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

Physics - Based Explainable AI for ECG Segmentation: A Lightweight Model Muhammad Fathur Rohman Sidiq Department of Physics, Faculty of Mathematics and Science, Brawijaya University, Malang, Indonesia Abdurrouf Department of Physics, Faculty of Mathematics and Science, Brawijaya University, Malang, Indonesia Didik Rahadi Santoso * Department of Physics, Faculty of Mathematics and Science, Brawijaya University, Malang, Indonesia * Corresponding author. E - mail: dieks@ub.ac.id 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 ar e computationally intensive and inefficient. This study introduces a streamlined architecture that combines spectral analysis with probabilistic predictions for ECG signal segmentation. 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 i ncorporating principles from physics - based AI, this method provides a clear understanding of the decision - making process, ensuring reliability and transparency in ECG analysis.