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

Sensors

卷:21

Gyu-Sam Hwang¹  Sung-Hoon Kim¹  Hye-Mee Kwon¹  Woo-Hyun Shim²  Woo-Young Seo³  Jae-Man Kim³ 

[1] Asan Medical Center, Department of Anesthesiology and Pain Medicine, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Seoul 05505, Korea;

[2] Asan Medical Center, Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul 05505, Korea;

[3] Biomedical Engneering Research Center, Asan Medical Center, Seoul 05505, Korea;

关键词: stroke volume variance;    deep learning;    prediction;    CNN model;    mechanical ventilation;    diagnostic;   

DOI  :  10.3390/s21155130

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown