Atrial fibrillation (AF) is the most common arrhythmia in the heart. Two main types of AF are defined as paroxysmal and persistent. In this paper, we present a method to discriminate between the characteristics of paroxysmal and persistent using tensor decompositions of a multi-channel electrocardiogram (ECG) signal. For this purpose, ECG signals are segmented by applying a Hilbert transform on the thresholded signal. Dynamic time warping is used to align the separated segments of each channel and then a tensor is constructed with three dimensions as time, heartbeats and channels. A Canonical polyadic decomposition with rank 2 is computed from this tensor and the resulting loading vectors describe the characteristics of paroxysmal and persistent AF in these three dimensions. The time loading vector reveals the pattern of a single P wave or abnormal AF patterns. The heartbeat loading vector shows whether the pattern is present or absent in a specific beat. The results can be used to distinguish between the patterns in paroxysmal AF and persistent AF.
|Title of host publication||28th European Signal Processing Conference (EUSIPCO 2020)|
|Place of Publication||Amsterdam (Netherlands)|
|Number of pages||5|
|Publication status||Published - 1 Aug 2020|
|Event||EUSIPCO 2020: The 28th European Signal Processing Conference - Amsterdam, Netherlands|
Duration: 18 Jan 2021 → 22 Jan 2021
Conference number: 28th
|Period||18/01/21 → 22/01/21|
|Other||Date change due to COVID-19 (former date August 24-28 2020)|
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