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Related Experiment Videos

Robust Non-Invasive Cardiac Index Prediction via Feature Integration and Data-Augmented Neural Networks.

Chih-Hao Chang1, Mei-Ling Chan2, Yu-Hung Fang3

  • 1Department of Computer Science and Information Engineering, National Central University, Taoyuan 320317, Taiwan.

Bioengineering (Basel, Switzerland)
|May 4, 2026
PubMed

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Summary
This summary is machine-generated.

This study uses non-invasive Internet of Things (IoT) devices and an artificial neural network (ANN) to predict cardiac index (CI). The ANN model achieved 97.78% accuracy, offering a practical solution for real-time cardiovascular health assessment.

Area of Science:

  • Cardiovascular Health
  • Biomedical Engineering
  • Artificial Intelligence in Medicine

Background:

  • Rising obesity and cardiovascular issues in young adults due to lifestyle changes present a global health challenge.
  • Current methods for assessing cardiac hemodynamic parameters can be invasive or lack real-time capabilities.
  • There is a need for accessible, non-invasive methods for monitoring cardiovascular health in young adults.

Purpose of the Study:

  • To develop and validate a non-invasive framework for predicting cardiac index (CI) using Internet of Things (IoT) sensing devices and artificial neural networks (ANN).
  • To assess the accuracy and stability of the proposed ANN model for real-time CI prediction.
  • To explore the potential of this integrated system for early detection and management of cardiovascular conditions.
Keywords:
IoMTIoTartificial neural networkscardiac indexcardiovascular disease assessmentdata augmentationmachine learningnon-invasive sensingphysiological signal processing

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Main Methods:

  • Integration of non-invasive IoT devices: TERUMO ES-P2000 blood pressure monitor and PhysioFlow PF07 Enduro cardiac hemodynamic analyzer.
  • Development of an artificial neural network (ANN) model for cardiac index (CI) prediction, utilizing physiological parameters.
  • Application of data preprocessing and advanced model training strategies to enhance prediction accuracy and model generalization.

Main Results:

  • The ANN model achieved a high classification accuracy of 97.78% when using three physiological parameters as input.
  • The model demonstrated strong predictive performance even with only two input parameters.
  • The proposed framework proved effective and practical for real-time, non-invasive CI assessment, outperforming traditional methods.

Conclusions:

  • The developed ANN-based framework offers a highly accurate and non-invasive method for real-time cardiac index assessment.
  • This technology holds significant potential for improving cardiovascular health monitoring and management in young adults.
  • The study highlights the successful application of IoT and AI in addressing critical public health concerns related to cardiovascular disease.