融合神经机制的自适应算法，提升心电图分类准确率
Neuro-Informed Adaptive Learning (NIAL) Algorithm: A Hybrid Deep Learning Approach for ECG Signal Classification
- 结合CNN与注意力机制，动态调整学习率以适应信号变化
- 在MIT-BIH和PTB数据集上表现优于传统方法
- 适合实时心脏监测场景，对临床诊断有实用价值
利用心电图（ECG）信号检测心脏异常对心血管疾病的早期诊断和干预至关重要。传统深度学习模型往往难以适应不同信号模式。本研究提出神经启发自适应学习（NIAL）算法，融合卷积神经网络（CNN）与基于Transformer的注意力机制，提升ECG信号分类性能。该算法根据实时验证性能动态调整学习率，确保高效收敛。在MIT-BIH心律失常数据集和PTB诊断心电图数据集上的实验表明，模型分类精度显著高于传统方法。结果表明，NIAL在实时心血管监测应用中具有巨大潜力。
原文摘要 · Abstract (English)
The detection of cardiac abnormalities using electrocardiogram (ECG) signals is crucial for early diagnosis and intervention in cardiovascular diseases. Traditional deep learning models often lack adaptability to varying signal patterns. This study introduces the Neuro-Informed Adaptive Learning (NIAL) algorithm, a hybrid approach integrating convolutional neural networks (CNNs) and transformer-based attention mechanisms to enhance ECG signal classification. The algorithm dynamically adjusts learning rates based on real-time validation performance, ensuring efficient convergence. Using the MIT-BIH Arrhythmia and PTB Diagnostic ECG datasets, our model achieves high classification accuracy, outperforming conventional approaches. These findings highlight the potential of NIAL in real-time cardiovascular monitoring applications.
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