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Identifying electrical signatures preceding a ventricular arrhythmia from the implantable cardioverter-defibrillators (ICDs) can help predict an upcoming ICD shock. To achieve this, we first deployed a large-scale study (N=326) to continuously monitor the electrogram (EGM) data from the ICDs and select the EGM segments prior to a shock event and under the normal condition. Next, we design a novel cohesive framework that integrates metric learning, prototype learning, and few-shot learning, enabling learning from an imbalanced dataset. We implement metric learning by leveraging a Siamese neural network architecture, which incorporates LSTM units. We innovatively utilize triplet and pair losses in a sequential manner throughout the training process on EGM samples. This approach generates embeddings that significantly enhance the distinction of EGM signals under different conditions. In the inference stage …