Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods.

Kim, Yehyun; Lee, Myeonggyu; Yoon, Jaeung; Kim, Yeji; Min, Hyunseok; Cho, Hyungjoo; Park, Junbeom; Shin, Taeyoung

Kim, Yehyun; Lee, Myeonggyu; Yoon, Jaeung; Kim, Yeji; Min, Hyunseok; Cho, Hyungjoo; Park, Junbeom; Shin, Taeyoung.

Afiliação

Kim Y; Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea.
Lee M; Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea.
Yoon J; Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea.
Kim Y; Department of Cardiology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea.
Min H; Tomocube Inc., Daejeon 34141, Republic of Korea.
Cho H; Tomocube Inc., Daejeon 34141, Republic of Korea.
Park J; Synergy A.I. Co., Ltd., Seoul 07573, Republic of Korea.
Shin T; Department of Cardiology, Ewha Womans University Mokdong Hospital, Seoul 07985, Republic of Korea.

Diagnostics (Basel) ; 13(17)2023 Sep 03.

Article em En | MEDLINE | ID: mdl-37685387

Palavras-chave

arrhythmia risk prediction; artificial intelligence; atrial fibrillation risk prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagnostics (Basel) Ano de publicação: 2023 Tipo de documento: Article

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