Surgical treatment for obstructive sleep apnea prevents arrhythmic events: a nationwide population-based study

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Surgical treatment for obstructive sleep apnea prevents arrhythmic events: a nationwide population-based study

Session: eCardiology

Topic: Big Data and Digital Twin

Speaker: Associate Professor L. Jung (Jeonju, KR)

Event: EHRA 2024

Congress Presentation

About the speaker

Associate Professor Laeyoung Jung

Jeonbuk National University Hospital, Jeonju (Korea (Republic of))

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6 more presentations in this session

Polygenic risk score for Atrial Fibrillation as a predictor for the incidence of Sick Sinus Syndrome: A comprehensive analysis from the UK biobank.

Speaker: Doctor H. Kim (Daegu, KR)

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Abstract

Prediction of Atrial Fibrillation with Single-Lead Mobile ECG during Normal Sinus Rhythm using Deep Learning

Speaker: Associate Professor K. Lee (Gwangju, KR)

Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin

Speaker: Professor D. Linz (Maastricht, NL)

Association between amount of biventricular pacing and heart failure status measured by a multisensor implantable defibrillator algorithm.

Speaker: Doctor L. Santini (Rome, IT)

Development and validation of a machine learning-based prediction model for sudden cardiac death in the general population: insights from the community cardiovascular cohort

Speaker: Assistant Professor Y. Chen (Taichung, TW)

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eCardiology

Speakers: Associate Professor L. Jung, Doctor H. Kim, Associate Professor K. Lee, Professor D. Linz, Doctor L. Santini...

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About the event

EHRA 2024

7 April - 9 April 2024

Sessions Presentations

Congress

Surgical treatment for obstructive sleep apnea prevents arrhythmic events: a nationwide population-based study

About the speaker

Associate Professor Laeyoung Jung

6 more presentations in this session

Polygenic risk score for Atrial Fibrillation as a predictor for the incidence of Sick Sinus Syndrome: A comprehensive analysis from the UK biobank.

Prediction of Atrial Fibrillation with Single-Lead Mobile ECG during Normal Sinus Rhythm using Deep Learning

Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin

Association between amount of biventricular pacing and heart failure status measured by a multisensor implantable defibrillator algorithm.

Development and validation of a machine learning-based prediction model for sudden cardiac death in the general population: insights from the community cardiovascular cohort

Access the full session

eCardiology

About the event

EHRA 2024

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