A Noise Tolerant Method for ECG Signals Feature Eztraction and Noise Reduction

摘要：

A noise tolerant template model technique for ECG feature extraction based on an individual-specific training approach is presented in this paper. Baseline wander, electrode motion artifacts, and electromyographic interference were added with varying signal-to-noise ratios (SNRs) to a dataset of approximately 3000 beats of different ECG recordings from the QT database to validate the performance of our technique. All of the QRS-complex, P-and T-waves detectors achieved an average sensitivity of 96.11％, positive predictivity of 83.8％ and accuracy detection rate of 81.9％ for SNRs between 24dB and -6dB, outperforming four recent beat detection algorithms evaluated with respect to the same types of noise. Furthermore, the ability of our technique to achieve efficient noise reduction including in-band noise, while preserving the morphological and clinical information of the original signal, is described.

作者: Emna Zoghlami Ayari Reinhard Tielert Norbert Wehn

作者单位: Microelectronic Systems Design Research Group University of Kaiserslautern,Germany

会议类型: 国际会议

会议名称: The 3rd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

会议地点: 北京

会议语种:英文

页码: 1-4

在线出版日期: 2009-06-11（万方平台首次上网日期，不代表论文的发表时间）