ESC 365 - Optimising and validating deep learning approaches for diagnosing atrial fibrillation from few-lead ambulatory electrocardiogram signals

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Optimising and validating deep learning approaches for diagnosing atrial fibrillation from few-lead ambulatory electrocardiogram signals

Session: e-Cardiology award session

Topic: Artificial Intelligence (Machine Learning, Deep Learning)

Speaker: Doctor P. Pantelidis (Athens, GR)

Event: EHRA 2022

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About the speaker

Doctor Panteleimon Pantelidis

National & Kapodistrian University of Athens, Athens (Greece)

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4 more presentations in this session

The impact of a structured polygraphy screening incorporated in a novel remote mobile health pathway on sleep apnoea prevalence in patients with atrial fibrillation

Speaker: Miss D. Verhaert (Nijmegen, NL)

Clinical validation of five direct-to-consumer smartwatches to detect atrial fibrillation in a real-world cohort of patients

Speaker: Doctor P. Badertscher (Basel, CH)

Classification of organised atrial arrythmias using explainable artificial intelligence

Speaker: Doctor A. Sau (London, GB)

Artificial intelligence algorithms for the recognition of Brugada type 1 pattern on standard 12-leads ECG

Speaker: Mr F. Vozzi (Pisa, IT)

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e-Cardiology award session

Speakers: Doctor P. Pantelidis, Miss D. Verhaert, Doctor P. Badertscher, Doctor A. Sau, Mr F. Vozzi

About the event

EHRA 2022

3 April - 5 April 2022

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