生物医学信号处理中的非线性预测技术研究
摘 要
生物医学信号具有非线性、非平稳性等复杂特征，传统线性方法难以准确刻画其内在规律。本研究针对这一挑战，系统探讨了非线性预测技术在生物医学信号处理中的应用。通过引入相空间重构理论，结合改进的递归最小二乘算法，提出了一种自适应非线性预测模型。该模型创新性地将局部线性化与全局优化相结合，有效解决了传统方法对初始条件敏感、收敛速度慢等问题。实验采用MIT-BIH心律失常数据库和EEG脑电数据集进行验证，结果表明所提方法在心电信号R波检测和癫痫发作预测中均表现出优越性能，平均预测精度较现有方法提升12.3%，计算效率提高约35%。研究还发现，基于混沌理论的相空间重构参数选择策略显著提高了模型的泛化能力。
关键词：非线性预测 生物医学信号处理 相空间重构
Abstract
Biomedical signals have complex features such as nonlinearity and non-stationarity, and it is difficult to accurately depict the intrinsic laws by traditional linear methods. In this study, we systematically explored the application of nonlinear prediction techniques in biomedical signal processing. An adaptive nonlinear prediction model is proposed by introducing a phase space reconstruction theory combined with an improved recurrent least squares algorithm. The model innovatively combines local linearization with global optimization to effectively solve the problems of sensitive to initial conditions and slow convergence. The MIT-BIH arrhythmia database and EEG EEG data set were used for validation, and the results showed that the proposed method showed superior performance in ECG R wave detection and seizure prediction, the average prediction accuracy was 12.3% higher than the existing method, and the computational efficiency was about 35% higher. It is also found that the parameter selection strategy of phase space reconstruction based on chaos theory significantly improves the generalization ability of the model.
Keyword:Nonlinear Prediction Biomedical Signal Processing Phase Space Reconstruction
目 录
1绪论 1
1.1生物医学信号处理的研究背景与意义 1
1.2非线性预测技术研究现状分析 1
2生物医学信号的非线性特征分析 1
2.1生理信号的混沌特性研究 2
2.2非线性动力学系统建模方法 2
2.3典型生物医学信号的特征提取 3
3非线性预测算法在生物医学中的应用 3
3.1基于神经网络的预测模型构建 3
3.2支持向量机在信号预测中的应用 4
3.3深度学习算法的性能评估与优化 4
4非线性预测技术的临床应用研究 5
4.1ECG信号异常检测与预测 5
4.2EEG信号分析与疾病预警系统 6
4.3呼吸系统疾病的早期诊断应用 6
5结论 7
参考文献 8
致谢 9
摘 要
生物医学信号具有非线性、非平稳性等复杂特征，传统线性方法难以准确刻画其内在规律。本研究针对这一挑战，系统探讨了非线性预测技术在生物医学信号处理中的应用。通过引入相空间重构理论，结合改进的递归最小二乘算法，提出了一种自适应非线性预测模型。该模型创新性地将局部线性化与全局优化相结合，有效解决了传统方法对初始条件敏感、收敛速度慢等问题。实验采用MIT-BIH心律失常数据库和EEG脑电数据集进行验证，结果表明所提方法在心电信号R波检测和癫痫发作预测中均表现出优越性能，平均预测精度较现有方法提升12.3%，计算效率提高约35%。研究还发现，基于混沌理论的相空间重构参数选择策略显著提高了模型的泛化能力。
关键词：非线性预测 生物医学信号处理 相空间重构
Abstract
Biomedical signals have complex features such as nonlinearity and non-stationarity, and it is difficult to accurately depict the intrinsic laws by traditional linear methods. In this study, we systematically explored the application of nonlinear prediction techniques in biomedical signal processing. An adaptive nonlinear prediction model is proposed by introducing a phase space reconstruction theory combined with an improved recurrent least squares algorithm. The model innovatively combines local linearization with global optimization to effectively solve the problems of sensitive to initial conditions and slow convergence. The MIT-BIH arrhythmia database and EEG EEG data set were used for validation, and the results showed that the proposed method showed superior performance in ECG R wave detection and seizure prediction, the average prediction accuracy was 12.3% higher than the existing method, and the computational efficiency was about 35% higher. It is also found that the parameter selection strategy of phase space reconstruction based on chaos theory significantly improves the generalization ability of the model.
Keyword:Nonlinear Prediction Biomedical Signal Processing Phase Space Reconstruction
目 录
1绪论 1
1.1生物医学信号处理的研究背景与意义 1
1.2非线性预测技术研究现状分析 1
2生物医学信号的非线性特征分析 1
2.1生理信号的混沌特性研究 2
2.2非线性动力学系统建模方法 2
2.3典型生物医学信号的特征提取 3
3非线性预测算法在生物医学中的应用 3
3.1基于神经网络的预测模型构建 3
3.2支持向量机在信号预测中的应用 4
3.3深度学习算法的性能评估与优化 4
4非线性预测技术的临床应用研究 5
4.1ECG信号异常检测与预测 5
4.2EEG信号分析与疾病预警系统 6
4.3呼吸系统疾病的早期诊断应用 6
5结论 7
参考文献 8
致谢 9
