自适应离散心电表示——比较变深度抽取和连续非均匀采样

P. Augustyniak
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引用次数: 12

摘要

本文比较了变深度抽取(VDD)和连续非均匀采样(CNU)两种非均匀心电采样方法。VDD算法使用基于小波的时间尺度分解分割的心电信号,其中高频尺度表示消除了较窄带宽的信号部分(如T-P段)。结果,信号被局部抽取到依赖于预期带宽的水平。CNU算法在期望信号局部带宽的基础上对每个后续采样间隔的长度进行软估计。对于CSE多重导联数据库的心电记录,VDD算法的平均效率(4.26)明显高于CNU方法的平均效率(3.01)。不幸的是,VDD的全局重建误差(PRD)(0.40%)也高于CNU算法(0.22%)。
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Adaptive discrete ECG representation - comparing variable depth decimation and continuous non-uniform sampling
This paper compares two methods of non-uniform ECG sampling: the variable depth decimation (VDD) and the continuous non-uniform sampling (CNU). The VDD algorithm uses the wavelet-based time-scale decomposition of the segmented ECG in which the high frequency scales representation is eliminated for the signal sections of narrower bandwidth (e.g. T-P segment). In result, the signal is locally decimated down to the level depending on the expected bandwidth. The CNU algorithm uses a soft estimation of the length for each subsequent sampling interval on a basis of expected local bandwidth of the signal. For ECG records from the CSE Multilead Database the average efficiency of the VDD algorithm is significantly higher (4.26) than the efficiency computed for the CNU method (3.01). Unfortunately, the global reconstruction error (PRD) is also higher for the VDD (0.40%) than for the CNU algorithm (0.22%).
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