Detection of atrial fibrillation from variable-duration ECG signal based on time-adaptive densely network and feature enhancement strategy
Yükleniyor...
Dosyalar
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE-Institute Electrical Electronics Engineers Inc
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Atrial fibrillation (AF) is one of the clinic's most common arrhythmias with high morbidity and mortality. Developing an intelligent auxiliary diagnostic model of AF based on a body surface electrocardiogram (ECG) is necessary. Convolutional neural network (CNN) is one of the most commonly used models for AF recognition. However, typical CNN is not compatible with variable-duration ECG, so it is hard to demonstrate its universality and generalization in practical applications. Hence, this paper proposes a novel Time-adaptive densely network named MP-DLNet-F. The MP-DLNet module solves the problem of incompatibility between variable-duration ECG and 1D-CNN. In addition, the feature enhancement module and data imbalance processing module are respectively used to enhance the perception of temporal-quality information and decrease the sensitivity to data imbalance. The experimental results indicate that the proposed MP-DLNet-F achieved 87.98% classification accuracy, and F1-score of 0.847 on the CinC2017 database for 10-second cropped/padded single-lead ECG fragments. Furthermore, we deploy transfer learning techniques to test heterogeneous datasets, and in the CPSC2018 12-lead dataset, the method improved the average accuracy and F1-score by 21.81% and 16.14%, respectively. Experimental results indicate that our method can update the constructed model's parameters and precisely forecast AF with different duration distributions and lead distributions. Combining these advantages, MP-DLNet-F can exemplify all kinds of varied-duration or imbalance medical signal processing problems such as Electroencephalogram (EEG) and Photoplethysmography (PPG).
Açıklama
This work was supported in part by the National Natural Science Foundation of China under Grant 61873349, in part by the General Logistics Department of PLA under Grant BLB19J005, and in part by Guangzhou Science and Technology Planning Project under Grant 202003000040.
Anahtar Kelimeler
Electrocardiography, Feature Extraction, Convolutional Neural Networks, Arrhythmia Detection, Robust-Detection, Classification
Kaynak
IEEE Journal of Biomedical and Health Informatics
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
27
Sayı
2
Künye
Zhang, X., Jiang, M., Polat, K., Alhudhaif, A., Hemanth, J., & Wu, W. (2022). Detection of Atrial Fibrillation from Variable-Duration ECG Signal Based on Time-Adaptive Densely Network and Feature Enhancement Strategy. IEEE Journal of Biomedical and Health Informatics, 27(2), 944-955.
