用物理启发的轻量模型，高效准确分割心电图波形。
Physics-Based Explainable AI for ECG Segmentation: A Lightweight Model
- 结合谱分析与概率预测，简化结构捕捉波形时频特征。
- QRS波分割准确率97.00%，T波93.33%，P波96.07%。
- 引入可解释AI，让模型决策过程透明可信，适合临床应用。
心脏电活动通过心电图（ECG）记录，对诊断心血管疾病至关重要。然而，现有许多ECG分割模型依赖复杂的多层架构（如BiLSTM），计算开销大且效率低。本研究提出一种简化架构，融合谱分析与概率预测，用于ECG信号分割。通过用简单层替代复杂结构，模型有效捕捉了P波、QRS波和T波的时序与频域特征。同时，采用可解释人工智能（XAI）方法，揭示时序与频率特征如何影响分割决策。基于物理启发的AI原理，该方法使模型决策过程清晰可懂，提升了分析的可靠性与透明度。实验结果表明，该方法在分割精度上表现优异：QRS波准确率达97.00%，T波93.33%，P波96.07%。结果证明，该轻量化设计不仅提升计算效率，还实现精准分割，是心电信号监测的实用有效方案。
原文摘要 · Abstract (English)
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.
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