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Development of Rhythm-based and Morphology-based Algorithm for Atrial Fibrillation Detection From Single Lead ECG Signal

机译:基于心律和形态学的单导联心电信号心房颤动检测算法的开发

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Atrial Fibrillation or AF is the most common atrial arrhythmia and often occurs especially in the elderly people. Since AF is associated with an increased risk for stroke, regular AF monitoring is recommended for the elderly as well as the patients to reduce the incidence of stroke. In this study, the subsequent two methods of AF detection have been. The first one is a low complexity algorithm with rhythm-based for embedding into portable ECG devices. The second one is morphology-based method using a transferred deep learning with fine-tuning. The performances of the low complexity algorithm are 100% of sensitivity and 86.67% of specificity, respectively while the performances of the deep learning algorithm are 96.97% of sensitivity and 100% of specificity, respectively. From these results, the combination between rhythm-based and morphology-based algorithm can offer the highest sensitivity for screening of AF and highest specificity for separating Normal Sinus Rhythm (NSR) from AF. These developed methods will be useful for improving the performance of AF detection utilized in healthcare system.
机译:房颤或房颤是最常见的房性心律失常,尤其是在老年人中。由于房颤会增加中风风险,因此建议老年人和患者定期进行房颤监测,以减少中风的发生率。在这项研究中,AF检测的后续两种方法已经出现。第一个是基于节奏的低复杂度算法,可嵌入到便携式ECG设备中。第二种是基于形态学的方法,该方法使用带有微调的转移深度学习。低复杂度算法的性能分别为灵敏度的100%和特异性的86.67%,而深度学习算法的性能分别为灵敏度的96.97%和特异性的100%。从这些结果来看,基于节奏的算法和基于形态学的算法的结合可以为AF筛查提供最高的灵敏度,并为从AF分离正常窦性心律(NSR)提供最高的特异性。这些开发的方法将对改善医疗保健系统中使用的AF检测的性能很有用。

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