摘 要
生物信号处理在医疗诊断和健康监测中具有重要应用价值，然而信号采集过程中不可避免会受到噪声干扰，严重影响信号质量和后续分析。本研究旨在系统比较现有噪声抑制方法的性能，并提出一种基于自适应滤波与深度学习的混合降噪框架。研究首先对传统方法（如小波变换、卡尔曼滤波）和新兴深度学习方法（如卷积神经网络、循环神经网络）进行理论分析和实验验证，通过信噪比、均方误差等指标评估其在不同噪声环境下的表现。在此基础上，提出了一种结合自适应滤波预处理和深度残差网络的混合模型，该模型能够有效处理非平稳噪声并保留信号细节特征。实验结果表明，所提方法在心电、脑电等生物信号数据集上的降噪效果显著优于单一方法，平均信噪比提升约3.5dB，且具有较强的泛化能力。
关键词：生物信号降噪 自适应滤波 深度学习
Abstract
Biological signal processing has important application value in medical diagnosis and health monitoring, but it will inevitably be disturbed by noise in the signal process of signal acquisition, which seriously affects the signal quality and subsequent analysis. This study aims to systematically compare the performance of existing noise suppression methods and propose a hybrid noise reduction fr amework based on adaptive filtering and deep learning. Firstly, the theoretical analysis and experimental verification of traditional methods (such as wavelet transform, Kalman filter) and emerging deep learning methods (such as convolutional neural network and recurrent neural network), and evaluate their performance in different noise environments through signal-to-noise ratio, mean square error and other indicators. Furthermore, we propose a hybrid model combining adaptive filtering preprocessing and deep residual network, which can effectively deal with non-stationary noise and preserving signal detail features. The experimental results show that the proposed method is significantly better than the single method in the biological signal data sets, and the average SNR improves by about 3.5dB, and has strong generalization ability.
Keyword: Biosignal noise reduction adaptive-filtering deep learning
目 录
1绪论 1
1.1研究背景 1
1.2研究现状 1
1.3研究方法与创新点 2
2生物信号噪声特性分析 2
2.1常见生物信号的噪声来源 2
2.2典型生物信号的噪声特征 3
2.3噪声对信号质量的影响评估 3
3传统噪声抑制方法比较研究 4
3.1基于滤波的噪声抑制方法 4
3.2基于小波变换的去噪技术 5
3.3自适应滤波算法性能分析 5
4新型智能去噪方法研究 6
4.1深度学习在生物信号去噪中的应用 6
4.2混合去噪算法的设计与实现 6
4.3不同去噪方法的性能对比分析 7
5结论 8
参考文献 9
致谢 10
生物信号处理在医疗诊断和健康监测中具有重要应用价值，然而信号采集过程中不可避免会受到噪声干扰，严重影响信号质量和后续分析。本研究旨在系统比较现有噪声抑制方法的性能，并提出一种基于自适应滤波与深度学习的混合降噪框架。研究首先对传统方法（如小波变换、卡尔曼滤波）和新兴深度学习方法（如卷积神经网络、循环神经网络）进行理论分析和实验验证，通过信噪比、均方误差等指标评估其在不同噪声环境下的表现。在此基础上，提出了一种结合自适应滤波预处理和深度残差网络的混合模型，该模型能够有效处理非平稳噪声并保留信号细节特征。实验结果表明，所提方法在心电、脑电等生物信号数据集上的降噪效果显著优于单一方法，平均信噪比提升约3.5dB，且具有较强的泛化能力。
关键词：生物信号降噪 自适应滤波 深度学习
Abstract
Biological signal processing has important application value in medical diagnosis and health monitoring, but it will inevitably be disturbed by noise in the signal process of signal acquisition, which seriously affects the signal quality and subsequent analysis. This study aims to systematically compare the performance of existing noise suppression methods and propose a hybrid noise reduction fr amework based on adaptive filtering and deep learning. Firstly, the theoretical analysis and experimental verification of traditional methods (such as wavelet transform, Kalman filter) and emerging deep learning methods (such as convolutional neural network and recurrent neural network), and evaluate their performance in different noise environments through signal-to-noise ratio, mean square error and other indicators. Furthermore, we propose a hybrid model combining adaptive filtering preprocessing and deep residual network, which can effectively deal with non-stationary noise and preserving signal detail features. The experimental results show that the proposed method is significantly better than the single method in the biological signal data sets, and the average SNR improves by about 3.5dB, and has strong generalization ability.
Keyword: Biosignal noise reduction adaptive-filtering deep learning
目 录
1绪论 1
1.1研究背景 1
1.2研究现状 1
1.3研究方法与创新点 2
2生物信号噪声特性分析 2
2.1常见生物信号的噪声来源 2
2.2典型生物信号的噪声特征 3
2.3噪声对信号质量的影响评估 3
3传统噪声抑制方法比较研究 4
3.1基于滤波的噪声抑制方法 4
3.2基于小波变换的去噪技术 5
3.3自适应滤波算法性能分析 5
4新型智能去噪方法研究 6
4.1深度学习在生物信号去噪中的应用 6
4.2混合去噪算法的设计与实现 6
4.3不同去噪方法的性能对比分析 7
5结论 8
参考文献 9
致谢 10
