摘 要
肌电图信号作为反映肌肉生理活动的重要指标，已被广泛应用于肌肉疾病诊断和康复医学领域。然而，传统方法在信号分类与疾病识别中存在特征提取复杂、分类精度不足等问题。本研究旨在通过引入先进的信号处理与机器学习技术，提升肌电图信号分类的准确性和肌肉疾病识别的可靠性。研究首先对采集的肌电图信号进行预处理，包括滤波降噪和分段处理，以提高数据质量；随后采用时域、频域及非线性特征相结合的方法提取信号的多维度特征，并利用主成分分析实现特征降维，从而减少冗余信息并优化计算效率。在此基础上，构建基于深度学习的卷积神经网络模型，结合迁移学习策略以增强模型对小样本数据集的适应能力。实验结果表明，所提方法在多种肌肉疾病分类任务中表现出优异性能，平均分类准确率较传统方法提升约15%。此外，该方法还具备较强的鲁棒性和泛化能力，能够有效应对不同个体间的信号差异。本研究的主要创新点在于将深度学习与传统特征提取方法有机结合，同时针对肌电图信号特性优化模型结构，为肌肉疾病的精准诊断提供了新思路和技术支持。研究成果不仅有助于深化对肌肉生理机制的理解，也为临床应用提供了可靠的工具和方法学参考。
关键词：肌电图信号；深度学习；特征提取；肌肉疾病分类；迁移学习
Classification of Electromyographic Signals and Identification of Muscle Diseases
Abstract: Electromyography (EMG) signals, as a critical indicator reflecting muscle physiological activities, have been widely applied in the diagnosis of muscle diseases and rehabilitation medicine. However, traditional methods for signal classification and disease identification suffer from issues such as complex feature extraction and insufficient classification accuracy. This study aims to enhance the accuracy of EMG signal classification and the reliability of muscle disease recognition by incorporating advanced signal processing and machine learning techniques. Initially, the collected EMG signals were preprocessed through filtering, noise reduction, and segmentation to improve data quality. Subsequently, a multi-dimensional feature extraction approach combining time-domain, frequency-domain, and nonlinear features was employed, followed by principal component analysis for feature dimensionality reduction to minimize redundant information and optimize computational efficiency. On this basis, a deep-learning-based convolutional neural network model was constructed, integrating transfer learning strategies to strengthen the model's adaptability to small-sample datasets. Experimental results demonstrate that the proposed method exhibits superior performance in various muscle disease classification tasks, with an average classification accuracy improvement of approximately 15% compared to traditional methods. Moreover, the method possesses strong robustness and generalization capabilities, effectively addressing inter-individual signal variations. The primary innovation of this study lies in the organic combination of deep learning with traditional feature extraction methods, along with the optimization of model architecture tailored to the characteristics of EMG signals, thereby providing new insights and technical support for the precise diagnosis of muscle diseases. The research not only contributes to a deeper understanding of muscle physiological mechanisms but also offers reliable tools and methodological references for clinical applications.
Keywords: Electromyography Signal; Deep Learning; Feature Extraction; Muscle Disease Classification; Transfer Learning
目 录
1绪论 1
1.1肌电图信号分类的研究背景 1
1.2肌肉疾病识别的意义与价值 1
1.3国内外研究现状分析 1
1.4本文研究方法概述 2
2肌电图信号特征提取与分析 2
2.1肌电图信号的基本特性 2
2.2特征提取技术综述 3
2.3时间域特征分析方法 3
2.4频率域特征分析方法 4
2.5时频域特征的综合应用 4
3肌电图信号分类算法研究 5
3.1分类算法在肌电图中的应用 5
3.2基于机器学习的分类方法 5
3.3深度学习在信号分类中的优势 6
3.4分类模型性能评估指标 6
3.5算法优化与改进策略 7
4肌肉疾病识别的应用实践 7
4.1肌肉疾病的典型特征表现 7
4.2数据采集与预处理方法 8
4.3疾病识别模型构建与验证 8
4.4实验结果分析与讨论 9
4.5应用场景与未来展望 9
结论 9
参考文献 11
致 谢 12
