Browser-Based AI Pipeline Processes ECG Signals Locally Without Sending Data to Servers

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A technical guide published on DEV Community outlines how to build a real-time ECG denoising and classification system that runs entirely within a user's web browser. The approach uses WebLLM and WebGPU to perform AI inference locally, eliminating the need to transmit sensitive patient bio-signal data to external servers. Developers can leverage TypeScript, WASM preprocessing, and Transformer-based models compiled into WebGPU shaders via the TVM stack to process high-frequency ECG signals at up to 60 frames per second. The pipeline addresses both privacy regulations and latency concerns that typically accompany cloud-based healthcare data processing. Compatible with Chrome 113 and later, the system represents a broader shift toward edge AI in digital health applications.

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