An Effective LSTM Recurrent Network to Detect Arrhythmia on Imbalanced ECG Dataset.
J Healthc Eng
; 2019: 6320651, 2019.
Article
em En
| MEDLINE
| ID: mdl-31737240
ABSTRACT
To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Contexto em Saúde:
6_ODS3_enfermedades_notrasmisibles
Problema de saúde:
6_cardiovascular_diseases
/
6_other_circulatory_diseases
Assunto principal:
Arritmias Cardíacas
/
Diagnóstico por Computador
/
Redes Neurais de Computação
/
Eletrocardiografia
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
J Healthc Eng
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
China