Hybrid CNN-SVR Blood Pressure Estimation Model Using ECG and PPG Signals

Abstract

Continuous blood pressure (BP) measurement is vital in monitoring patients’ health with a high risk of cardiovascular disease. The complex and dynamic nature of the cardiovascular system can influence BP through many factors, such as cardiac output, blood vessel wall elasticity, circulated blood volume, peripheral resistance, respiration, and emotional behavior. Yet, traditional BP measurement methods in continuously estimating the BP are cumbersome and inefficient. This paper presents a novel hybrid model by integrating a convolutional neural network (CNN) as a trainable feature extractor and support vector regression (SVR) as a regression model. This model can automatically extract features from the electrocardiogram (ECG) and photoplethysmography (PPG) signals and continuously estimates the systolic blood pressure (SBP) and diastolic blood pressure (DBP). The CNN takes the correct topology of input data and establishes the relationship between ECG and PPG features and BP. A total of 120 patients with available ECG, PPG, SBP, and DBP data are selected from the MIMIC III database to evaluate the performance of the proposed model. This novel model achieves an overall Mean Absolute Error (MAE) of 1.23 2.45 mmHg (MAE ± STD) for SBP and 3.08 5.67 for DBP, all of which comply with the accuracy requirements of the AAMI SP10 standard.

Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter

“…In this study, we employed a custom lightweight CNN model rather than using pre-trained models from ImageNet. While pre-trained models can be fine-tuned for efficient performance, it is not always necessary to use highly trained models [ 34 , 35 ]. However, the performance of CNN models may vary based on the hyperparameter values, such as the model depth, optimizer, loss function, and preprocessing steps [ 36 , 37 ].…”

RGB Camera-Based Blood Pressure Measurement Using U-Net Basic Generative Model

“…However, this method required 10-s signal segments as input data. Solmaz Rastegar et al proposed a method for predicting blood pressure by integrating a convolutional neural network (CNN) structure and support vector regression (SVR), using both ECG and PPG signals as input [9]. Blood pressure estimation performance was good, but this method required a second signal in addition to the PPG signal.…”

“…This dataset includes various vital signs such as blood pressure, ECG, and pulse rate. Solmaz Rastegar et al [9] utilized a subset of the Medical Information Mart for Intensive Care III (MIMIC-III) waveform database [12], which contains various physiological waveforms extracted with a sampling frequency of 125 Hz. Recently, longitudinal studies have been conducted to directly predict blood pressure from PPG signals based on the morphological similarities between PPG and blood pressure waveforms.…”

“…However, widespread access to blood pressure monitors and the challenges associated with consistent measurement hinder effective monitoring efforts. To address this problem, numerous studies have explored alternative methods, one of which is blood pressure prediction using contactless photoplethysmography (cPPG) signals, eliminating the need for traditional cuffbased monitors [2][3][4][5][6][7][8][9]. cPPG offers a non-invasive approach to blood pressure estimation, utilizing data that can be extracted from various sources such as hospitals, wristbands, and smartwatches.…”