Block Term Decomposition Analysis in Long Segments of Atrial Fibrillation ECGs | Zendy

Pedro Oliveira | Zendy; Vicente Zarzoso | Zendy

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Block Term Decomposition Analysis in Long Segments of Atrial Fibrillation ECGs

Author(s) -

Pedro Oliveira,

Vicente Zarzoso

Publication year - 2018

Publication title -

anais de xxxvi simpósio brasileiro de telecomunicações e processamento de sinais

Language(s) - English

Resource type - Conference proceedings

DOI - 10.14209/sbrt.2018.182

Subject(s) - atrial fibrillation , cardiology , electrocardiography , matrix decomposition , cardiac arrhythmia , tensor decomposition , medicine , block (permutation group theory) , pattern recognition (psychology) , computer science , artificial intelligence , tensor (intrinsic definition) , mathematics , physics , geometry , quantum mechanics , eigenvalues and eigenvectors , pure mathematics

Responsible for 25% of strokes, atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice. A precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary to better understand this challenging cardiac condition. Recently, the block term decomposition (BTD) has been proposed as a novel tool to extract AA in AF ECG signals noninvasively. However, this tensor factorization technique was performed only in short segments. In this paper, the BTD is assessed in long segments of an AF ECG, varying the observation window size. Experimental results show the performance of BTD in long segments of an AF ECG recording and the analysis of the observation window size. This promising tensor technique is compared to two matrix-based methods.

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