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A Deep Convolution Method for Hypertension Detection from Ballistocardiogram Signals with Heat-Map-Guided Data
Renjie Cheng1, Yi Huang1, Wei Hu1
1Shenzhen HUAYI Medical Technologies Co., Ltd., Shenzhen 518055, China.
Insights
This study introduces BH-Net, a deep learning model for detecting hypertension using ballistocardiography (BCG) signals. The novel approach achieves high accuracy, offering a promising non-contact method for hypertension monitoring.
Area of Science:
- Biomedical Engineering
- Cardiovascular Health
- Artificial Intelligence in Medicine
Background:
- Hypertension (HPT) is a major risk factor for stroke, coronary artery disease, and heart failure.
- Non-contact detection and continuous monitoring of HPT are challenging clinical needs.
- Ballistocardiography (BCG) signals, reflecting heartbeat-induced body motion, offer potential for HPT assessment.
Purpose of the Study:
- To develop an end-to-end deep convolutional model (BH-Net) for hypertension detection using BCG signals.
- To propose a data augmentation scheme to improve the accuracy of HPT detection from BCG.
- To evaluate the proposed model and data augmentation against existing state-of-the-art methods.
Main Methods:
- An end-to-end deep convolutional neural network, termed BH-Net, was designed for HPT detection.
- A novel data augmentation technique focused on J-peak neighborhoods within BCG time sequences was implemented.
- The BH-Net model and data augmentation scheme were rigorously evaluated using a public BCG dataset.
Main Results:
- The proposed BH-Net model achieved an average accuracy of 97.93% and an average F1-score of 97.62%.
- The model significantly outperformed existing state-of-the-art methods for HPT detection using BCG.
- The data augmentation scheme demonstrably improved the performance of both traditional machine learning and comparative deep learning models.
Conclusions:
- BH-Net represents a highly effective deep learning approach for non-contact hypertension detection via BCG signals.
- The proposed data augmentation strategy enhances the robustness and accuracy of BCG-based HPT detection.
- This research offers a promising, non-invasive tool for hypertension screening and monitoring.
Abstract:
Hypertension (HPT) is a chronic disease characterized by the consistent elevation of arterial blood pressure, which is considered to be a significant risk factor for conditions such as stroke, coronary artery disease, and heart failure. The detection and continuous monitoring of HPT can be a demanding process. As a non-contact measuring method, the ballistocardiography (BCG) signal characterizes the repetitive body motion resulting from the forceful ejection of blood into the major blood vessels during each heartbeat. Therefore, it can be applied for HPT detection. HPT detection with BCG signals remains a challenging task. In this study, we propose an end-to-end deep convolutional model BH-Net for HPT detection through BCG signals. We also propose a data augmentation scheme by selecting the J-peak neighborhoods from the BCG time sequences for hypertension detection. Rigorously evaluated via a public data-set, we report an average accuracy of 97.93% and an average F1-score of 97.62%, outperforming the comparative state-of-the-art methods. We also report that the performance of the traditional machine learning methods and the comparative deep learning models was improved with the proposed data augmentation scheme.
