A Novel Application for Real-time Arrhythmia Detection using YOLOv8

In recent years, there has been an increasing need to reduce healthcare costs in remote monitoring of cardiovascular health. Detecting and classifying cardiac arrhythmia is critical to diagnosing patients with cardiac abnormalities. This paper shows that complex systems such as electrocardiograms (ECG) can be applicable for at-home monitoring. This paper proposes a novel application for arrhythmia detection using the state-of-the-art You-Only-Look-Once (YOLO)v8 algorithm to classify single-lead ECG signals. We proposed a loss-modified YOLOv8 model that was fine-tuned on the MIT-BIH arrhythmia dataset to detect to allow real-time continuous monitoring. Results show that our model can detect arrhythmia with an average accuracy of 99.5% and 0.992 mAP@50 with a detection time of 0.002s on an NVIDIA Tesla V100. Our study demonstrated the potential of real-time arrhythmia detection, where the model output can be visually interpreted for at-home users. Furthermore, this study could be extended into a real-time XAI model, deployed in the healthcare industry, and significantly advancing healthcare needs

Genotypic variants at 2q33 and risk of esophageal squamous cell carcinoma in China: A meta-analysis of genome-wide association studies

10.1093/hmg/dds029Human Molecular Genetics2192132-2141HMGE