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.

翻译：临床决策支持系统已被广泛应用于协助心脏病专家从心电图中检测和分类心律失常。然而，由于心律失常持续时间长短不一，构建用于心律失常分类任务的临床决策支持系统颇具挑战性。尽管心律失常的发作时间各异，但以往开发的方法并未考虑此类情况。因此，我们提出一个包含以下三个环节的框架：（i）局部时序信息提取、（ii）全局模式提取，以及（iii）带注意力的局部-全局信息融合，以在受限输入长度下执行心律失常检测与分类。通过检测心律失常作为发作片段的起止时间以及基于MIT-BIH心律失常数据库和MIT-BIH心房颤动数据库的心律失常持续时间，分别评估了本方法的10类及4类分类性能。统计结果显示，本方法的效果显著优于对比模型。为检验所提方法的泛化能力，将基于心房颤动数据库训练的模型用于心律失常数据库测试，与当前最优模型相比取得了更优性能。本方法能够在不造成信息损失的前提下捕获局部-全局信息及动态特征。因此，心律失常可被更准确地识别，其发生时间亦可被计算；通过应用本方法，临床领域可制定更精准的治疗方案。