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Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated
Yunendah Nur Fuadah1,2, Ki Moo Lim1,3
1Computational Medicine Lab, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Insights
This study introduces a new method using combined photoplethysmogram (PPG) and electrocardiogram (ECG) signals to accurately classify blood pressure (BP) levels. The approach achieved high accuracy, aiding in early hypertension detection and management.
Area of Science:
- Biomedical Engineering
- Cardiovascular Health
- Machine Learning in Medicine
Background:
- Hypertension is a major global health concern, increasing risks for stroke, heart disease, and kidney dysfunction.
- Early blood pressure (BP) screening is crucial for preventing severe complications and mortality.
- Photoplethysmogram (PPG) and electrocardiogram (ECG) signals show potential for non-invasive BP assessment.
Purpose of the Study:
- To develop and evaluate a novel machine learning model for classifying blood pressure levels.
- To integrate features from both PPG and ECG signals for improved BP classification accuracy.
- To assess the efficacy of a concatenated convolutional neural network (CNN) for categorizing BP into hypotension, normotension, and different hypertension stages.
Main Methods:
- Utilized the MIMIC III database containing PPG, ECG, and arterial blood pressure (ABP) signals from 221 patients.
- Processed 14,298 signal segments, dividing them into training, validation, and testing sets.
- Developed a concatenated CNN architecture integrating features from both PPG and ECG signals.
- Employed five-fold cross-validation to optimize model selection and performance.
Main Results:
- The proposed concatenated CNN model achieved a high test accuracy of 94.56-95.15% (95% CI) in classifying BP levels.
- The model effectively differentiated between hypotension, normotension, prehypertension, hypertension stage 1, and hypertension stage 2.
- Feature integration from both PPG and ECG signals proved effective for accurate BP categorization.
Conclusions:
- The combined PPG and ECG signal analysis using a concatenated CNN offers a promising, accurate, and effective method for BP level classification.
- This approach can serve as a valuable tool for medical professionals in clinical diagnosis and hypertension management.
- Further research can explore the clinical utility of this non-invasive BP monitoring technique.
Abstract:
Hypertension is a severe public health issue worldwide that significantly increases the risk of cardiac vascular disease, stroke, brain hemorrhage, and renal dysfunction. Early screening of blood pressure (BP) levels is essential to prevent the dangerous complication associated with hypertension as the leading cause of death. Recent studies have focused on employing photoplethysmograms (PPG) with machine learning to classify BP levels. However, several studies claimed that electrocardiograms (ECG) also strongly correlate with blood pressure. Therefore, we proposed a concatenated convolutional neural network which integrated the features extracted from PPG and ECG signals. This study used the MIMIC III dataset, which provided PPG, ECG, and arterial blood pressure (ABP) signals. A total of 14,298 signal segments were obtained from 221 patients, which were divided into 9150 signals of train data, 2288 signals of validation data, and 2860 signals of test data. In the training process, five-fold cross-validation was applied to select the best model with the highest classification performance. The proposed concatenated CNN architecture using PPG and ECG obtained the highest test accuracy of 94.56-95.15% with a 95% confidence interval in classifying BP levels into hypotension, normotension, prehypertension, hypertension stage 1, and hypertension stage 2. The result shows that the proposed method is a promising solution to categorize BP levels effectively, assisting medical personnel in making a clinical diagnosis.
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