Survey of Machine Learning Techniques To Predict Heartbeat Arrhythmias

2022/08/22 by Samuel Armstrong, Armstrong, Samuel
Medicine · #ECG Monitoring and Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2208.10463

openalex publication_date 2022/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Many works in biomedical computer science research use machine learning techniques to give accurate results. However, these techniques may not be feasible for real-time analysis of data pulled from live hospital feeds. In this project, different machine learning techniques are compared from various sources to find one that provides not only high accuracy but also low latency and memory overhead to be used in real-world health care systems.

Related

• Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms
• Feature Extraction and Automated Classification of Heartbeats by Machine Learning
• Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG Data
• Early prediction of Cardiac Arrhythmia based on Active Fuzzy Deep Learning
• Machine Learning-based Efficient Ventricular Tachycardia Detection Model of ECG Signal