  Clinical decision support systems (CDSSs) have been widely utilized to
support the decisions made by cardiologists when detecting and classifying
arrhythmia from electrocardiograms (ECGs). However, forming a CDSS for the
arrhythmia classification task is challenging due to the varying lengths of
arrhythmias. Although the onset time of arrhythmia varies, previously developed
methods have not considered such conditions. Thus, we propose a framework that
consists of (i) local temporal information extraction, (ii) global pattern
extraction, and (iii) local-global information fusion with attention to perform
arrhythmia detection and classification with a constrained input length. The
10-class and 4-class performances of our approach were assessed by detecting
the onset and offset of arrhythmia as an episode and the duration of arrhythmia
based on the MIT-BIH arrhythmia database (MITDB) and MIT-BIH atrial
fibrillation database (AFDB), respectively. The results were statistically
superior to those achieved by the comparison models. To check the
generalization ability of the proposed method, an AFDB-trained model was tested
on the MITDB, and superior performance was attained compared with that of a
state-of-the-art model. The proposed method can capture local-global
information and dynamics without incurring information losses. Therefore,
arrhythmias can be recognized more accurately, and their occurrence times can
be calculated; thus, the clinical field can create more accurate treatment
plans by using the proposed method.
