Advance Prediction of Ventricular Tachyarrhythmias using Patient Metadata and Multi-Task Networks

2018/11/30 by Marek Rei, Rei, Marek, Joshua Oppenheimer +4
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Medicine · Neuroscience · #Cardiac electrophysiology and arrhythmias #Cardiology #Computer science #ECG Monitoring and Analysis #EEG and Brain-Computer Interfaces #Engineering #FOS: Biological sciences #FOS: Computer and information sciences #Internal medicine #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medicine #Metadata #Quantitative Methods (q-bio.QM) #Task (project management) #World Wide Web #cs.LG #q-bio.QM #stat.ML

paper · pdf · doi:10.48550/arxiv.1811.12938

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018

arxiv created 2018/11/30 · openalex publication_date 2018/11/30 · arxiv updated 2018/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We describe a novel neural network architecture for the prediction of ventricular tachyarrhythmias. The model receives input features that capture the change in RR intervals and ectopic beats, along with features based on heart rate variability and frequency analysis. Patient age is also included as a trainable embedding, while the whole network is optimized with multi-task objectives. Each of these modifications provides a consistent improvement to the model performance, achieving 74.02% prediction accuracy and 77.22% specificity 60 seconds in advance of the episode.

Citations

• ADADELTA: An Adaptive Learning Rate Method
• Incidence of treated cardiac arrest in hospitalized patients in the United States*
• Trends in Survival after In-Hospital Cardiac Arrest
• PhysioBank, PhysioToolkit, and PhysioNet
• Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

Related

• Survey of Machine Learning Techniques To Predict Heartbeat Arrhythmias
• Machine Learning-based Efficient Ventricular Tachycardia Detection Model of ECG Signal
• Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks
• Early prediction of Cardiac Arrhythmia based on Active Fuzzy Deep Learning
• Deep neural heart rate variability analysis