Real-Time Baseline Wander Removal in ECG Signal Based on Weighted Local Linear Regression Smoothing

被引：0

作者：

Tan, Xiao ^{[1
]}

Chen, Xianxiang ^{[1
]}

Ren, Ren ^{[1
]}

Hu, Xinyu ^{[1
]}

Zhou, Bing ^{[1
]}

Fang, Zhen ^{[1
]}

Xia, Shanhong ^{[1
]}

机构：

[1] Chinese Acad Sci, Inst Elect, State Key Lab Transducer Technol, Beijing 100190, Peoples R China

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) | 2013年

关键词：

electrocardiogram (ECG); baseline wander (BW); weighted local regression;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Removing the baseline wander (BW) is vital in electrocardiogram (ECG) preprocessing steps, since it can severely influence the diagnostic results, especially in computer based diagnoses. This paper presents a method based on weighted local regression smoothing to correct BW in real time. Each signal data sample within a certain window is weighted. The weight of each sample is determined by the distance between the sample and the to-be-predicted sample. Then the regression is adopted by performing linear least-squares and a polynomial model to estimate BW. The ECG signal free from BW is obtained by subtracting the BW from the original ECG signal. The experiment results demonstrate that this method can effectively remove BW in ECG signal in real time and with minimum distortion of ECG waveform.

引用

页码：453 / 456

页数：4

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