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Ventricular arrhythmias (VA) are the main causes of sudden cardiac death.Developing machine learning methods for detecting VA based onelectrocardiograms (ECGs) can help save people's lives. However, developingsuch machine learning models for ECGs is challenging because of the following:1) group-level diversity from different subjects and 2) individual-leveldiversity from different moments of a single subject. In this study, we aim tosolve these problems in the pre-training and fine-tuning stages. For thepre-training stage, we propose a novel model agnostic meta-learning (MAML) withcurriculum learning (CL) method to solve group-level diversity. MAML isexpected to better transfer the knowledge from a large dataset and use only afew recordings to quickly adapt the model to a new person. CL is supposed tofurther improve MAML by meta-learning from easy to difficult tasks. For thefine-tuning stage, we propose improved pre-fine-tuning to solveindividual-level diversity. We conduct experiments using a combination of threepublicly available ECG datasets. The results show that our method outperformsthe compared methods in terms of all evaluation metrics. Ablation studies showthat MAML and CL could help perform more evenly, and pre-fine-tuning couldbetter fit the model to training data.

MetaVA: Curriculum Meta-learning and Pre-fine-tuning of Deep Neural Networks for Detecting Ventricular Arrhythmias based on ECGs