Atrial fibrillation diagnosis algorithm based on improved convolutional neural network / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 686-694, 2021.
Artigo
em Chinês
| WPRIM (Pacífico Ocidental)
| ID: wpr-888228
Biblioteca responsável:
WPRO
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.
Texto completo:
Disponível
Contexto em Saúde:
ODS3 - Meta 3.4 Reduzir as mortes prematuras devido doenças não transmissíveis
Problema de saúde:
Doença Cardiovascular
/
Doença Cerebrovascular
Base de dados:
WPRIM (Pacífico Ocidental)
Assunto principal:
Fibrilação Atrial
/
Algoritmos
/
Redes Neurais de Computação
/
Acidente Vascular Cerebral
/
Eletrocardiografia
Tipo de estudo:
Estudo diagnóstico
/
Estudo prognóstico
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2021
Tipo de documento:
Artigo