GRU-Based Fusion Models for Enhanced Blood Pressure Estimation From PPG Signalsopen access
- Authors
- Rizal, Syamsul; Ana Rahma, Yuniarti
- Issue Date
- Jun-2024
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Blood pressure; Biomedical monitoring; Data models; Monitoring; Estimation; Logic gates; Blood; Deep learning; Neural networks; Photoplethysmography; deep learning; gated recurrent units (GRU); neural networks; photoplethysmography (PPG)
- Citation
- IEEE ACCESS, v.12, pp 80317 - 80326
- Pages
- 10
- Journal Title
- IEEE ACCESS
- Volume
- 12
- Start Page
- 80317
- End Page
- 80326
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28791
- DOI
- 10.1109/ACCESS.2024.3409741
- ISSN
- 2169-3536
2169-3536
- Abstract
- The current study presents a novel, non-invasive method for estimating both systolic and diastolic blood pressure by combining photoplethysmogram (PPG) signals with physiological data, such as sex, age, weight, height, heart rate, and BMI, using two Gated Recurrent Units (GRUs) models. The first model processes dynamic patterns in PPG signals, while the second model incorporates physiological parameters. Both models are connected through a series of dense layers. To prepare the datasets for the GRU framework, rigorous preprocessing was conducted. This resulted in a robust architecture capable of accurately predicting systolic and diastolic blood pressure. The proposed method achieved a Mean Absolute Error (MAE) of 1.458 for systolic and 1.164 for diastolic blood pressure. These findings demonstrate the potential of this approach for continual and non-intrusive blood pressure monitoring in wearable health technology. The study's results also make a significant contribution to the field of medical monitoring technology. The proposed solution addresses a major limitation in traditional blood pressure measurement practices and paves the way for advancements in personalized health monitoring, particularly for managing hypertension and cardiovascular conditions.
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