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Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with
Jaypal Singh Rajput1, Manish Sharma1, T Sudheer Kumar1
1Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026, India.
Insights
Hypertension (HPT) detection is challenging. This study introduces a novel deep-learning approach using ballistocardiography (BCG) signals and a 2D-CNN model, achieving 86.14% accuracy for automated HPT identification.
Area of Science:
- Cardiovascular Health
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Hypertension (HPT) is a critical global health issue, often asymptomatic in early stages, leading to delayed diagnosis and severe complications.
- Standard blood pressure (BP) monitoring can be inconsistent, complicating HPT identification and treatment.
- Existing diagnostic methods face challenges in detecting subtle, early-stage HPT.
Purpose of the Study:
- To develop an automated method for detecting hypertension (HPT) using ballistocardiography (BCG) signals.
- To investigate the efficacy of a deep-learning model for classifying HPT from BCG data.
- To establish a novel, non-invasive approach for HPT screening and diagnosis.
Main Methods:
- Ballistocardiography (BCG) signals were acquired and processed.
- Continuous Wavelet Transform (CWT) was employed to convert BCG signals into scalogram images.
- A 2-D Convolutional Neural Network (2D-CNN) model was trained to differentiate between healthy controls (HC) and HPT patients.
Main Results:
- The 2D-CNN model achieved a high classification accuracy of 86.14%.
- A ten-fold cross-validation strategy confirmed the model's robust performance.
- This study represents the first application of a 2D-CNN with BCG signals for HPT detection.
Conclusions:
- Deep learning models, specifically 2D-CNN, show significant promise for automated hypertension detection using BCG signals.
- BCG-based analysis offers a potential non-invasive and accessible tool for early HPT identification.
- Further research can refine this approach for clinical application in managing hypertension.
Abstract:
Managing hypertension (HPT) remains a significant challenge for humanity. Despite advancements in blood pressure (BP)-measuring systems and the accessibility of effective and safe anti-hypertensive medicines, HPT is a major public health concern. Headaches, dizziness and fainting are common symptoms of HPT. In HPT patients, normalcy may be observed at one instant and abnormality may prevail during a long duration of 24 h ambulatory BP. This may cause difficulty in identifying patients with HPT, and hence there is a possibility that individuals may be untreated or administered insufficiently. Most importantly, uncontrolled HPT can lead to severe complications (stroke, heart attack, kidney disease, and heart failure), mainly ignoring the signs in nascent stages. HPT in the beginning stages may not present distinct symptoms and may be difficult to diagnose from standard physiological signals. Hence, ballistocardiography (BCG) signal was used in this study to detect HPT automatically. The processed signals from BCG were converted into scalogram images using a continuous wavelet transform (CWT) and were then fed into a 2-D convolutional neural network model (2D-CNN). The model was trained to learn and recognize BCG patterns of healthy controls (HC) and HPT classes. Our proposed model obtained a high classification accuracy of 86.14% with a ten-fold cross-validation (CV) strategy. Hence, this is the first use of a 2D-CNN model (deep-learning algorithm) to detect HPT employing BCG signals.
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