Título:	Method and algorithm of window wavelet processing of photopletysmographic signal in the Mayer basis as a tool for diagnostic arrhythmias
Authors:	Khvostivski, Mykola Kirash, Victoriia Khvostivska, Liliia Karabinenko, Yuliia
Bibliographic description (Ukraine):	Khvostivskyi M., Kirash V., Khvostivska L., Karabinenko Yu. Method and algorithm of window wavelet processing of photopletysmographic signal in the Mayer basis as a tool for diagnostic arrhythmias. Collection of Scientific Papers with the Proceedings of the 3rd International Scientific and Practical Conference «Modern Problems of Science and Technology» (September 22-24, 2025, Tallinn, Estonia). European Open Science Space, 2025. p.142-147. DOI: 10.70286/eoss-22.09.2025.004. ISBN 979-8-89704-951-6
Data de edición:	22-Sep-2025
Date of entry:	22-Sep-2025
Editor:	European Open Science Space
Place of the edition/event:	Tallinn, Estonia
Page range:	142-147
URI:	http://elartu.tntu.edu.ua/handle/lib/50072
ISBN:	979-8-89704-951-6
Copyright owner:	@ Khvostivskyi M., Kirash V., Khvostivska L., Karabinenko Yu.
References (Ukraine):	1. World Health Organization. Newborn mortality [Internet]. Updated Mar 14, 2024. Available from: https://www.who.int. 2. Park J., Lee J., Oh J. Review of photoplethysmogram analysis: spectral approaches and applications // Frontiers in Physiology. – 2022. – Vol. 13. – P. 1–12. DOI: 10.3389/fphys.2022.123456. 3. Bereznyi I.V., Nakonechnyi A. Wavelet analysis of remote photoplethysmography for rhythm anomaly detection // Information Systems and Technologies in Medicine and Engineering. – 2025. – Vol. 29, №1. – P. 45–53. 4. Cheng Y., Zhang X., Li H. Continuous wavelet transform and deep learning for atrial fibrillation detection using PPG // Biomedical Signal Processing and Control. – 2021. – Vol. 68. – P. 102741. DOI: 10.1016/j.bspc.2021.102741. 5. Väliaho E.-S., Lipponen J.A., Kuoppa P., Martikainen T.J. et al. Autocorrelation analysis enables detection of atrial fibrillation from photoplethysmography without pulse detection // Frontiers in Physiology. – 2021. – Vol. 12. – P. 726451. DOI: 10.3389/fphys.2021.726451. 6. Tison G.H., Sanchez J.M., Ballinger B. et al. Passive detection of atrial fibrillation using a commercially available smartwatch // JAMA Cardiology. – 2018. – Vol. 3(5). – P. 409–416. DOI: 10.1001/jamacardio.2018.0136. 7. Paradkar N., Chowdhury S.R. Cardiac arrhythmia detection using photoplethysmography: PhysioNet Challenge 2015 // Computing in Cardiology. – 2015. – Vol. 42. – P. 273–276. 8. Park J., Lee S., Jeon M. Atrial fibrillation detection by heart rate variability in Poincaré plot of PPG // Computers in Biology and Medicine. – 2009. – Vol. 39(8). – P. 746–754. DOI: 10.1016/j.compbiomed.2009.06.006. 9. Bashar S.K., Han D., Hajeb-Mohammadalipour S. et al. Atrial fibrillation detection from photoplethysmography using smartwatches // Scientific Reports. – 2019. – Vol. 9. – P. 15054. DOI: 10.1038/s41598-019-50940-0. 10. Pereira T., Tran N., Gadhoumi K. et al. Photoplethysmography based atrial fibrillation detection: a review // NPJ Digital Medicine. – 2020. – Vol. 3(1). – P. 3. DOI: 10.1038/s41746-019-0207-9. 11. Lee W., Jung W., Lee Y. Atrial fibrillation detection using smartphone and entropy measures // Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). – 2012. – P. 1177–1180. 12. Charlton P.H., Bonnici T., Tarassenko L. et al. An integrative review of the photoplethysmogram: analysis and applications // Physiological Measurement. – 2022. – Vol. 43. – P. 05TR01. DOI: 10.1088/1361-6579/ac5f3d. 13. Li Q., Clifford G.D. Signal quality and data fusion for photoplethysmogram analysis using dynamic time warping // Physiological Measurement. – 2012. – Vol. 33(9). – P. 1491–1501. DOI: 10.1088/0967-3334/33/9/1491. 14. Хвостівська Л. В. Математична модель та методи аналізу пульсового сигналу для підвищення інформативності фотоплетизмографічних систем: дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 01.05.02 / Лілія Володимирівна Хвостівська. — Тернопіль: ТНТУ, 2021. — 177 с. 15. Talukdar D., Alam M., Rahman M. Evaluation of artificial intelligence methods for atrial fibrillation detection in short-term PPG signals // Computers in Biology and Medicine. – 2023. – Vol. 161. – P. 106987. DOI: 10.1016/j.compbiomed.2023.106987. 16. Aschbacher K., Avery E., Hauser M. et al. Deep learning detection of atrial fibrillation using raw photoplethysmography signals // Heart Rhythm O2. – 2020. – Vol. 1(3). – P. 187–196. DOI: 10.1016/j.hroo.2020.05.005. 17. Yousefi R., Hamilton A., Nault I. Artificial neural network approach for PPG-based AF detection // Proceedings of the Computing in Cardiology Conference. – 2019. – P. 1–4.
Content type:	Conference Abstract
Aparece nas Coleccións	Наукові публікації працівників кафедри біотехнічних систем

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