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BCG Signal Quality Assessment Based on Time-Series Imaging Methods.

Sungtae Shin1, Soonyoung Choi1, Chaeyoung Kim2

  • 1Department of Mechanical Engineering, Dong-A University, Busan 49315, Republic of Korea.

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|December 9, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a method to classify arm ballistocardiogram (BCG) signal quality for accurate, non-invasive blood pressure monitoring. Using image-based deep learning, the system achieved 87.5% accuracy in distinguishing high-quality from low-quality BCG signals.

Keywords:
ballistocardiogramclassificationconvolutional neural networksignal quality assessmenttime-series imaging

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Wearable Technology

Background:

  • Ballistocardiogram (BCG) signals offer potential for non-invasive, continuous blood pressure monitoring using accelerometers in wearable devices.
  • BCG signals are prone to noise from motion artifacts, degrading blood pressure estimation accuracy.
  • Effective signal quality classification is crucial to maintain the performance of BCG-based blood pressure measurement systems.

Purpose of the Study:

  • To develop and evaluate a binary classification model for distinguishing high-quality from low-quality arm ballistocardiogram (BCG) signals.
  • To prevent performance degradation in non-invasive blood pressure estimation caused by noisy BCG data.
  • To explore time-series imaging techniques and convolutional neural network (CNN) architectures for BCG signal classification.

Main Methods:

  • Four time-series imaging methods (recurrence plot, Gramain angular summation field, Gramain angular difference field, Markov transition field) were employed to convert BCG signals into images.
  • Convolutional Neural Network (CNN) models including ResNet, SqueezeNet, DenseNet, and LeNet were utilized for image classification.
  • A dataset of 9626 BCG beats was used for training, validation, and testing the classification models.

Main Results:

  • The Gramain angular difference field method, when combined with ResNet and SqueezeNet CNN models, achieved a high binary classification accuracy of up to 87.5%.
  • This indicates the effectiveness of converting temporal BCG signals into image representations for quality assessment.
  • The study successfully demonstrated a method to identify usable BCG signals for reliable blood pressure monitoring.

Conclusions:

  • The developed binary classification model effectively distinguishes between high-quality and low-quality BCG signals, crucial for accurate non-invasive blood pressure monitoring.
  • Time-series imaging combined with deep learning CNNs provides a robust approach for BCG signal quality assessment.
  • This method holds promise for improving the reliability and performance of wearable devices for continuous blood pressure measurement.