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Bandwidth-Efficient Transmission of HRV Features Using PhysioNet ECG Data for IoT-Based Wearable Health Monitoring

Author

Listed:
  • Naoya Morikawa

    (Department of Information Engineering, Graduate School of Engineering, Mie University, Tsu 514-8507, Japan)

  • Emi Yuda

    (Innovation Center for Semiconductor and Digital Future, Mie University, Tsu 514-8507, Japan
    Department of Management Science and Technology, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan)

Abstract

In IoT health monitoring using electrocardiograms (ECGs), the surge in data transmission volume poses a significant challenge. This study utilized PhysioNet ECG data to compare the transmission volumes of raw ECG signals, R-R intervals (RRIs), and HRV metrics (SDNN, RMSSD, and LF/HF), thereby evaluating the effectiveness of communication optimization. The results demonstrated that transmitting RRI data and HRV metrics reduced data volume by approximately 99% and over 99.9%, respectively, compared to transmitting raw ECG data. These results suggest that the proposed approach could contribute to improved energy efficiency and reduced transmission latency in wearable devices, supporting its potential feasibility for bandwidth-constrained IoMT deployments.

Suggested Citation

  • Naoya Morikawa & Emi Yuda, 2026. "Bandwidth-Efficient Transmission of HRV Features Using PhysioNet ECG Data for IoT-Based Wearable Health Monitoring," Future Internet, MDPI, vol. 18(8), pages 1-17, August.
    • Handle: RePEc:gam:jftint:v:18:y:2026:i:8:p:420-:d:2011591
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