Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs.

Zhao, Z; Murphy, D; Gifford, H; Williams, S; Darlington, A; Relton, S D; Fang, H; Wong, D C

Zhao, Z; Murphy, D; Gifford, H; Williams, S; Darlington, A; Relton, S D; Fang, H; Wong, D C.

Afiliación

Zhao Z; University of Manchester, United Kingdom.
Murphy D; Xi'an Jiaotong University, Xi'an, People's Republic of China.
Gifford H; University of Manchester, United Kingdom.
Williams S; University of Exeter, Exeter, United Kingdom.
Darlington A; University of Leeds, Leeds, United Kingdom.
Relton SD; University of Manchester, United Kingdom.
Fang H; University of Leeds, Leeds, United Kingdom.
Wong DC; Loughborough University, Loughborough, United Kingdom.

Physiol Meas ; 43(3)2022 04 04.

Article en En | MEDLINE | ID: mdl-35255483

Asunto(s)

Enfermedades Cardiovasculares; Cardiopatías; Arritmias Cardíacas/diagnóstico; Electrocardiografía/métodos; Humanos; Redes Neurales de la Computación

Palabras clave

ECG; PhysioNet; deep neural network; electrocardiogram; machine learning; signal processing

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

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