Prediction of Pulmonary Artery and Peripheral Vascular Pressure based on Echocardiography Data and Artificial Neural Network

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ABBASI HAMED; SHOJAEI SH.; NADERI N.

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Journal: IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING Year:2019 | Volume:13 | Issue:2 Page(s): 105-115

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Title

Prediction of Pulmonary Artery and Peripheral Vascular Pressure based on Echocardiography Data and Artificial Neural Network

Author(s)

ABBASI HAMED | SHOJAEI SH. | NADERI N. | Issue Writer Certificate

Keywords

predictionQ2

Artificial neural networkQ2

Pulmonary Vascular ResistanceQ1

EchocardiographyQ1

Abstract

Today, in order to decide on many cardiac surgeries, and whether the patient is able to get under surgery or the time of surgery is passed, it is necessary to measure Pulmonary Vascular Resistance and if the resistance is above a threshold, the patient is considered to be non-surgery; and sometimes, some therapies are used to reduce the resistance of the pulmonary arteries to the initial disease of the arteries, in which, in order to track down the resistance of the pulmonary vascular, a re-measurement of this parameter is required. Currently, the golden standard of this measure is the use of catheterization procedures, which are aggressive and associated with complications. The purpose of this study is to replace a non-invasive method, rather than an invasive method of cardiac catheterization, by predicting Pulmonary Vascular Resistance based on echocardiographic data by Artificial neural networks. Research was performed on 591 patients. Echocardiography was recorded for all subjects, and the echocardiographic data (mPAP, dPAP, sPAP, PCWP, CO) as the neural network input and Pulmonary Vascular Resistance of all patients who were subjected to previous catheterization was evaluated as the output of the neural network and thus, it was obtained, the relationship between Echocardiography data and PVRcath. The proposed neural network was typically learned with 75% of the data, and was tested with 25% of the data, and these ratios were modified to better learn the neural network. As a result of implementation, the mean squared error, respectively, for the learning and testing data for the proposed neural network, was 0. 37 and 0. 27 for the first model, 14. 67 and 10. 76 for the second model, and 15. 82 and 9. 58 for the third model.

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APA: Copy

ABBASI, HAMED, SHOJAEI, SH., & NADERI, N.. (2019). Prediction of Pulmonary Artery and Peripheral Vascular Pressure based on Echocardiography Data and Artificial Neural Network. IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING, 13(2 ), 105-115. SID. https://sid.ir/paper/369924/en

Vancouver: Copy

ABBASI HAMED, SHOJAEI SH., NADERI N.. Prediction of Pulmonary Artery and Peripheral Vascular Pressure based on Echocardiography Data and Artificial Neural Network. IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING[Internet]. 2019;13(2 ):105-115. Available from: https://sid.ir/paper/369924/en

IEEE: Copy

HAMED ABBASI, SH. SHOJAEI, and N. NADERI, “Prediction of Pulmonary Artery and Peripheral Vascular Pressure based on Echocardiography Data and Artificial Neural Network,” IRANIAN JOURNAL OF BIOMEDICAL ENGINEERING, vol. 13, no. 2 , pp. 105–115, 2019, [Online]. Available: https://sid.ir/paper/369924/en

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