A new approach to compressing ECG signals with trained overcomplete dictionary
A new approach to compressing ECG signals with trained overcomplete dictionary
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一种使用经过训练的过完备字典压缩心电信号的新方法
DOI：
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发表时间：
2014
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通讯作者：
P. Chou
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文献类型：
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作者：
Seung Jae Lee;Jun Luan;P. Chou
本文提出了一种基于学习过完备字典的心电数据压缩算法，以利用相邻心跳信号之间的相关性。通过K-SVD字典学习算法，对长度和大小进行预处理和归一化，构建学习后的过完备字典。该算法利用过完备字典找到稀疏估计，能有效地表示心电信号。在MIT-BIH心律失常数据库上的实验结果表明，该算法具有较高的压缩比，且数据失真最小。
We propose a new ECG data compression algorithm based on a learned overcomplete dictionary to exploit the correlation between signals in adjacent heart beats. The learned overcomplete dictionary is constructed by K-SVD dictionary learning algorithm, after preprocessing and normalization of length and magnitude. Using the overcomplete dictionary, the proposed algorithm can find sparse estimation, which can represent the ECG signal effectively. Experimental results on MIT-BIH arrhythmia database confirms that our proposed algorithm has high compression ratio while minimizing data distortion.