Estimation of Systolic and Diastolic Blood Pressure for Hypertension Identification from Photoplethysmography Signals

Continuous monitoring plays a crucial role in diagnosing hypertension, characterized by the increase in Arterial Blood Pressure (ABP). The gold-standard method for obtaining ABP involves the uncomfortable and invasive technique of cannulation. Conversely, ABP can be acquired non-invasively by using...

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Bibliographic Details
Published in:Applied sciences 2024-03, Vol.14 (6), p.2470
Main Authors: De Oliveira, Hygo Sousa, Pinto, Rafael Albuquerque, Souto, Eduardo James Pereira, Giusti, Rafael
Format: Article
Language:English
Subjects:
Algorithms
arterial blood pressure
Blood pressure
continuous monitoring
Deep learning
dilated convolution
Electrocardiography
Heart rate
Human error
Hypertension
Neural networks
photoplethysmograph
Sensors
Signal processing
Wavelet transforms
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Summary:Continuous monitoring plays a crucial role in diagnosing hypertension, characterized by the increase in Arterial Blood Pressure (ABP). The gold-standard method for obtaining ABP involves the uncomfortable and invasive technique of cannulation. Conversely, ABP can be acquired non-invasively by using Photoplethysmography (PPG). This non-invasive approach offers the advantage of continuous BP monitoring outside a hospital setting and can be implemented in cost-effective wearable devices. PPG and ABP signals differ in scale values, which creates a non-linear relationship, opening avenues for the utilization of algorithms capable of detecting non-linear associations. In this study, we introduce Neural Model of Blood Pressure (NeuBP), which estimates systolic and diastolic values from PPG signals. The problem is treated as a binary classification task, distinguishing between Normotensive and Hypertensive categories. Furthermore, our research investigates NeuBP’s performance in classifying different BP categories, including Normotensive, Prehypertensive, Grade 1 Hypertensive, and Grade 2 Hypertensive cases. We evaluate our proposed method by using data from the publicly available MIMIC-III database. The experimental results demonstrate that NeuBP achieves results comparable to more complex models with fewer parameters. The mean absolute errors for systolic and diastolic values are 5.02 mmHg and 3.11 mmHg, respectively.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14062470