Secure stochastic ECG signals based on gaussian mixture model for e-healthcare systems
Abstract
The blood circulation system in a human body provides a unique and natural trust zone for secure data communications in wireless healthcare systems such as body area networks. Unfortunately, biometric signal authentication using physiological attributes in wireless healthcare has not been extensively studied. In this paper, we propose a data authentication approach utilizing electrocardiography (ECG) signal patterns for reducing key exchange overhead. The major contribution of this research is to apply stochastic pattern recognition techniques in wireless healthcare. In the proposed approach, the inter-pulse interval (IPI) signal pattern at transmitter side is summarized as a biometric authentication key using Gaussian mixture model (GMM). At the receiver side, a light-weight signature verification scheme is adopted that uses IPI signals gathered locally at the receiver. The proposed authentication scheme has the advantage of high sample misalignment tolerance. In our earlier work, we had demonstrated the concept of stochastic authentication for ECG signal, but the signature verification process and GMM authentication performance under time synchronization and various sample points were not discussed. Here, we present a new set of analytical and experimental results to demonstrate that the proposed stochastic authentication approach achieves a low half total error rate in ECG signals verification.
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“This metadata provides contextual analysis and improves the process of knowledge extraction [29,57].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [57]
Gaussian mixture model is a statistical model used for classification and pattern recognition.
“Gaussian mixture model (GMM) is another statistical model which used for classification and pattern recognition [57].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [57]
Data simulation is useful when the focus of the data processing method is on efficiency and robustness of information extraction rather than handling real-world data including artifacts, errors, and conditions of the data gathering environment.
“Data simulation would be useful when the more focus of data processing method is on the efficiency and robustness of information extraction [28,57] rather than handling real-world data including the artifact, errors, conditions of data gathering environment, etc.”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [57]
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- Authors
- Wang, W., Wang, H., Hempel, M., Peng, D., Sharif, H., & Chen, H.-H.
- Affiliations
- South Dakota State University (US)
- University of Massachusetts Dartmouth (US)
- University of Nebraska–Lincoln (US)
- National Cheng Kung University (TW)
- Venue
- IEEE Systems Journal
- Published
- 2011-12
- Volume
- 5
- Issue
- 4
- Pages
- 564-573
- ISSN
- 1932-8184 (ISSN-L)
- OpenAlex
- W2105449668
- Cited by
- 45
- References
- 32
- Citations by year
- 2023: 1 · 2022: 2 · 2021: 3 · 2020: 4 · 2019: 7 · 2018: 3 · 2017: 5 · 2016: 6 · 2015: 5 · 2014: 7 · 2013: 1 · 2012: 1
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Systems Journal (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Wireless Communication Security Techniques
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Authentication Protocols Security
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- Gaussian mixture model, biometric authentication, interpulse interval, signature verification, body area networks, wireless healthcare, e-healthcare security, clock synchronization
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