Real-time arrhythmia detection using convolutional neural networks.

Vu, Thong; Petty, Tyler; Yakut, Kemal; Usman, Muhammad; Xue, Wei; Haas, Francis M; Hirsh, Robert A; Zhao, Xinghui

Vu, Thong; Petty, Tyler; Yakut, Kemal; Usman, Muhammad; Xue, Wei; Haas, Francis M; Hirsh, Robert A; Zhao, Xinghui.

Afiliação

Vu T; School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States.
Petty T; School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States.
Yakut K; Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States.
Usman M; Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States.
Xue W; Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States.
Haas FM; Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States.
Hirsh RA; Department of Anesthesiology, Cooper University Hospital, Camden, NJ, United States.
Zhao X; School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States.

Front Big Data ; 6: 1270756, 2023.

Article em En | MEDLINE | ID: mdl-38058406

Palavras-chave

anomaly detection; big data; convolutional neural networks; machine learning; smart health

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Big Data Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google