, making it a practical and effective solution for heart signal monitoring.