Journal ArticleDOI
ECG data compression using truncated singular value decomposition
Jyh-Jong Wei,Chuang-Jan Chang,Nai-Kuan Chou,Gwo-Jen Jan +3 more
- Vol. 5, Iss: 4, pp 290-299
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TLDR
The results showed that truncated SVD method can provide an efficient coding with high-compression ratios and demonstrated the method as an effective technique for ECG data storage or signals transmission.Abstract:
The method of truncated singular value decomposition (SVD) is proposed for electrocardiogram (ECG) data compression. The signal decomposition capability of SVD is exploited to extract the significant feature components of the ECG by decomposing the ECG into a set of basic patterns with associated scaling factors. The signal information is mostly concentrated within a certain number of singular values with related singular vectors due to the strong interbeat correlation among ECG cycles. Therefore, only the relevant parts of the singular triplets need to be retained as the compressed data for retrieving the original signals. The insignificant overhead can be truncated to eliminate the redundancy of ECG data compression. The Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database was applied to evaluate the compression performance and recoverability in the retrieved ECG signals. The approximate achievement was presented with an average data rate of 143.2 b/s with a relatively low reconstructed error. These results showed that the truncated SVD method can provide efficient coding with high-compression ratios. The computational efficiency of the SVD method in comparing with other techniques demonstrated the method as an effective technique for ECG data storage or signals transmission.read more
Citations
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TL;DR: This chapter discusses the power of different tensor decompositions with a focus on typical ECG problems that can be solved using tensors.
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References
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TL;DR: The theoretical bases behind the direct ECG data compression schemes are presented and classified into three categories: tolerance-comparison compression, DPCM, and entropy coding methods and a framework for evaluation and comparison of ECG compression schemes is presented.
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