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Using machine learning models for cuffless blood pressure estimation with ballistocardiogram and impedance
Shing-Hong Liu1, Yao Sun2, Bo-Yan Wu1
1Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung City, Taiwan.
Insights
This study developed a cuffless blood pressure measurement method using ballistocardiograms and impedance plethysmograms from a weight-fat scale. The technology achieved high accuracy for estimating systolic and diastolic blood pressure, paving the way for mobile health applications.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Machine Learning
Background:
- Blood pressure (BP) monitoring is critical for managing hypertension and cardiovascular diseases.
- Conventional cuff-based sphygmomanometers are incompatible with mobile health (mHealth) applications.
- Cuffless BP measurement offers a promising alternative for continuous and convenient monitoring.
Purpose of the Study:
- To develop and validate a cuffless BP estimation method using ballistocardiograms (BCG) and impedance plethysmograms (IPG) from a weight-fat scale.
- To utilize deep learning and machine learning for accurate signal quality classification and BP parameter extraction.
- To assess the feasibility of integrating this technology into mHealth devices.
Main Methods:
- A stacked model combining 1D Convolutional Neural Network (1D CNN) and Gated Recurrent Unit (GRU) was used for BCG and IPG signal quality classification.
- Pulse transit time (PTT) parameters were extracted from BCG and IPG signals.
- Random Forest (RF) and XGBoost models were employed to estimate systolic BP (SBP) and diastolic BP (DBP) using PTT parameters and heart rate (HR).
Main Results:
- Signal quality classification achieved an accuracy of 0.989.
- Five-fold cross-validation yielded high Pearson correlation coefficients: 0.953 ± 0.007 for SBP and 0.935 ± 0.007 for DBP.
- The XGBoost model demonstrated low mean absolute differences: 3.54 ± 0.34 mmHg for SBP and 2.57 ± 0.17 mmHg for DBP.
Conclusions:
- The proposed method significantly enhances the accuracy of cuffless BP measurement.
- Integration into weight-fat scales offers an unconstrained device for mHealth applications.
- This technology holds potential for improved remote patient monitoring and management of cardiovascular health.
Introduction:
Blood pressure (BP) serves as a crucial parameter in the management of three prevalent chronic diseases, hypertension, cardiovascular diseases, and cerebrovascular diseases. However, the conventional sphygmomanometer, utilizing a cuff, is unsuitable for the approach of mobile health (mHealth).
Methods:
Cuffless blood pressure measurement, which eliminates the need for a cuff, is considered a promising avenue. This method is based on the relationship between pulse arrival time (PAT) parameters and BP. In this study, pulse transit time (PTT) was derived from ballistocardiograms (BCG) and impedance plethysmograms (IPG) obtained from a weight-fat scale. This study aims to address two challenges using deep learning and machine learning technologies: first, identifying BCG and IPG signals with good quality, and then extracting PTT parameters from them to estimate BP. A stacked model comprising a one-dimensional convolutional neural network (1D CNN) and gated recurrent unit (GRU) was proposed to classify the quality of BCG and IPG signals. Seven parameters, including calibration-based and calibration-free PTT parameters and heart rate (HR), were examined to estimate BP using random forest (RF) and XGBoost models. Seventeen healthy subjects participated in the study, with their BP elevated through exercise. A digital sphygmomanometer was employed to measure BP as reference values. Our methodology was validated using data collected from our custom-made device.
Results:
The results demonstrated a signal quality classification accuracy of 0.989. Furthermore, in the five-fold cross-validation, Pearson correlation coefficients of 0.953 ± 0.007 and 0.935 ± 0.007 were achieved for systolic BP (SBP) and diastolic BP (DBP) estimations, respectively. The mean absolute differences (MADs) of XGBoost model were calculated as 3.54 ± 0.34 and 2.57 ± 0.17 mmHg for SBP and DBP, respectively.
Discussion:
The proposed method significantly improved the accuracy of cuffless BP measurement, indicating its potential integration into weight-fat scales as an unconstrained device for effective utilization in mHealth applications.
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