Autoencoder-Based Extrasystole Detection and Modification of RRI Data for Precise Heart Rate Variability Analysis.

Fujiwara, Koichi; Miyatani, Shota; Goda, Asuka; Miyajima, Miho; Sasano, Tetsuo; Kano, Manabu

Fujiwara, Koichi; Miyatani, Shota; Goda, Asuka; Miyajima, Miho; Sasano, Tetsuo; Kano, Manabu.

Afiliación

Fujiwara K; Department of Material Process Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan.
Miyatani S; Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan.
Goda A; Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan.
Miyajima M; Department of Liaison Psychiatry and Palliative Medicine, Tokyo Medical and Dental University, Tokyo 113-8510, Japan.
Sasano T; Department of Liaison Psychiatry and Palliative Medicine, Tokyo Medical and Dental University, Tokyo 113-8510, Japan.
Kano M; Department of Systems Science, Kyoto University, Kyoto 606-8501, Japan.

Sensors (Basel) ; 21(9)2021 May 07.

Article en En | MEDLINE | ID: mdl-34067051

Asunto(s)

Complejos Cardíacos Prematuros; Electrocardiografía; Algoritmos; Complejos Cardíacos Prematuros/diagnóstico; Frecuencia Cardíaca; Humanos; Redes Neurales de la Computación

Palabras clave

RRI data; autoencoder; extrasystole; heart rate variability analysis; machine learning

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Electrocardiografía / Complejos Cardíacos Prematuros Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Japón

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