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Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition

Published: 09 August 2019 Publication History

Abstract

Human physiological data are naturalistic and objective user data inputs for a great number of cyber-physical systems (CPS). Electrocardiogram (ECG) as a widely used physiological golden indicator for certain human state and disease diagnosis is often used as user data input for various CPS such as medical CPS and human–machine interaction. Wireless transmission and wearable technology enable long-term continuous ECG data acquisition for human–CPS interaction; however, these emerging technologies bring challenges of storing and wireless transmitting huge amounts of ECG data, leading to energy efficiency issue of wearable sensors. ECG signal compression technique provides a promising solution for these challenges by decreasing ECG data size. In this study, we develop the first scheme of leveraging empirical mode decomposition (EMD) on ECG signals for sparse feature modeling and compression and further propose a new ECG signal compression framework based on EMD constructed feature dictionary. The proposed method features in compressing ECG signals using a very limited number of feature bases with low computation cost, which significantly improves the compression performance and energy efficiency. Our method is validated with the ECG data from MIT-BIH arrhythmia database and compared with existing methods. The results show that our method achieves the compression ratio (CR) of up to 164 with the root mean square error (RMSE) of 3.48% and the average CR of 88.08 with the RMSE of 5.66%, which is more than twice of the average CR of the state-of-the-art methods with similar recovering error rate of around 5%. For diagnostic distortion perspective, our method achieves high QRS detection performance with the sensitivity (SE) of 99.8% and the specificity (SP) of 99.6%, which shows that our ECG compression method can preserve almost all the QRS features and have no impact on the diagnosis process. In addition, the energy consumption of our method is only 30% of that of other methods when compared under the same recovering error rate.

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Published In

cover image ACM Transactions on Cyber-Physical Systems
ACM Transactions on Cyber-Physical Systems  Volume 3, Issue 4
Special Issue on Human-Interaction-Aware Data Analytics for CPS
October 2019
171 pages
ISSN:2378-962X
EISSN:2378-9638
DOI:10.1145/3356399
  • Editor:
  • Tei-Wei Kuo
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 August 2019
Accepted: 01 May 2019
Revised: 01 February 2019
Received: 01 August 2018
Published in TCPS Volume 3, Issue 4

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Author Tags

  1. Signal compression
  2. empirical mode decomposition
  3. energy efficiency

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  • (2024)Tracking user trust and mental states during cyber-attacks: A survey of existing methods and future research directions on AI-enabled decision-making for the Royal Canadian Navy2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)10.1109/ICHMS59971.2024.10555658(1-4)Online publication date: 15-May-2024
  • (2022)Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine TransformComputers, Materials & Continua10.32604/cmc.2022.02404471:3(4391-4408)Online publication date: 2022
  • (2022)FreeSia: A Cyber-physical System for Cognitive Assessment through Frequency-domain Indoor Locomotion AnalysisACM Transactions on Cyber-Physical Systems10.1145/34704546:2(1-31)Online publication date: 11-Apr-2022
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  • (2020)ECG Monitoring Systems: Review, Architecture, Processes, and Key ChallengesSensors10.3390/s2006179620:6(1796)Online publication date: 24-Mar-2020

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