Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes
TLDR
This paper quantifies the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote and shows that CS represents a competitive alternative to state- of- the-art digital wavelet transform (DWT)-basedECG compression solutions in the context of WBSn-based ECG monitoring systems.Abstract:
Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for “good” reconstruction quality.read more
Citations
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Journal ArticleDOI
Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things
TL;DR: In this paper, a compressed sensing-based data sampling and data acquisition in wireless sensor networks and the Internet of Things (IoT) has been investigated, in which the end nodes measure, transmit, and store the sampled data in the framework.
Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things (Extended) IEEE Industrial Electronics Technology News
TL;DR: This paper briefly introduces the CS theory with respect to the sampling and transmission coordination during the network lifetime through providing a compressed sampling process with low computation costs, and proposes a CS-based framework for IoT and an efficient cluster-sparse reconstruction algorithm for in-network compression.
Journal ArticleDOI
A survey on ECG analysis
Selcan Kaplan Berkaya,Alper Kursat Uysal,Efnan Sora Gunal,Semih Ergin,Serkan Gunal,M. Bilginer Gülmezoğlu +5 more
TL;DR: The literature on ECG analysis, mostly from the last decade, is comprehensively reviewed based on all of the major aspects mentioned above.
Journal ArticleDOI
Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG Via Block Sparse Bayesian Learning
TL;DR: Experimental results show that the block sparse Bayesian learning framework, compared to other algorithms such as current CS algorithms and wavelet algorithms, can greatly reduce code execution in CPU in the data compression stage.
Proceedings ArticleDOI
Smart e-Health Gateway: Bringing intelligence to Internet-of-Things based ubiquitous healthcare systems
Amir-Mohammad Rahmani,Nanda Kumar Thanigaivelan,Tuan Nguyen Gia,Jose Granados,Behailu Negash,Pasi Liljeberg,Hannu Tenhunen +6 more
TL;DR: This paper exploits the strategic position of such gateways to offer several higher-level services such as local storage, real-time local data processing, embedded data mining, etc., proposing thus a Smart e-Health Gateway.
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