Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features

BACKGROUND: Subtle, prognostically important ECG features may not be apparent to physicians. In the course of supervised machine learning, thousands of ECG features are identified. These are not limited to conventional ECG parameters and morphology. We aimed to investigate whether neural network–der...

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Detalhes bibliográficos
Publicado no:Circ Cardiovasc Qual Outcomes
Main Authors: Sau, Arunashis, Ribeiro, Antônio H., McGurk, Kathryn A., Pastika, Libor, Bajaj, Nikesh, Gurnani, Mehak, Sieliwonczyk, Ewa, Patlatzoglou, Konstantinos, Ardissino, Maddalena, Chen, Jun Yu, Wu, Huiyi, Shi, Xili, Hnatkova, Katerina, Zheng, Sean L., Britton, Annie, Shipley, Martin, Andršová, Irena, Novotný, Tomáš, Sabino, Ester C., Giatti, Luana, Barreto, Sandhi M., Waks, Jonathan W., Kramer, Daniel B., Mandic, Danilo, Peters, Nicholas S., O’Regan, Declan P., Malik, Marek, Ware, James S., Ribeiro, Antonio Luiz P., Ng, Fu Siong
Formato: Artigo
Idioma:Inglês
Publicado em: Lippincott Williams & Wilkins 2024
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Original Articles
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7616866/
https://ncbi.nlm.nih.gov/pubmed/39540287
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1161/CIRCOUTCOMES.123.010602
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