摘要
由于心电图(ECG)信号的特点以及在采集过程中所受到的干扰影响,ECG信号去噪已成为ECG信号智能分析的基础。本文在基于小波变换方法的基础上,对阈值参数进行改进,提出了与噪声更加匹配的阈值表达式。利用改进的阈值对离散分解后的小波系数进行处理,通过小波逆变换重构信号,能够更加准确地去除噪声的小波系数,保留更多原信号小波系数。采用MIT-BIH中的数据进行实验,结果表明,改进方法较之现有小波阈值去噪方法,能够达到更好的去噪效果。
Due to the characteristics and environmental factors, electrocardiogram （ECG） signals are usually inter- fered by noises in the course of signal acquisition, so it is crucial for ECG intelligent analysis to eliminate noises in ECG signals. On the basis of wavelet transform, threshold parameters were improved and a more appropriate thresh- old expression was proposed. The discrete wavelet coefficients were processed using the improved threshold parame- ters, the accurate wavelet coefficients without noises were gained through inverse discrete wavelet transform, and then more original signal coefficients could be preserved. MIT-BIH arrythmia database was used to validate the method. Simulation results showed that the improved method could achieve better denoising effect than the tradition- al ones.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2014年第3期511-515,共5页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(61203160
61074175)
河北省自然科学基金资助项目(F2011201159)
关键词
心电图信号
去噪
小波变换
边带
阈值
electrocardiogram signal
denoising
wavelet transform
subhand
threshold
