Szczegóły publikacji
Opis bibliograficzny
Classification of valvular heart diseases based on heart rate variability and Hjorth parameters in electrocardiograms, seismocardiograms, and gyrocardiograms / Szymon SIECIŃSKI, Ewaryst Tkacz, Marcin Grzegorzek // W: EMBC 2025 [Dokument elektroniczny] : 2025 47th annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC) : Copenhagen, Denmark, 14–18 July 2025 : proceedings. — Wersja do Windows. — Dane tekstowe. — Piscataway : Institute of Electrical and Electronics Engineers, cop. 2025. — ( Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society ; ISSN 1094-687X ). — e-ISBN: 979-8-3315-8618-8. — S. [1–4]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [3–4], Abstr. — Publikacja dostępna online od: 2025-12-03. — Sz. Sieciński - dod. afiliacje: Academy of Silesia, Katowice ; University of Luebeck, Lübeck, Germany
Autorzy (3)
- AGHSieciński Szymon
- Tkacz Ewaryst
- Grzegorzek Marcin
Dane bibliometryczne
| ID BaDAP | 165041 |
|---|---|
| Data dodania do BaDAP | 2026-01-12 |
| Tekst źródłowy | URL |
| DOI | 10.1109/EMBC58623.2025.11252906 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | The Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2025 |
| Czasopismo/seria | Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
Abstract
Valvular heart disease (VHD) is defined as a cardiovascular disease that affects any heart valve and is becoming a major concern for public health due to the growing prevalence, impact on patient quality of life and healthcare costs. In this study, we carried out the classification of valvular heart diseases based on heart rate variability (HRV) indices and Hjorth parameters in electrocardiograms (ECG), seismocardiography (SCG), and gyrocardiogram (GCG) with the Classification Learner App in MATLAB R2024a. The study was carried out on 30 concurrent ECG, SCG, and GCG signals with annotated heartbeats taken from a publicly available dataset. The most frequently used classifier was Efficient Logistic Regression (binary linear classifier). The highest classification accuracy was observed for the detection of mitral regurgitation (86.7% for all signals), followed by mitral stenosis (83.3%-86.7%) and tricupsid regurgitation (70.0%-76.7%). The most relevant features for mitral regurgitation and mitral stenosis were based on frequency domain and ellipse area, except for SCG, and for tricupsid regurgitation were based on time domain for SCG and GCG.
