Szczegóły publikacji
Opis bibliograficzny
Assessment of blood flow parameters in a hybrid-digital model of the cardiovascular system applying recurrent neural networks / Michał Ślęzak, Magdalena KOPERNIK, Karolina Szawiraacz, Grzegorz Milewski // Biomedical Signal Processing and Control ; ISSN 1746-8094. — 2024 — vol. 98 art. no. 106680, s. 1–15. — Bibliogr. s. 14–15, Abstr. — Publikacja dostępna online od: 2024-08-20. — K. Szawiraacz - afiliacja: Institute of Metallurgy and Materials Science, Polish Academy of Sciences, Cracow
Autorzy (4)
- Ślęzak Michał
- AGHKopernik Magdalena
- Szawiraacz Karolina
- Milewski Grzegorz
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 154904 |
|---|---|
| Data dodania do BaDAP | 2024-09-03 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.bspc.2024.106680 |
| Rok publikacji | 2024 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Biomedical Signal Processing and Control |
Abstract
End-systolic elastance of the left ventricle along with the waveforms of pressure and volumetric blood flow in particular sectors of the circulatory system are of importance in diagnosing various problems like dilated cardiomyopathy, left-ventricular hypertrophy, pulmonary hypertension, or ischemic heart disease. The objective of the paper is to broaden the spectrum of available methods to estimate those parameters since currently accessible techniques are often costly or troublesome. Six models have been developed − three of them estimate end-systolic elastance, two perform regression of volumetric blood flow, and one predicts blood pressure. Training datasets have been collected applying the unique hybrid-digital model. The input of the designed models consists of two or three different waveforms representing pressure and volumetric blood flow in particular areas, including heart ventricles, atria, and pulmonary vessels, in addition to the heart rate value. The basis of each model comprises bidirectional Long Short-Term Memory layers along with the dropout and feed-forward layers. Models that estimate end-systolic elastance achieved various accuracy. One of them performed exceptionally well since the absolute error did not exceed 0.169 [Formula presented] which is a negligibly small value. The root-mean-square error (RMSE) of the model predicting pressure waveform reached 0.165 mmHg in the worst case. Regression of the volumetric blood flow resulted in 6.062 [Formula presented] worst-case RMSE for the model focusing on the pulmonary valve and 15.979 [Formula presented] for pulmonary veins model. Computed results, especially those of the models estimating end-systolic elastance, indicate that it is possible to utilize neural networks to estimate those parameters with sufficient accuracy.
