Journal Article

Estimation of Stroke Volume Variance from Arterial Blood Pressure: Using a 1-D Convolutional Neural Network

Shim, Woo-Hyun, Seo, Woo-Young, Kwon, Hye-Mee, Kim, Sung-Hoon, Hwang, Gyu-Sam, Kim, Jae-Man

2021

Overview

Background: We aimed to create a novel model using a deep learning method to estimate stroke volume variation (SVV), a widely used predictor of fluid responsiveness, from arterial blood pressure waveform (ABPW). Methods: In total, 557 patients and 8,512,564 SVV datasets were collected and were divided into three groups: training, validation, and test. Data was composed of 10 s of ABPW and corresponding SVV data recorded every 2 s. We built a convolutional neural network (CNN) model to estimate SVV from the ABPW with pre-existing commercialized model (EV1000) as a reference. We applied pre-processing, multichannel, and dimension reduction to improve the CNN model with diversified inputs. Results: Our CNN model showed an acceptable performance with sample data (r = 0.91, MSE = 6.92). Diversification of inputs, such as normalization, frequency, and slope of ABPW significantly improved the model correlation (r = 0.95), lowered mean squared error (MSE = 2.13), and resulted in a high concordance rate (96.26%) with the SVV from the commercialized model. Conclusions: We developed a new CNN deep-learning model to estimate SVV. Our CNN model seems to be a viable alternative when the necessary medical device is not available, thereby allowing a wider range of application and resulting in optimal patient management.

• Publisher: MDPI AG,MDPI
• Subject: Anesthesia / Bias / Blood pressure / Cardiac arrhythmia / CNN model / Datasets / Deep learning / diagnostic / Diagnostic tests / Electrocardiography / Fentanyl / Heart rate / mechanical ventilation / Medical diagnosis / Neural networks / Patients / prediction / Pulmonary arteries / Respiration / stroke volume variance / Variables