Identification of patients at risk of cardiac conduction diseases requiring a permanent pacemaker following TAVI procedure: a deep-learning approach on ECG signals.
Marco MamprinJo M. ZelisPim A. L. ToninoSvitlana ZingerPeter H. N. de WithPublished in: ICBET (2022)
Keyphrases
- ecg signals
- deep learning
- cardiac arrhythmias
- critical care
- cardiovascular disease
- risk factors
- acute coronary syndrome
- unsupervised learning
- machine learning
- patient data
- medical diagnosis
- weakly supervised
- mit bih arrhythmia database
- heart rate
- mental models
- patient specific
- clinical trials
- pre operative
- intraoperative
- natural images
- object recognition