Use Cases
- Real-time compression of multi-sensor biomedical streams
- Low memory usage suitable for battery-powered devices
- 20-50 MB/s throughput on embedded cores
- High-fidelity signal preservation for research
- Compatibility with multi-modal datasets
- Reduced storage footprint for long-term studies
- Significantly faster cloud and edge transmission
- High-frequency embedded sensor device support
- Clinical integrity maintained throughout
Features
Purpose-Built for Medical Data: Optimized around the structure and temporal patterns of physiological signals, delivering significantly better compression ratios than generic algorithms
Lossless & Clinically Safe: Every sample, spike, and waveform is preserved exactly, ensuring signal quality suitable for research, clinical use, and regulatory-sensitive workflows
Designed for Real-World Deployment: Integrates effortlessly into cloud pipelines, edge gateways, and high-frequency embedded sensor devices
High Throughput on Embedded Cores: 20-50 MB/s throughput with real-time compression of multi-sensor biomedical streams and low memory usage suitable for battery-powered devices
Embedded-Friendly Performance: Engineered to operate efficiently on ESP32-class devices, ARM Cortex-M series (Cortex-M0/M3/M4/M7), and low-power wearable and IoT platforms
Benchmark Performance
MedCodec achieves the highest compression ratios across BVP, Heart rate, IBI, and EEG datasets, with the most significant performance lead in Heart rate data (6.86x vs 5.07x for XZ).
Benefits
Deployment Options
Deploy on ESP32-class devices for real-time biomedical signal compression with minimal power consumption.
Run on ARM Cortex-M series (Cortex-M0/M3/M4/M7) for low-power wearable and IoT platforms.
Integrate into cloud pipelines and edge gateways for high-frequency embedded sensor device support.