AI-Powered Diagnostics Platform for Clinical Signal Processing

A leading medical device manufacturer needed to reduce the time clinical reviewers spent identifying anomalies in ECG and EEG signal data — without compromising diagnostic accuracy or regulatory compliance. Sequoia designed and deployed a production-grade deep learning signal processing platform now running across 12 clinical sites.

The Challenge

Clinical signal data from cardiac and neurological devices was growing faster than the review capacity of clinical teams. Manual review workflows required specialists to examine thousands of signal segments per day, creating a throughput bottleneck that delayed time-to-diagnosis and increased reviewer fatigue.

Existing software tools provided basic filtering but no intelligent triage — every signal required the same level of human review regardless of clinical significance. The result was a system that could not scale with device deployment volumes.

“Every minute of delay in anomaly identification has clinical implications. We needed a system that could prioritise review workloads intelligently — not just store and display signals.”

— VP of Clinical Technology

Our Approach

Sequoia embedded engineers and clinical data specialists alongside the client team, working in three phases:

• Signal classification model — A convolutional neural network trained on 2.4M labelled signal segments, optimised for inference latency below 40ms per segment on edge-deployed hardware.
• Risk stratification pipeline — An automated triage layer that assigns clinical priority scores to incoming signals, routing high-priority anomalies directly to the specialist review queue.
• HIPAA-compliant inference infrastructure — End-to-end encrypted data handling with audit trails, role-based access controls, and on-premises deployment to meet regulatory requirements.

The Outcome

Reviewer workload reduced significantly, with the triage pipeline handling initial classification autonomously. Clinical specialists now focus exclusively on confirmed high-priority signals, improving both throughput and diagnostic quality across all sites.

The platform was designed for expansion — new signal modalities and additional clinical sites can be added without re-engineering the core inference infrastructure.

Published by the Sequoia Team