1-Dimensional Convolutional Neural Network Based Blood Pressure Estimation with Photo plethysmography Signals and Semi-Classical Signal Analysis

Abstract

In this paper, we propose a 1-Dimensional Convolutional Neural Network (1D-CNN) based Blood Pressure (BP) estimation using Photo plethysmography (PPG) signals and their features obtained through Semi-classical Signal Analysis (SCSA). The procedure of the proposed BP estimation technique is as follows. First, PPG signals are divided into each beat. Then, 9 features are obtained through SCSA for the divided beats. In addition, 5 biometric data are used. The Biometrics data include Heart Rate (HR), age, sex, height, and weight. The total 14 features are used for training and validating the 1D-CNN BP estimation model. After testing three types of 1D-CNNs, the model with the most optimal performance is selected. The selected model structure consists of three convolutional layers and one fully connected layer. The performance is measured by Mean Error (ME) ± Standard Deviation (STD) following the Association for the Advancement of Medical Instrumentation (AAMI) standard. According to the results of the test, Systolic Blood Pressure (SBP) is -2.99±14.48 mmHg and Diastolic Blood Pressure (DBP) is 1.16±9.30 mmHg. Using the proposed technique, blood pressure can be easily predicted using PPG obtained with a non-invasive and cuff-less wearable sensor.