Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study.

Yan, Jianjun; Cai, Xianglei; Chen, Songye; Guo, Rui; Yan, Haixia; Wang, Yiqin

Yan, Jianjun; Cai, Xianglei; Chen, Songye; Guo, Rui; Yan, Haixia; Wang, Yiqin.

Afiliación

Yan J; Institute of Intelligent Perception and Diagnosis, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China.
Cai X; Institute of Intelligent Perception and Diagnosis, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China.
Chen S; Institute of Intelligent Perception and Diagnosis, School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China.
Guo R; Shanghai Key Laboratory of Health Identification and Assessment, Laboratory of Traditional Chinese Medicine for Diagnostic Information, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yan H; Shanghai Key Laboratory of Health Identification and Assessment, Laboratory of Traditional Chinese Medicine for Diagnostic Information, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Wang Y; Shanghai Key Laboratory of Health Identification and Assessment, Laboratory of Traditional Chinese Medicine for Diagnostic Information, Shanghai University of Traditional Chinese Medicine, Shanghai, China.

JMIR Med Inform ; 9(10): e28039, 2021 Oct 21.

Article en En | MEDLINE | ID: mdl-34673537

Palabras clave

deep convolutional neural network; ensemble learning; feature extraction; fully connected neural network; machine learning; pulse analysis; pulse classification; pulse signal; support vector machine; synthetic minority oversampling technique; traditional Chinese medicine; wrist pulse

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: JMIR Med Inform Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Canadá

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