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

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5130
Author(s):  
Hye-Mee Kwon ◽  
Woo-Young Seo ◽  
Jae-Man Kim ◽  
Woo-Hyun Shim ◽  
Sung-Hoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Deep Learning ◽  
Stroke Volume ◽  
Convolutional Neural Network ◽  
Arterial Blood Pressure ◽  
Mean Squared Error ◽  
Arterial Blood ◽  
Blood Pressure Waveform ◽  
High Concordance

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.

scholarly journals Deep Learning-Based Stroke Volume Estimation Outperforms Conventional Arterial Contour Method in Patients with Hemodynamic Instability

2019 ◽  
Vol 8 (9) ◽  
pp. 1419 ◽  
Author(s):  
Young-Jin Moon ◽  
Hyun S. Moon ◽  
Dong-Sub Kim ◽  
Jae-Man Kim ◽  
Joon-Kyu Lee ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Deep Learning ◽  
Stroke Volume ◽  
Arterial Blood Pressure ◽  
Gold Standard ◽  
Hemodynamic Instability ◽  
Data Sets ◽  
Pressure Waveform ◽  
Arterial Blood ◽  
Blood Pressure Waveform

Although the stroke volume (SV) estimation by arterial blood pressure has been widely used in clinical practice, its accuracy is questionable, especially during periods of hemodynamic instability. We aimed to create novel SV estimating model based on deep-learning (DL) method. A convolutional neural network was applied to estimate SV from arterial blood pressure waveform data recorded from liver transplantation (LT) surgeries. The model was trained using a gold standard referential SV measured via pulmonary artery thermodilution method. Merging a gold standard SV and corresponding 10.24 seconds of arterial blood pressure waveform as an input/output data set with 2-senconds of sliding overlap, 484,384 data sets from 34 LT surgeries were used for training and validation of DL model. The performance of DL model was evaluated by correlation and concordance analyses in another 491,353 data sets from 31 LT surgeries. We also evaluated the performance of pre-existing commercialized model (EV1000), and the performance results of DL model and EV1000 were compared. The DL model provided an acceptable performance throughout the surgery (r = 0.813, concordance rate = 74.15%). During the reperfusion phase, where the most severe hemodynamic instability occurred, DL model showed superior correlation (0.861; 95% Confidence Interval, (CI), 0.855–0.866 vs. 0.570; 95% CI, 0.556–0.584, P < 0.001) and higher concordance rate (90.6% vs. 75.8%) over EV1000. In conclusion, the DL-based model was superior for estimating intraoperative SV and thus might guide physicians to precise intraoperative hemodynamic management. Moreover, the DL model seems to be particularly promising because it outperformed EV1000 in circumstance of rapid hemodynamic changes where physicians need most help.

scholarly journals A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

2021 ◽  
Author(s):  
Annunziata Paviglianiti ◽  
Vincenzo Randazzo ◽  
Stefano Villata ◽  
Giansalvo Cirrincione ◽  
Eros Pasero
Keyword(s):  
Blood Pressure ◽  
Neural Networks ◽  
Deep Learning ◽  
Arterial Blood Pressure ◽  
Blood Pressure Measurement ◽  
National Standards ◽  
Detection Methods ◽  
Physiological Parameter ◽  
Arterial Blood ◽  
Learning Techniques

AbstractContinuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino.

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