Clinical validation
Clinical Rigour Meets Automation.
Automated sleep scoring shouldn't require compromising on diagnostic confidence. Our deep learning models are continuously validated against expert consensus datasets to ensure human-level precision with zero-friction deployment.
Performance metrics
Validated Performance at Scale
95.39%
Epoch-by-Epoch Agreement
Matching or exceeding the average inter-scorer agreement between two expert human RPSGTs across 5-stage sleep scoring.
0.93
AHI Correlation (r)
Strong linear correlation with consensus sleep physician diagnoses for Apnea-Hypopnea Index tracking.
<5 min
Average Score Time
Complete processing speed from raw polysomnography (PSG) data ingestion to a draft-ready clinical report.
High-fidelity signal analysis
Multi-Channel Physiological Intelligence
Our models interpret raw, multi-channel physiological signals, directly analyzing EEG, ECG, EOG, and EMG channels simultaneously. By training on diverse clinical-grade datasets, the platform successfully maps complex sleep architecture, capturing rapid transitions and micro-arousals that traditional rule-based algorithms frequently miss.
This ensures that your clinical staff receives a baseline draft that aligns seamlessly with gold-standard laboratory scoring rules.
- EEG — Sleep stage classification (N1, N2, N3, REM)
- EOG — Rapid eye movement detection
- EMG — Muscle tone and arousal tracking
- ECG — Cardiac rhythm and apnea correlation
Documentation
White Paper & Compliance Downloads
Institutional evidence and technical documentation available on request.