This revolutionary Neural Network methodology has been applied to both electrical (ECG) and optical (PPG) cardiac signals. MaxYield is completely device agnostic and can be integrated through API, CDK, or SDK directly into your existing workflows & hardware.
MaxYield accepts ECG traces or PPG recordings from any device. Our universal input layer automatically adapts to different sampling rates, amplitudes, and signal characteristics without requiring device-specific calibration.
Our Neural Network understands what the true signal actually looks like, allowing it to “see through” the noise, isolating and labeling the key characteristics. In one pass MaxYield’s multi-stage noise filter explicitly targets:
The key innovation: we map noise without distorting the underlying physiology, preserving the diagnostic information that matters, quantifying it in a data table within moments.
Our foundational datasets require recordings from leading medical institutions, spanning diverse patient populations, recording device types, and environmental conditions. Every annotation is validated by experts in the field, all part of the gold-standard methodology that underpins every release - lead by our Advisory Board
Our advanced Neural Networks are trained to handle high noise levels expected in real-world use cases and still perform at an expert-level. This ensures robust performance across edge cases that traditional, "picture matching", algorithms often struggle with.
Multiple validation approaches verify accuracy against gold-standard annotations. Our machine learning models for predictive analytics undergo rigorous testing across demographic groups, device types, and both clinical & real-world conditions before deployment.
Our patented, expert-validated & accurate beat-detection AI uses dynamic segmentation to locate critical signal features:
MaxYield cleans recordings of any length & quantifies the every heartbeat in the signal. Beat-by-beat data tables include the onsets / offsets of P wave, QRS complex, & T wave, plus time-series intervals between them
Isolates true signal from noise & artifact identifying pulse peaks for every heartbeat. Enable continuous HR & HRV monitoring, even during high-noise environments like exercise, on your existing hardware.
Our neural network adapts to all environments, continuously learning on each signal processed
JSON-based API endpoints for seamless integration into existing healthcare workflows. Comprehensive documentation and sample code accelerate implementation.
Native software development kits (SDK) enable computing scenarios where latency, privacy, or connectivity constraints require local processing power.
Flexible CDK architecture supports both cloud-scale batch processing and real-time edge analysis, optimizing for your specific use case requirements.
