Multichannel ECG Compression using Block-Sparsity-based Joint Compressive Sensing
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Multichannel ECG Compression using Block-Sparsity-based Joint Compressive Sensing Sushant Kumar1 · Bhabesh Deka1
· Sumit Datta1
Received: 18 December 2018 / Revised: 10 June 2020 / Accepted: 12 June 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Wireless body area networks (WBANs) are increasingly used for remote healthcare surveillance in recent times, where electrocardiogram (ECG) signals are continuously acquired and transmitted to a base station or remote hospital for their storage and subsequent analysis. Multichannel ECG (MECG) is preferred over single-channel ECG as it provides more information from diagnostic point of view. One of the biggest challenges is to minimize the energy required for the WBAN network for continuous transmission of MECG data, which in turn demands for efficient data compression. Compressive sensing is an efficient signal processing tool for simultaneous compression and reconstruction of MECG data without visibly no or minimum loss of diagnostic information. In this paper, we propose an energy-efficient novel block-sparsity-based MECG compression scheme, which exploits both spatiotemporal correlation and multi-scale information of MECG data in the wavelet domain, effectively. Experimental results show that the proposed method outperforms other recently developed methods for MECG compression both qualitatively and quantitatively. Keywords Multichannel ECG · Wireless body area network · Compressed sensing · Joint sparsity · Energy-efficient compression
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Bhabesh Deka [email protected] Sushant Kumar [email protected] Sumit Datta [email protected]
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Department of Electronics and Communication Engineering, Tezpur University, Tezpur 784028, India
Circuits, Systems, and Signal Processing
1 Introduction A wireless body area network (WBAN) is a self-organized network for radio frequency communication that interconnects a number of wireless sensor nodes positioned on or around a human body in concern [3]. It supports ambulatory ECG monitoring of a user even during his regular physical activities. However, it has limiting factors in terms of power, bulkiness, storage, autonomy, etc. Development of an energy-efficient WBAN system is becoming a major research problem as such systems run on batteries and hardly sustain for long duration. Compressive sensing (CS) is a simultaneous sensing and reconstruction technique for signals that are sparse or compressible in a transform domain, usually incoherent to the signal domain [1]. CS offers data reduction by simple matrix-vector multiplication and makes signal encoding quite simple and energy efficient. In the literature, different CS-based ECG data compression techniques exist that are designed for less energy consumption and computational burden suitable for the application of WBAN in remote health monitoring [14,17,26,27]. Multichannel ECG (MECG) signals are preferred over single-channel ECG (SECG) by cardiologists due to their detailed clinical/pathological information [23]. In Fig. 1, it can
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