CardioPulse: AI Arrhythmia Early-Warning Platform
How Ajath HealthTech architected a low-latency companion app and edge neural inference engine to detect Atrial Fibrillation and Ventricular Tachycardia in real time across 500,000+ active patients.
Clinical Impact Summary
CardioPulse Telemetry & AIThe Challenge: Bottlenecks in Traditional Cardiac Holter Monitoring
Traditional 14-day Holter patches generated gigabytes of uncompressed biometric data that required manual technician review, delaying life-saving cardiac interventions for patients experiencing intermittent atrial fibrillation.
Critical Latency Delays
Patients had to complete 14 days of recording before mail-in analysis, leaving dangerous ventricular tachycardias undetected in real time.
Battery & Memory Constraints
Continuous Bluetooth streaming of raw 500Hz ECG waveforms drained mobile phone batteries within 6 hours, causing high patient non-compliance.
FDA SaMD Class II Rigor
The AI algorithm required complete traceability, IEC 62304 Level C software architecture, and ISO 14971 medical risk management documentation.
The Technical Solution: Dual-Stage Edge & Cloud Telemetry
We engineered a hybrid architecture that balances extreme battery efficiency with instant clinical escalation.
On-Device CoreML / TFLite Inference
Quantized 1D Convolutional Neural Network (CNN) running locally on iOS and Android to evaluate R-peak intervals, detecting AFib and PVC bursts in real-time with negligible CPU load.
Binary WebSocket Ingestion Cluster
When an arrhythmia episode is detected on the edge, the app opens a Protobuf-compressed WebSocket session, streaming the 30-second pre/post event strip directly to the cardiologist console.
SMART on FHIR InBasket Dispatch
Automated conversion of classified arrhythmia episodes into HL7 FHIR v4 Observation and DiagnosticReport resources, inserting actionable ECG caliper PDF strips into hospital EHR charts.