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Telehealth and remote monitoring improve healthcare accessibility but also expose sensitive data to security and privacy risks, where traditional authentication methods often prove inadequate. The electrocardiogram (ECG) has emerged as a promising biometric modality due to its inherent liveness detection, universality, and individual uniqueness. This paper reviews the ECG authentication pipeline, including signal acquisition through wearables, pre-processing techniques for noise reduction, feature extraction methods (fiducial, non-fiducial, and deep learning), and classification models. Publicly available datasets are examined, alongside key performance benchmarks. Persistent challenges-such as intra-subject variability, computational constraints in wearables, and ethical concerns are discussed, with future directions highlighting multimodal systems, privacy-preserving techniques, and standardized validation. ECG authentication demonstrates strong potential to become a secure and practical component of Telehealth systems.