General Learning Encoder (GLE) powered models for multi-modal biosignal analysis. Rigorously validated using gold-standard methodologies. Production ready for deployment.
Every model undergoes rigorous multi-method validation to ensure real-world reliability
Every sample tested once with maximum training data. Most rigorous for small datasets.
Stratified 5-fold and 10-fold validation ensures consistent performance across data partitions.
Completely unseen test sets reserved from training. No data leakage possible.
Randomized label tests confirm models learn true signal, not artifacts.
Multi-modal biosignal analysis across metabolomics, spectroscopy, and electrophysiology
1,000+ samples validated
Validated Accuracy
Real-time consciousness state classification from brain signals. Independently verified to outperform EEG Foundation Challenge 2025 winners by 13.5x improvement margin. Foundation for mental health, focus tracking, and neurofeedback applications.
300 samples validated
Validated Accuracy
Serum metabolomics screening via LC-MS. Validated on public Metabolomics Workbench dataset (PR002101). Clinical markers include HbA1c, fasting blood glucose, creatinine, and liver enzymes from blood serum.
1,751 samples validated
Validated Accuracy
Saliva-based Raman spectroscopy for early neurodegenerative screening. Subject-level splits ensure no data leakage.
4,200+ samples validated
Validated Accuracy
Real-time Raman-based detection from saliva. No reagents required. Multi-model ensemble provides robust predictions.
15,000+ samples validated
Validated Accuracy
Binary distress classification from voice audio. Speaker-disjoint validation (92.81%) ensures no speaker overlap between train and test sets — the gold standard for voice models. Production-deployed for real-time crisis intervention.
2,693 samples validated
Validated Accuracy
Audio-based breathing pattern classification: normal, deep, shallow, and breath-hold detection. Used for stress monitoring, respiratory health assessment, and Wim Hof method training. Real-time processing with <0.5s latency.
How our validated models compare to existing diagnostic methods
| Application | GLE Platform | Current Standard | Advantage |
|---|---|---|---|
| Consciousness | 97.65%Verified | Clinical EEG: 60-85% | Real-time, consumer hardware |
| T2D Screening | 94.67%LOOCV | HbA1c: 80-85% | Non-invasive, faster results |
| PD/AD Detection | 91.45%Holdout | CSF Biomarkers: 85-90% | No lumbar puncture required |
| Voice Emotion | 93.0%Speaker-Disjoint | Self-report scales: 50-70% | Real-time, no questionnaires |
| Breathing Analysis | 88.97%Holdout | Manual observation: 70-80% | Automated, phone microphone |
| COVID-19 | 86.35%Ensemble | Rapid Antigen: 70-90% | Reusable, no consumables |
Our validation methodology follows the strictest standards required for publication in top-tier medical and machine learning journals.
No samples from the same patient appear in both training and test sets
Shuffle tests confirm models learn true biological signal
Key models validated on publicly available datasets for reproducibility
Each model tested with multiple validation approaches for confidence
We offer verification pathways for research partners, regulatory bodies, and clinical collaborators:
Partner with us to bring peer-review-ready biosignal intelligence to your clinical or commercial application.
Important: These models are validated for research and screening purposes. They are not FDA-cleared medical devices and should not be used as the sole basis for clinical diagnosis. Clinical implementation requires proper regulatory approval and should be supervised by qualified healthcare professionals.