GRU-Based Fusion Models for Enhanced Non-Invasive Blood Pressure
Estimation from PPG Signals
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 studyâ\euro™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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