Results on ECG compressed sensing using specific dictionaries and its validation

M. Fira, L. Goras, Nicolae Cleju, C. Barabasa
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引用次数: 9

Abstract

The paper presents a new method and results regarding the compressed sensing (CS) and classification of ECG waveforms using a general dictionary as well as specific dictionaries built using normal and pathological cardiac patterns. The proposed method has been validated by computation of the distortion errors between the original and the reconstructed signals and by the classification ratio of the reconstructed signals obtained with the k-nearest neighbors (KNN) algorithm.
基于特定字典的心电压缩感知结果及其验证
本文提出了一种新的心电波形压缩感知(CS)和分类方法,该方法使用一般字典以及使用正常和病理心脏模式构建的特定字典。通过计算原始信号与重构信号之间的失真误差以及利用k近邻(KNN)算法得到的重构信号的分类率,验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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