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Evaluated on both public (NIFEA) and private (FHRA) datasets, Fetalet achieves state-of-the-art performance with 0.98 F1-score and 0.96 accuracy while demonstrating robustness to clinical noise conditions, maintaining less than 3% performance degradation under Gaussian noise levels up to 0.2. Notably, on the FHRA dataset, Fetalet demonstrates a 19.7% F1-score improvement over Anomaly Transformer with significantly faster training time (3.6s versus 100\u00a0s). The framework\u2019s interpretability features enable clinicians to validate model decisions through shapelet matching with established clinical patterns, enhancing trust in AI-assisted monitoring systems. 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