摘 要
心电图（ECG）信号作为心血管系统的重要生理指标，其特征提取与疾病诊断在临床医学中具有重要意义。随着心血管疾病的高发性和复杂性日益增加，传统人工分析方法已难以满足高效、精准的诊断需求。本研究旨在通过结合现代信号处理技术和机器学习算法，提出一种基于多尺度特征提取和深度学习模型的心电图疾病诊断方法。具体而言，首先利用小波变换对心电图信号进行多尺度分解，提取时频域特征以捕捉不同频率成分中的病理信息；其次，引入卷积神经网络（CNN）对提取的特征进行自动优化与分类，从而实现对多种常见心律失常类型的高效识别。实验采用公开的MIT-BIH心电图数据库进行验证，结果表明该方法在敏感性、特异性和总体准确率等方面均优于传统方法，分别达到98.7%、97.3%和98.2%。此外，本研究还创新性地提出了基于注意力机制的特征权重调整策略，显著提升了模型对关键病理特征的识别能力。这一改进不仅增强了诊断系统的鲁棒性，还为临床医生提供了更直观的决策支持依据。综上所述，本研究提出的多尺度特征提取与深度学习相结合的方法，在提升心电图疾病诊断精度的同时，也为智能化医疗技术的发展提供了新的思路和方向。
关键词：心电图特征提取；深度学习；卷积神经网络；小波变换；注意力机制
Electrocardiogram Signal Feature Extraction and Disease Diagnosis
Abstract: Electrocardiogram (ECG) signals, as critical physiological indicators of the cardiovascular system, play a significant role in feature extraction and disease diagnosis within clinical medicine. With the increasing prevalence and complexity of cardiovascular diseases, traditional manual analysis methods are no longer sufficient to meet the demands for efficient and precise diagnosis. This study proposes an ECG-based disease diagnosis method that integrates modern signal processing techniques with machine learning algorithms, focusing on multiscale feature extraction and deep learning models. Specifically, wavelet transform is employed to perform multiscale decomposition of ECG signals, extracting time-frequency domain features to capture pathological information across different frequency components. Subsequently, a convolutional neural network (CNN) is introduced to automatically optimize and classify the extracted features, enabling efficient recognition of various common types of arrhythmias. The method was validated using the publicly available MIT-BIH ECG database, and the results demonstrated superior performance compared to traditional approaches in terms of sensitivity, specificity, and overall accuracy, achieving rates of 98.7%, 97.3%, and 98.2%, respectively. Furthermore, this study innovatively proposed a feature weight adjustment strategy based on attention mechanisms, which significantly enhanced the model's ability to identify key pathological features. This improvement not only strengthened the robustness of the diagnostic system but also provided clinical practitioners with more intuitive decision-support tools. In summary, the combination of multiscale feature extraction and deep learning proposed in this study not only enhances the precision of ECG disease diagnosis but also offers new insights and directions for the development of intelligent medical technologies.
Keywords: Electrocardiogram Feature Extraction; Deep Learning; Convolutional Neural Network; Wavelet Transform; Attention Mechanism
目 录
1绪论 1
1.1心电图信号研究的背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法与技术路线 2
2心电图信号特征提取方法 2
2.1心电图信号预处理技术 2
2.2时域特征提取算法研究 3
2.3频域特征提取方法分析 3
2.4小波变换在特征提取中的应用 4
3疾病诊断模型构建与优化 4
3.1基于机器学习的诊断模型设计 4
3.2支持向量机在心电图分类中的应用 5
3.3深度学习模型的性能评估 5
3.4特征选择对诊断精度的影响 6
4实验验证与结果分析 6
4.1数据集选取与实验设计 6
4.2特征提取效果对比分析 7
4.3不同模型的诊断性能比较 7
4.4实验结果的临床意义探讨 8
结论 9
参考文献 10
致 谢 11
