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Related Experiment Video

Updated: Jul 12, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
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Cuffless Hypertension Detection using Swarm Support Vector Machine Utilizing Photoplethysmogram and

Nuryani Nuryani1, Trio Pambudi Utomo1, Nanang Wiyono2

  • 1Department of Physics, University of Sebelas Maret Jl. Ir. Sutami 36A Kentingan Jebres Surakarta 57126, Indonesia.

Journal of Biomedical Physics & Engineering
|October 23, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a cuff-free hypertension detection system using Electrocardiogram (ECG) and Photoplethysmogram (PPG) signals with a Swarm-based Support Vector Machine (SSVM) algorithm, achieving 96% accuracy. This non-invasive method offers a promising approach for early hypertension monitoring.

Keywords:
Medical InformaticsPhotoplethysmographySupport Vector Machine

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Area of Science:

  • Biomedical Engineering
  • Cardiovascular Health
  • Artificial Intelligence in Medicine

Background:

  • Hypertension poses significant health risks, necessitating early detection to prevent severe complications.
  • Timely identification of hypertension events is crucial for effective management and improved patient outcomes.

Purpose of the Study:

  • To investigate a novel, cuff-free strategy for hypertension detection.
  • To utilize bioelectric signals, specifically Electrocardiogram (ECG) and Photoplethysmogram (PPG), for non-invasive blood pressure monitoring.
  • To develop and optimize a Swarm-based Support Vector Machine (SSVM) algorithm for accurate hypertension detection.

Main Methods:

  • Collected ECG and PPG data from normal and hypertensive participants from the MIMIC database.
  • Extracted key parameters including Pulse Arrival Time (PAT) and PPG signal derivatives.
  • Employed an SSVM algorithm, optimized with Quantum Delta-potential-well Particle Swarm Optimization (QDPSO), to analyze extracted parameters.

Main Results:

  • The proposed SSVM strategy achieved a 96% performance rate across F1-score, accuracy, sensitivity, and specificity.
  • The developed system demonstrated superior performance compared to other tested methods.
  • The study successfully developed a functional cuff-free hypertension monitoring system.

Conclusions:

  • The SSVM algorithm, utilizing ECG and PPG parameters, provides an acceptable method for hypertension detection.
  • Integrating both ECG and PPG signals yields better hypertension detection performance than using PPG alone.
  • The research validates the potential of non-invasive bioelectric signal analysis for hypertension management.