Classification of ECG using ensemble of residual CNNs with or without attention mechanism.

Nejedly, Petr; Ivora, Adam; Viscor, Ivo; Koscova, Zuzana; Smisek, Radovan; Jurak, Pavel; Plesinger, Filip

Nejedly, Petr; Ivora, Adam; Viscor, Ivo; Koscova, Zuzana; Smisek, Radovan; Jurak, Pavel; Plesinger, Filip.

Afiliación

Nejedly P; Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Ivora A; Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Viscor I; Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Koscova Z; Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Smisek R; Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Jurak P; Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.
Plesinger F; Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic.

Physiol Meas ; 43(4)2022 04 28.

Article en En | MEDLINE | ID: mdl-35381586

Asunto(s)

Algoritmos; Electrocardiografía; Electrocardiografía/métodos; Entropía; Probabilidad

Palabras clave

ECG; PhysioNet challenge 2021; attention mechanism; classification; deep learning

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Electrocardiografía Tipo de estudio: Prognostic_studies Idioma: En Revista: Physiol Meas Asunto de la revista: BIOFISICA / ENGENHARIA BIOMEDICA / FISIOLOGIA Año: 2022 Tipo del documento: Article País de afiliación: República Checa Pais de publicación: Reino Unido

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google