Atrial fibrillation diagnosis algorithm based on improved convolutional neural network.
10.7507/1001-5515.202007039
- Author:
Yu PU
1
;
Junjiang ZHU
1
;
Detao ZHANG
2
;
Tianhong YAN
1
Author Information
1. College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310000, P.R.China.
2. SID Medical Co., Ltd., Shanghai 200030, P.R.China.
- Publication Type:Journal Article
- Keywords:
atrial fibrillation;
convolutional neural network;
false-negative rate;
loss function
- MeSH:
Algorithms;
Atrial Fibrillation/diagnosis*;
Electrocardiography;
Humans;
Neural Networks, Computer;
Stroke
- From:
Journal of Biomedical Engineering
2021;38(4):686-694
- CountryChina
- Language:Chinese
-
Abstract:
Atrial fibrillation (AF) is a common arrhythmia, which can lead to thrombosis and increase the risk of a stroke or even death. In order to meet the need for a low false-negative rate (FNR) of the screening test in clinical application, a convolutional neural network with a low false-negative rate (LFNR-CNN) was proposed. Regularization coefficients were added to the cross-entropy loss function which could make the cost of positive and negative samples different, and the penalty for false negatives could be increased during network training. The inter-patient clinical database of 21 077 patients (CD-21077) collected from the large general hospital was used to verify the effectiveness of the proposed method. For the convolutional neural network (CNN) with the same structure, the improved loss function could reduce the FNR from 2.22% to 0.97% compared with the traditional cross-entropy loss function. The selected regularization coefficient could increase the sensitivity (SE) from 97.78% to 98.35%, and the accuracy (ACC) was 96.62%, which was an increase from 96.49%. The proposed algorithm can reduce the FNR without losing ACC, and reduce the possibility of missed diagnosis to avoid missing the best treatment period. Meanwhile, it provides a universal loss function for the clinical auxiliary diagnosis of other diseases.
