摘要
Arrhythmias stand out for having irregular cardiac rhythms,and the fast diagnosis of arrhythmias holds significant clinical importance due to its potential to mitigate adverse health outcomes.Despite the progress in this field,existing research efforts have encountered limitations,necessitating innovative approaches to address diagnostic challenges effectively.The primary objective of this research is to propose an innovative classification methodology for distinguishing five distinct arrhythmia classes:atrial premature beat(A),normal(N),ventricular premature beat(V),right bundle branch block(R),and left bundle branch block(L).The proposed methodology involves constructing a hybrid model that incorporates an attention mechanism,utilizing electrocardiogram(ECG)data from an open-source repository.Additionally,we have incorporated an explainability feature into the model,allowing for the interpretation and explanation of its predictions.This model is designed to capitalize on the unique features of arrhythmic patterns and enhance classification metrics.Innovative techniques employed within the methodology are detailed to elucidate the rationale behind their selection and their anticipated contributions to improved model performance.Findings from this study underscore the superiority of the proposed classification model over existing methodologies.Quantitative analysis demonstrates its outstanding performance.The approach,outperforming existing methods,achieves high levels of accuracy(99.16%),specificity(99.79%),recall(99.20%),precision(99.20%),F1-measure(99.16%),and AUC(99.92%).This research advances medical diagnostics by integrating advanced machine-learning techniques to enhance arrhythmia detection.
基金
supported by the National Natural Science Foundation of China under Grant No.62271127
the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China and the West China Hospital of Sichuan University under Grants No.ZYGX2022YGRH011 and No.HXDZ22005
the Natural Science Foundation of Sichuan,China under Grant No.23NSFSC0627
Sichuan Provincial Key Laboratory Fund for Ultra Sound Cardioelectrophysiology and Biomechanics,China under Grant No.2023KFKT01.
