This study successfully combined lightweight models and a metric-based few-shot learning framework; the proposed models trained in limited data have comparable performance against those trained in the extensive dataset. They also outperformed the existing few-shot learning model. To overcome label scarcity, a longstanding issue in ECG analysis, this study refined 2-dimensional lightweight models SEembedNet and LMUEBCNet, which have the best results with the most efficient parameter size, using a metric-based few-shot learning framework. The framework adjusts them into a one-dimensional convolutional neural network as a feature extractor and computes feature dissimilarity between input pairs for classification purposes. The experiments use the MIT-BIH dataset for 10-fold cross-validation and compare the result with the conventional deep learning training method, which is to train the feature extraction CNN directly for a classification task. The results showed that the metric-based few-shot learning approach for SEembedNet yielded higher average macro F1 scores in ectopic heartbeat classification than conventional deep learning methods, with improvements ranging from 11% to 38% for real-world data and 3% to 11% for balanced data. In the case of LMUEBCNet, the improvements ranged from 2.5% to 29.9% for real-world data and 5% to 14% for balanced data. The proposed models performed comparably to existing few-shot learning models under identical test conditions but with significantly reduced parameter sizes. Specifically, the parameters of SEembedNet and LMUEBCNet are reduced by 955 and 821 times. This study underlines the potential of lightweight metric-based few-shot learning models for automated ectopic heartbeat classification.