Document Type : Full Research Paper


1 Department of Electrical and Computer Engineering, Jundi-Shapur University of Technology

2 Assistant Professor, Department of Electrical and Computer Engineering, Jundi-Shapur University of Technology



Hypertension is the leading cause of death worldwide. Continuous blood pressure (BP) measurement is crucial for the elderly and people with myocardial infarction, cardiovascular disease, kidney disease and gestational hypertension. Cuff-based blood pressure Holters are the most common method for continuous blood pressure measurement, but due to the use of an inflatable cuff, they often cause discomfort, particularly during sleep. A solution to such problems is the optical measurement of blood pressure using the photoplethysmogram (PPG) signal. This paper introduces a transfer deep learning framework for estimating systolic BP (SBP) and diastolic BP (DBP) using a single PPG signal. The proposed framework consists of three main parts: 1) downsampling by a factor of 4 aimed at reducing model complexity, 2) designing a pre-trained model including CNN and BiLSTM layers, and 3) personalizing the pre-trained model for each patient through transfer learning. We carry out Bland-Altman and correlation analysis to compare our method to the invasive arterial catheter (the gold-standard BP measurement method). Our model was validated on a wide range of BP signals acquired from 100 patients in MIMIC-III database. Results showed that the error and Pearson correlation coefficient of our model are 0.14±7.38 mmHg (mean ± standard deviation) and 0.95 for SBP, and 0.00±4.67 mmHg and 0.92 for DBP. The proposed method satisfies the requirements the AAMI and IEEE-1708a standard and receives a grade A according to the BHS standard. This research has shed light on long-term BP monitoring and the prevention of cardiovascular events.


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