Energy-Efficient ECG Signal Compression for User Data Input in Cyber-Physical Systems by Leveraging Empirical Mode Decomposition
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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Real-time Bluetooth transmission typically consumes 22.7 mW.
“real-time Bluetooth transmission typically consumes 22.7 mW67”
From Flexible self-rectifying synapse array for energy-efficient edge multiplication in electrocardiogram diagnosis (2025) · cites this paper as [67]
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- Authors
- Huang, H., Hu, S., & Sun, Y.
- Affiliations
- Michigan Technological University (US)
- ORCID
- Venue
- ACM Transactions on Cyber-Physical Systems
- Published
- 2019-08-09
- Volume
- 3
- Issue
- 4
- Pages
- 1-19
- ISSN
- 2378-962X (ISSN-L)
- 2378-9638
- OpenAlex
- W2967827507
- Cited by
- 10
- References
- 41
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 1 · 2022: 2 · 2021: 1 · 2020: 2
- FWCI
- 0.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM Transactions on Cyber-Physical Systems (publishedVersion)
- Digital Commons - Michigan Tech (Michigan Technological University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Keywords
- ECG signal compression, empirical mode decomposition, energy efficiency, wearable sensors, cyber-physical systems, human-machine interaction, feature dictionary, QRS complex detection, physiological signals, wireless transmission, MIT-BIH arrhythmia database
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