Healthcare artificial intelligence is shifting from centralized archives toward distributed pipelines that combine wearables, edge processing, cloud analytics, and multi-institutional learning. In this regime, electrocardiogram (ECG) streams cross multiple trust boundaries, so privacy cannot be reduced to a single transmission control. Existing privacy-enhancing technologies typically secure isolated stages and leave gaps between acquisition, analysis, and institutional collaboration. This doctoral research develops a unified lifecycle framework for end-to-end privacy-preserving ECG AI. Rather than introducing another privacy mechanism, it orchestrates complementary privacy-enhancing technologies across the clinical ECG lifecycle, allocating each mechanism to the trust boundary where its security, computational, and operational properties are most appropriate. Three interoperable modules instantiate the framework: AES with CKKS-based homomorphic statistics for secure monitoring; adaptive chaotic protection for low-latency edge transmission; and federated learning with homomorphic aggregation under heterogeneous partitions. Across prototype monitoring, signal-security analysis, and simulated collaboration, the programme shows that privacy layers can be composed without collapsing diagnostic utility, while exposing overhead and fidelity limits that constrain clinical translation.

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