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Pragmatic Models for Detection of Hypertension Using Ballistocardiograph Signals and Machine Learning.

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This study introduces two machine learning models for detecting hypertension using Ballistocardiograph (BCG) signals. The best model achieved 96.89% accuracy, offering a novel approach to hypertension identification.

Keywords:
BCGclassificationfeature extractionfeature selectionhypertension

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Area of Science:

  • Biomedical Signal Processing
  • Machine Learning Applications
  • Cardiovascular Health

Background:

  • Hypertension is a prevalent health issue linked to unhealthy lifestyle choices.
  • Ballistocardiograph (BCG) signals offer potential for non-invasive hypertension detection.
  • Accurate interpretation of BCG signals is crucial for effective classification.

Purpose of the Study:

  • To propose and evaluate two machine learning models for hypertension detection using BCG signals.
  • To explore various feature extraction, selection, and classification techniques.
  • To identify the optimal model configuration for high classification accuracy.

Main Methods:

  • Model 1: Feature extraction (K-means, MODWT, EWT), selection (BTSA, IG), and classification (Hybrid AdaBoost-MULDA, Hybrid AdaBoost-RF).
  • Model 2: Feature extraction (PCA, KPCA, RFM), selection (IG, AOA), and classification (Hybrid ARIMA-AdaBoost, TW-HASVM).
  • Utilized a publicly available BCG dataset for model validation.

Main Results:

  • Both models demonstrated effectiveness in classifying BCG signals for hypertension detection.
  • The second model, employing Kernel Principal Component Analysis (KPCA) for feature extraction and Aquila Optimization Algorithm (AOA) for feature selection, showed superior performance.
  • The Hybrid AutoRegressive Integrated Moving Average (ARIMA)-AdaBoost classifier achieved the highest accuracy of 96.89%.

Conclusions:

  • The proposed machine learning approaches, particularly the KPCA-AOA-ARIMA-AdaBoost combination, show significant promise for accurate hypertension detection using BCG signals.
  • This methodology offers a valuable, non-invasive tool for early identification and management of hypertension.
  • Further research can explore refining these techniques for broader clinical application.