In the world of digital health, the "Cloud-First" approach is hitting a massive wall: Privacy. Sending raw, sensitive Bio-signal data like Electrocardiograms (ECG) to a central server isn't just a latency nightmare—it's a regulatory minefield.
What if we could run heavy-duty signal processing and AI classification directly in the user's browser? Thanks to WebGPU acceleration and the maturity of Edge AI, we can now achieve zero-latency, zero-backend-cost processing. By leveraging WebLLM and WASM, we are moving the intelligence to where the data lives. In this guide, we’ll explore how to implement a real-time ECG denoising pipeline that ensures raw patient waveforms never leave the device.
The Architecture: Local-First Bio-signal Processing
Processing ECG signals requires high throughput. Traditional JavaScript is too slow for real-time Fourier transforms or deep learning inference on high-frequency (500Hz+) signals. This is where WebGPU comes in, providing a low-level interface to the device's graphics hardware.
Here is the data flow of our browser-based medical AI pipeline:
