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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