Early Diagnosis of Atrial Fibrillation Episodes: Comparative Analysis of Different Matrix Architectures
Source abstract
Abstract
This study presents three different matrix architectures for the analysis of ECG parameters, aimed at detecting atrial fibrillation episodes. The evaluation involves a cohort of 15 individuals, utilizing these matrix architectures across various orders. The findings reveal that the matrix norm delivers significantly better results compared to the large discriminant of the matrix. Detailed analysis of the spatial expansion of each matrix structure indicates that the PMLD architecture excels in terms of expandability compared to the MA1 and MA2 matrices. Consequently, third- and fifth-order PMLD matrix architectures are employed for classification techniques, demonstrating enhanced sensitivity with increased matrix order. These results are validated through the classification of several test candidates, confirming the efficacy of the proposed method. The study suggests that the developed approach holds substantial potential for clinical diagnostics in the early detection of atrial fibrillation.
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Research context
OpenAlex topics: Atrial Fibrillation Management and Outcomes · Cardiac Arrhythmias and Treatments · Cardiac electrophysiology and arrhythmias
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mathematical Modelling, Kaunas University of Technology, Studentu g. 50, 51368 Kaunas, Lithuania
Institute of Cardiology, Lithuanian University of Health Sciences, Sukileliu str. 15, 50162 Kaunas, Lithuania
References (34)
Show source-provided references
1. Raising awareness and early detection of atrial fibrillation, an experience resorting to mobile technology centred on informed individuals10.1016/j.sapharm.2019.08.036 ↗
2. Atrial fibrillation and risks of cardiovascular disease, renal disease, and death: Systematic review and meta-analysis10.1136/bmj.i4482 ↗
3. Asymptomatic atrial fibrillation10.1016/j.pcad.2005.06.005 ↗
4. Clinical relevance of silent atrial fibrillation: Prevalence, prognosis, quality of life, and management10.1023/A:1009823001707 ↗
5. Usefulness of continuous electrocardiographic monitoring for atrial fibrillation10.1016/j.amjcard.2012.03.021 ↗
6. Guidelines for the management of atrial fibrillation: The Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC)10.1093/eurheartj/ehq278 ↗
7. 2011 ACCF/AHA/HRS focused update on the management of patients with atrial fibrillation (updating the 2006 guideline) a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines10.1161/CIR.0b013e3181fa3cf4 ↗
8. 101 Clinical profiles of hospitalized patients diagnosed with af compared to those diagonsed in an ambulatory setting: Analysis from the jordan atrial fibrilliation (jofib) study
9. Venero, J.E.V., and Benites-Zapata, V.A. (2021). Hyperuricemia as a Associated Factor to Atrial Fibrillation in Type 2 Diabetes Mellitus Patients an a Private Clinic in Peru 2020. Metab.-Clin. Exp., 116.10.1016/j.metabol.2020.154623 ↗
10. Risk factors for atrial fibrillation progression10.1016/j.ccep.2020.10.011 ↗
11. Short-term outcomes of apixaban versus warfarin in patients with atrial fibrillation: Is body weight an important consideration?
12. Clinical outcomes and mortality in patients with atrial fibrillation and recently diagnosed lung cancer in oncology outpatient settings
13. Diastolic dysfunction and atrial fibrillation in coronary heart disease surgery: A literature review10.1016/j.ijso.2023.100615 ↗
14. Shah, N.A., Shah, S., Rijal, A., Chaudhary, A., Chand, S., Pandey, S., Rawal, L., Parajuli, S., Khanal, R., and Poudel, C.M. (2022). Anterior wall STEMI in a patient with paroxysmal atrial fibrillation due to coronary embolism: A case report. Ann. Med. Surg., 82.10.1016/j.amsu.2022.104602 ↗
15. The signal-averaged P wave duration: A rapid and noninvasive marker of risk of atrial fibrillation10.1016/0735-1097(93)90381-A ↗
16. Detection and delineation of P and T waves in 12-lead electrocardiograms10.1111/j.1468-0394.2008.00486.x ↗
17. Qammar, N.W., Orinaitė, U., Šiaučiūnaitė, V., Vainoras, A., Šakalytė, G., and Ragulskis, M. (2022). The Complexity of the Arterial Blood Pressure Regulation during the Stress Test. Diagnostics, 12.10.3390/diagnostics12051256 ↗
18. Ziaukas, P., Alabdulgader, A., Vainoras, A., Navickas, Z., and Ragulskis, M. (2017). New approach for visualization of relationships between RR and JT intervals. PLoS ONE, 12.10.1371/journal.pone.0174279 ↗
19. Erelund, S., Karp, K., Wiklund, U., Hörnsten, R., and Arvidsson, S. (2021). Are ECG changes in heart-healthy individuals of various ages related to cardiac disease 20 years later?. Upsala J. Med. Sci., 126.10.48101/ujms.v126.6064 ↗
20. Qammar, N.W., Šiaučiūnaitė, V., Zabiela, V., Vainoras, A., and Ragulskis, M. (2022). Detection of atrial fibrillation episodes based on 3D algebraic relationships between cardiac intervals. Diagnostics, 12.10.3390/diagnostics12122919 ↗
21. Prediction of paroxysmal Atrial Fibrillation: A machine learning based approach using combined feature vector and mixture of expert classification on HRV signal10.1016/j.cmpb.2018.07.014 ↗
22. Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review10.1016/j.ins.2018.07.063 ↗
23. A deep learning approach for real-time detection of atrial fibrillation10.1016/j.eswa.2018.08.011 ↗
24. Sleep apnoea detection from ECG using features extracted from reconstructed phase space and frequency domain10.1016/j.bspc.2013.05.007 ↗
25. Casaleggio, A., Braiotta, S., and Corana, A. (1995, January 10–13). Study of the Lyapunov exponents of ECG signals from MIT-BIH database. Proceedings of the Computers in Cardiology, Vienna, Austria.
26. Detecting variabilities of ECG signals by Lyapunov exponents10.1007/s00521-008-0229-8 ↗
27. Correlation dimension estimation from electrocardiograms10.1016/0960-0779(93)E0053-E ↗
28. Heart rate variability analysis using correlation dimension and detrended fluctuation analysis10.1016/S1297-9562(02)90002-1 ↗
29. Applying nonlinear dynamics to ECG signal processing10.1109/51.664037 ↗
30. Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data10.1016/S0010-4825(01)00031-2 ↗
31. Early detection of sudden cardiac death using Poincaré plots and recurrence plot-based features from HRV signals10.3906/elk-1509-149 ↗
32. Method for prediction of acute hypotensive episodes10.5755/j01.eee.22.1.13453 ↗
33. Šiaučiūnaitė, V., Ragulskis, M., Vainoras, A., Dabiri, B., and Kaniusas, E. (2021). Visualization of complex processes in cardiovascular system during electrical auricular vagus nerve stimulation. Diagnostics, 11.10.3390/diagnostics11122190 ↗
34. Analysis of dynamical interrelations of electrocardiogram parameters