基于小波变换和卡尔曼滤波的心电信号滤波算法研究OA

Objective To propose a Wavelet-Kalman filter cooperative algorithm to solve the problem of the wavelet transform in low-frequency noise reduction when used individually for processing ECG signals.Methods Firstly,ECG signals were decomposed into different frequency bands using the multiresolution properties of the wavelet transform;secondly,noise removal was carried out with Kalman filtering in the low-frequency band and with wavelet thresholding in the high-frequency band,and then denoised signals were obtained through wavelet reconstruction;finally,denoising simulation experiments were conducted with No.105,106,107,108 and 109 ECG signals from MIT-BIH Arrhythmia Database at three noise levels of 25,20 and 15 dB,and the denoising effects of wavelet transform algorithm and Wavelet-Kalman filter cooperative algorithm were analyzed qualitatively and quantitatively.Results Qualitative analysis showed ECG signals denoised using the cooperative algorithm exhibited superior waveform smoothness,and the cooperative algorithm gained advantages over the wavelet transform algorithm in suppression of high-frequency noise and baseline drift.Quantitative analysis indicated that the cooperative algorithm outperformed the wavelet transform algorithm in all the test cases in terms of signal-to-noise ratio improvement,and in most cases in terms of lowered root mean square error.Conclusion The proposed Wavelet-Kalman filter cooperative algorithm eliminates the limitations of the wavelet transform algorithm,effectively removes various types of noises while preserving key features of ECG signals,thereby providing an effective solution for ECG signal filtering.[Chinese Medical Equipment Journal,2026,47(3):18-25]