A new approach for arrhythmia classification using deep coded features and LSTM networks.
Comput Methods Programs Biomed
; 176: 121-133, 2019 Jul.
Article
em En
| MEDLINE
| ID: mdl-31200900
ABSTRACT
BACKGROUND AND OBJECTIVE:
For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.METHODS:
A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.RESULTS:
Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.CONCLUSIONS:
One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Arritmias Cardíacas
/
Processamento de Sinais Assistido por Computador
/
Redes Neurais de Computação
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Compressão de Dados
/
Eletrocardiografia
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Comput Methods Programs Biomed
Assunto da revista:
INFORMATICA MEDICA
Ano de publicação:
2019
Tipo de documento:
Article