摘要
为校正ECG信号的基线漂移,提出小波变换和自适应滤波相结合的方法。利用小波变换对原始ECG信号进行分解,将得到的高频分量作为参考信号输入,采用基于幂函数的最小均方算法(P-LMS)进行自适应滤噪处理。通过与传统的归一化最小均方算法(NLMS)进行对比,验证该算法的准确性。仿真实验和MIT-BIH数据库中的实际数据检验结果表明,基于幂函数的最小均方算法和小波变换相结合的方法能够有效校正基线漂移,并较好地保持心电信号的几何特征。
In order to calibrate the baseline shift of ECG signal, the combination methods of wavelet transform and adaptive filtering are proposed. The wavelet transform method is used to decompose the original ECG signal mad the high-frequency components are used as reference input data. A new adaptive filtering algorithm, P-LMS based on the power function is proposed to conduct adaptive noise filtering. Compared with the traditional Normalized Least Mean Square（NLMS） algorithm, the proposed algorithm is precise. Using the simulated experiment and actual data in the MIT-BIH database, the method of combining P-LMS and wavelet transform is verified that can effectively correct the baseline shift and maintain the geometric characteristics of the ECG signal.
出处
《计算机工程》
CAS
CSCD
2013年第11期226-229,244,共5页
Computer Engineering
基金
国家自然科学基金资助项目(61100097)
黑龙江省教育厅科学技术研究基金资助项目(12513077)
黑龙江省教育厅面上基金资助项目(12521475)
关键词
基线漂移
自适应滤波
小波变换
基于幂函数的最小均方算法
归一化最小均方算法
baseline shift
adaptive filtering
wavelet transform
Power function of Least Mean Square（P-LMS） algorithm
NormalizedLeast Mean Square（NLMS） algorithm
