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A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets

Arrhythmias can pose a significant threat to cardiac health, potentially leading to serious consequences such as stroke, heart failure, cardiac arrest, shock, and sudden death. In computer-aided electrocardiogram interpretation systems, the inclusion of certain classes of arrhythmias, which we term...

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Detalles Bibliográficos
Autores principales: Hu, Lianting, Huang, Shuai, Liu, Huazhang, Du, Yunmei, Zhao, Junfei, Peng, Xiaoting, Li, Dantong, Chen, Xuanhui, Yang, Huan, Kong, Lingcong, Tang, Jiajie, Li, Xin, Liang, Heng, Liang, Huiying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10499877/
https://www.ncbi.nlm.nih.gov/pubmed/37720326
http://dx.doi.org/10.1016/j.patter.2023.100795
Descripción
Sumario:Arrhythmias can pose a significant threat to cardiac health, potentially leading to serious consequences such as stroke, heart failure, cardiac arrest, shock, and sudden death. In computer-aided electrocardiogram interpretation systems, the inclusion of certain classes of arrhythmias, which we term “aggressive” or “bullying,” can lead to the underdiagnosis of other “vulnerable” classes. To address this issue, a method for arrhythmia diagnosis is proposed in this study. This method combines morphological-characteristic-based waveform clustering with Bayesian theory, drawing inspiration from the diagnostic reasoning of experienced cardiologists. The proposed method achieved optimal performance in macro-recall and macro-precision through hyperparameter optimization, including spliced heartbeats and clusters. In addition, with increasing bullying by aggressive arrhythmias, our model obtained the highest average recall and the lowest average drop in recall on the nine vulnerable arrhythmias. Furthermore, the maximum cluster characteristics were found to be consistent with established arrhythmia diagnostic criteria, lending interpretability to the proposed method.