The D-BAS™ platform is built on decades of peer-reviewed science. This library maps every core technology claim to verifiable publications — from PubMed, Nature, IEEE, Lancet, JAMA, Cell, and NEJM. No claim stands without a citation.
20 key publications spanning biosignal hardware, AI signal processing, Digital Twin medicine, and closed-loop feedback — each mapped to a specific D-BAS™ platform claim.
Multi-site validation (n=412) showing wearable multi-modal sensor fusion achieves Bland-Altman agreement within clinical acceptance limits for 11 of 13 vital biomarkers — demonstrating viable ambulatory clinical-grade recording.
Cross-attention transformer trained on simultaneous EEG, ECG, EDA, PPG achieves 97.4% classification accuracy for 8 physiological states — outperforming single-modality baselines by 23.6 percentage points.
Systematic review of 47 studies finding that personalised Digital Twins outperform population-reference diagnostics in 41/47 cases. Identifies longitudinal biosignal-driven modeling as the highest-value clinical domain.
Establishes HRV metrics (SDNN, RMSSD, LF/HF, DFA α1) as validated biomarkers of autonomic function, cardiovascular risk, and stress. Defines the clinical norms used in the D-BAS™ ECG pipeline. 4,800+ citations — the definitive reference.
Real-time EEG classification achieving 94.2% accuracy for stress and 91.8% for cognitive load in ecologically valid conditions. Evaluates 14 feature sets across time, frequency, and connectivity domains.
Establishes engineering and clinical framework for closed-loop biosignal systems. Real-time feedback loops reduce adverse events by 61% compared to open-loop protocols. Direct blueprint for D-BAS™ Layer 5.
Review of 89 studies validating EDA as a reliable real-time biomarker for sympathetic NS activation, pain, and emotional regulation. Establishes EDA signal quality standards for clinical-grade wearable systems.
24-month longitudinal cohort (n=214) demonstrating personalised Digital Twins detect deterioration 3.2× earlier than population-average models. Establishes the clinical value of individual-baseline vs. reference-range approaches.
Prospective study (n=328) evaluating PPG-derived HRV vs. 12-lead ECG gold standard. Concordance coefficient 0.94 for RMSSD with validated artefact rejection. Establishes clinical case for PPG-based HRV monitoring.
100M-parameter foundation model pre-trained on 1.4 billion biosignal samples achieves state-of-the-art on 10 downstream clinical tasks with minimal fine-tuning — validating large-scale pre-training for biosignal AI.
Meta-analysis (173 studies) establishing neurocardiac coupling — joint EEG + HRV analysis — as a transdiagnostic biomarker for cardiovascular, metabolic, and psychiatric conditions. 6,200+ citations. Foundational for cross-channel biosignal analysis.
Fully integrated wearable combining electrochemical, electrophysiological, and optical sensors in one device. Longitudinal (n=174, 6 months) shows correlation with clinical laboratory outcomes. Highest-prestige validation of multi-modal wearable biosensing.
Open-access study: models personalised with 14 days of continuous biosignal data achieve 4.1× greater predictive accuracy for health event onset vs. population models. Provides computational framework for D-BAS™ Digital Twin construction.
Landmark longitudinal study (n=620, 12 months). AI-processed multi-modal biosignals identify dynamic signatures preceding clinical diagnosis by 7–21 days. Validates continuous AI-based multi-sensor data for early detection.
Demonstrates cross-channel biosignal analysis (EEG-ECG coupling) requires synchronisation below 0.5 ms. DMA-triggered multi-ADC pipelines achieve <0.3 ms jitter — directly informing the D-BAS™ hardware synchronisation specification.
RCT (n=284): AI-driven personalised interventions triggered by biosignal anomalies reduce targeted outcomes by 34% vs. generic recommendation control. Validates closed-loop AI feedback as clinically meaningful.
EEG spectral fingerprints are stable over time and unique per individual across 200+ subjects (99.1% discrimination accuracy). Provides neurophysiological basis for personalised EEG baseline modeling in Digital Twins.
NPU architecture achieving state-of-the-art biosignal inference at <50ms latency with 8-bit quantised models on 2mW power budget — enabling clinical-grade AI in wearable form factors. Directly validates D-BAS™ Home System on-device processing architecture.
Meta-analysis of 31 RCTs (n=8,242): continuous biosignal monitoring reduces adverse events by 28% and accelerates clinical response 2.4× vs. intermittent monitoring. Validates the core case for D-BAS™ 24/7 continuous monitoring.
Prospective trial (n=1,042): AI-powered continuous physiological monitoring reduces 30-day readmission by 38%. First large-scale prospective evidence for AI biosignal monitoring as a clinically meaningful intervention — published in NEJM AI, highest-tier clinical journal.
D-BAS™ is designed from inception within the regulatory frameworks of the world's leading health technology authorities — EU, FDA, UAE, WHO, and ISO.
D-BAS™ Pod targets Class IIa; Home System targets Class I. MDR mandates clinical evaluation, post-market surveillance, and UDI traceability.
Official Regulation ↗D-BAS™ AI aligns with FDA's AI/ML Software as a Medical Device (SaMD) framework, including predetermined change control plans for adaptive algorithms.
FDA Digital Health ↗UAE MoHAP's national digital health strategy targets AI-powered biosignal monitoring as a priority. D-BAS™ is aligned with the UAE strategic health technology roadmap.
MoHAP Digital Health ↗D-BAS™ follows WHO's six ethical principles for AI in health: transparency, inclusiveness, responsibility, impartiality, reliability, and safety & security.
WHO AI Guidance ↗Biosignal data is special-category health data under GDPR Article 9. D-BAS™ implements data minimisation, on-device processing, and explicit consent frameworks.
GDPR Art. 9 ↗D-BAS™ Pod hardware designed to IEC 60601-1 electrical safety. Quality management follows ISO 13485. Risk management follows ISO 14971 framework.
IEC Standards ↗EvatLabs LLC seeks academic and clinical research partnerships to validate and extend the D-BAS™ platform. We offer data access, hardware provision, and co-publication opportunities.
Seeking hospital and diagnostics partners for D-BAS™ Pod validation. IRB-ready protocols available for UAE, EU, and UK sites.
University partnerships for biosignal AI, Digital Twin modeling, and longitudinal health data analysis. Co-publication welcomed.
Anonymised, consent-gated biosignal datasets available to qualified researchers under GDPR and UAE data law compliant data sharing agreements.