A Dynamic Approach for Compressed Sensing of Multi-lead ECG Signals

被引：0

作者：

Iadarola, Grazia ^{[1
]}

Daponte, Pasquale ^{[1
]}

Picariello, Francesco ^{[1
]}

De Vito, Luca ^{[1
]}

机构：

[1] Univ Sannio, Dept Engn, Benevento, Italy

来源：

2020 IEEE INTERNATIONAL SYMPOSIUM ON MEDICAL MEASUREMENTS AND APPLICATIONS (MEMEA) | 2020年

关键词：

Electrocardiogram; biomedical measurement system; Internet-of-Medical-Things (IoMT); multiple measurement vector reconstruction; Compressed Sensing; sub-sampling; RECOVERY; ELECTROCARDIOGRAM;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

This paper proposes a dynamic method based on Compressed Sensing (CS) to reconstruct multi-lead electrocardiography (ECG) signals in support of Internet-of-Medical-Things. Specifically, the sensing matrix is dynamically evaluated through the signal samples acquired by the first lead. The experimental evaluation demonstrates that, compared to the traditional CS multi-lead method adopting a random sensing matrix, the proposed dynamic method exhibits a lower difference from the original ECG signal.

引用

页数：6

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