A hybrid CNN-SVR model using ECG and PPG signals estimated systolic and diastolic blood pressure with a Mean Absolute Error of 1.23 ± 2.45 mmHg and 3.08 ± 5.67 mmHg, respectively.
Does a hybrid CNN-SVR model using ECG and PPG signals accurately estimate continuous blood pressure?
A novel hybrid CNN-SVR model using ECG and PPG signals can accurately estimate continuous blood pressure, meeting AAMI SP10 standards.
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.
Rastegar et al. (Sun,) conducted a other in Blood pressure estimation (n=120). Hybrid CNN-SVR model was evaluated on Mean Absolute Error (MAE) for SBP and DBP. A hybrid CNN-SVR model using ECG and PPG signals estimated systolic and diastolic blood pressure with a Mean Absolute Error of 1.23 ± 2.45 mmHg and 3.08 ± 5.67 mmHg, respectively.