Wearable electrocardiogram (ECG) recordings pass through multiple networks and systems before automated interpretation, making patient metadata vulnerable to loss. Digital watermarking embeds this metadata directly into the signal. However, conventional ECG watermarking methods fail under temporal desynchronization, and waveform similarity does not guarantee downstream diagnostic preservation. This article presents ECGWmark, a deep watermarking framework for single-lead ECG. A fully convolutional generator embeds the payload without signal quantization, a detector decodes the message frame by frame and verifies watermark presence, and a frozen arrhythmia classifier penalizes changes that would alter diagnostic predictions. To bridge the gap between bit accuracy and whole-message recovery, payloads are protected by extended Hamming codes, validation-selected interleaving, and soft-decision decoding. On the MIT-BIH Arrhythmia Database, ECGWmark maintains over 90\% bit accuracy under time shifts of 10 to 50 samples, where transform baselines fall to chance level. With the coding layer, the protected 16-bit payload achieves 99.86\% exact recovery on clean segments and sustains over 90\% recovery under time shifts, 10\% sample deletion, and 10\% packet loss. Inter-patient evaluation confirms improved ventricular-beat sensitivity with preserved macro-F1. Requiring only 0.56 million parameters and about 15~ms per segment on a desktop CPU, ECGWmark highlights the need to assess physiological watermarking through whole-message recovery and diagnostic consistency.

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