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Multiresolutional distributed filtering: A novel technique the reduces the amount of data required in high resolution electrocardiography

机译:多分辨率分布式过滤:一种新颖的技术,可减少高分辨率心电图术所需的数据量

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摘要

High resolution ECG analysis is widely accepted as the best non-invasive technique for the assessment of ventricular tachycardia risk in post-myocardial infarction patients. However, the standard analysis approaches involve an extensive averaging procedure which requires long data records, accompanied by the consequent efforts for storage and transmission. This paper outlines an algorithm for multiresolutional distributed filtering, that can significantly reduce the necessary amount of data. The proposed filtering method comprises three basic steps: the dyadic wavelet transform computation, the shrinkage of the wavelet coefficients using adaptive Bayesian rules, and the reconstruction of the denoised signal through the inverse wavelet transform. The performance evaluation using controlled simulation experiments revealed that the present technique could accelerate the noise reduction preserving the diagnoistic value of the signals.
机译:高分辨率心电图分析已被广泛认为是评估心肌梗死后患者室性心动过速风险的最佳非侵入性技术。但是,标准分析方法涉及广泛的平均过程,该过程需要长数据记录,并伴随着随之而来的存储和传输工作。本文概述了一种用于多分辨率分布式过滤的算法,该算法可以显着减少必要的数据量。所提出的滤波方法包括三个基本步骤:二进小波变换计算,使用自适应贝叶斯规则缩小小波系数以及通过小波逆变换来重建去噪信号。使用受控模拟实验的性能评估表明,本技术可以加快降噪速度,并保留信号的诊断价值。

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