The field of electrocardiogram (ECG) analysis is rapidly evolving, with a focus on developing scalable, interpretable, and generalizable solutions for automated ECG analysis. Recent research has highlighted the importance of addressing challenges such as noise, class imbalance, and dataset heterogeneity in ECG analysis. To this end, innovative approaches such as dual-stage denoising, graph attention networks, and time series transformers are being explored. Additionally, the integration of large language models (LLMs) with traditional algorithmic approaches is showing promise in improving cause-of-death prediction and ECG analysis. The development of standardized benchmarks and baseline models is also facilitating rigorous comparison and accelerating progress in ECG representation learning. Noteworthy papers in this area include FoundationalECGNet, which achieves state-of-the-art performance in multi-class disease detection, and ECG-aBcDe, which introduces a novel ECG encoding method that enables direct fine-tuning of pre-trained LLMs without architectural modifications. Overall, the field is moving towards more accurate, efficient, and interpretable ECG analysis solutions, with significant implications for healthcare and clinical decision-making.