摘 要
心电图（ECG）信号作为反映心脏电生理活动的重要指标，广泛应用于心血管疾病的诊断与监测。然而，由于心律失常类型多样且信号特征复杂，传统方法在特征提取和分类识别方面存在局限性。为解决这一问题，本研究提出了一种基于深度学习与优化特征选择的心电图信号处理框架，旨在提高心律失常的识别精度。研究首先通过小波变换对原始心电图信号进行预处理，以增强信号质量并减少噪声干扰；随后利用卷积神经网络（CNN）自动提取多尺度时频特征，并结合长短期记忆网络（LSTM）捕捉时间序列中的动态依赖关系。此外，引入一种改进的遗传算法对特征子集进行优化选择，从而有效降低维度冗余并提升模型泛化能力。
关键词：心电图信号处理 深度学习 特征选择
Abstract
ECG (ECG) signal, as an important indicator reflecting cardiac electrophysiological activity, is widely used in the diagnosis and monitoring of cardiovascular diseases. However, due to the variety of arrhythmia types and complex signal features, the traditional methods have limitations in feature extraction and classification identification. To address this problem, this study proposes an ECG signal processing fr amework based on deep learning and optimized feature selection, aiming to improve the identification accuracy of cardiac arrhythmias. The study first preprocessed the original ECG signal by wavelet transform to enhance signal quality and reduce noise interference; then used convolutional neural network (CNN) to automatically extract multiscale time-frequency features and capture dynamic dependence in time series with long short-term memory network (LSTM). Moreover, an improved genetic algorithm is introduced to optimize the feature subsets, thus effectively reducing the dimensional redundancy and improving the model generalization ability.
Keyword:Electrocardiogram Signal Processing Deep Learning Feature Selection
目 录
1绪论 1
1.1心电图信号研究背景与意义 1
1.2国内外研究现状分析 1
1.3本文研究方法概述 1
2心电图信号预处理技术 2
2.1信号去噪方法研究 2
2.2基线漂移校正技术 2
2.3数据标准化与归一化 3
2.4预处理效果评估指标 3
3心电图特征提取方法 4
3.1时域特征提取技术 4
3.2频域特征分析方法 4
3.3小波变换在特征提取中的应用 5
3.4特征选择与降维技术 5
4心律失常识别算法研究 6
4.1常见心律失常类型分析 6
4.2机器学习分类算法应用 7
4.3深度学习模型的构建与优化 7
4.4算法性能评价与比较 8
结论 8
参考文献 10
致谢 11
