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Detecting Neurovascular Instability from Multimodal Physiological Signals Using Wearable-Compatible Edge AI: A Responsible Computational Framework

Truong Quynh Hoa, Hoang Dinh Cuong, Truong Xuan Khanh

Abstract

We propose Melaguard, a multimodal ML framework (Transformer-lite, 1.2M parameters, 4-head self-attention) for detecting neurovascular instability (NVI) from wearable-compatible physiological signals prior to structural stroke pathology. The model fuses heart rate variability (HRV), peripheral perfusion index, SpO2, and bilateral phase coherence into a composite NVI Score, designed for edge inference (WCET <=4 ms on Cortex-M4). NVI - the pre-structural dysregulation of cerebrovascular autoregulation preceding overt stroke - remains undetectable by existing single-modality wearables. With 12.2 million incident strokes annually, continuous multimodal physiological monitoring offers a practical path to community-scale screening. Three-stage independent validation: (1) synthetic benchmark (n=10,000), AUC=0.88 [0.83-0.92]; (2) clinical cohort PhysioNet CVES (n=172; 84 stroke, 88 control) - Transformer-lite achieves AUC=0.755 [0.630-0.778], outperforming LSTM (0.643), Random Forest (0.665), SVM (0.472); HRV-SDNN discriminates stroke (p=0.011); (3) PPG pipeline PhysioNet BIDMC (n=53) -- pulse rate r=0.748 and HRV surrogate r=0.690 vs. ECG ground truth. Cross-modality validation on PPG-BP (n=219) confirms PPG morphology classifies cerebrovascular disease at AUC=0.923 [0.869-0.968]. Multimodal fusion consistently outperforms single-modality baselines. Code: https://github.com/ClevixLab/Melaguard

Detecting Neurovascular Instability from Multimodal Physiological Signals Using Wearable-Compatible Edge AI: A Responsible Computational Framework

Abstract

We propose Melaguard, a multimodal ML framework (Transformer-lite, 1.2M parameters, 4-head self-attention) for detecting neurovascular instability (NVI) from wearable-compatible physiological signals prior to structural stroke pathology. The model fuses heart rate variability (HRV), peripheral perfusion index, SpO2, and bilateral phase coherence into a composite NVI Score, designed for edge inference (WCET <=4 ms on Cortex-M4). NVI - the pre-structural dysregulation of cerebrovascular autoregulation preceding overt stroke - remains undetectable by existing single-modality wearables. With 12.2 million incident strokes annually, continuous multimodal physiological monitoring offers a practical path to community-scale screening. Three-stage independent validation: (1) synthetic benchmark (n=10,000), AUC=0.88 [0.83-0.92]; (2) clinical cohort PhysioNet CVES (n=172; 84 stroke, 88 control) - Transformer-lite achieves AUC=0.755 [0.630-0.778], outperforming LSTM (0.643), Random Forest (0.665), SVM (0.472); HRV-SDNN discriminates stroke (p=0.011); (3) PPG pipeline PhysioNet BIDMC (n=53) -- pulse rate r=0.748 and HRV surrogate r=0.690 vs. ECG ground truth. Cross-modality validation on PPG-BP (n=219) confirms PPG morphology classifies cerebrovascular disease at AUC=0.923 [0.869-0.968]. Multimodal fusion consistently outperforms single-modality baselines. Code: https://github.com/ClevixLab/Melaguard
Paper Structure (29 sections, 2 equations, 8 figures, 6 tables)

This paper contains 29 sections, 2 equations, 8 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Wearable Neurovascular Monitoring
  4. Equitable Optical Biosensing
  5. Edge AI for Privacy-Preserving Inference
  6. Transformer Architectures for Biosignals
  7. System Design and Methods
  8. Framework Architecture
  9. Hydration-Activated Melanin Biosensor
  10. NVI Score Formulation
  11. Transformer-lite Classifier
  12. AeroKernel Edge Execution
  13. Datasets
  14. Statistical Analysis
  15. Computational Validation Results
  16. ...and 14 more sections

Figures (8)

  • Figure 1: System architecture of the Melaguard NVI framework, comprising four hierarchical layers: (A) PHBV:eumelanin composite material biosensing substrate with passive NFC; (B) multimodal signal acquisition (SpO$_2$, HRV, perfusion index, bilateral phase coherence proxy); (C) AeroKernel edge-AI inference with privacy-by-design AppBox isolation (USPTO PPA 63/838,707); (D) clinical NVI Score output with three-tier risk stratification. Dashed border denotes the responsibility-by-design boundary.
  • Figure 2: Hydration-activated biosensing mechanism (COMSOL-validated, computational). (A) Conductivity curve of PHBV:eumelanin (60:40) vs. relative humidity; operating point at 60% RH (dashed orange), target conductivity thresholds ($10^{-8}$ and $10^{-4}$ S/m). Green shaded region indicates active sensing zone. (B) Theoretical signal amplification model: hydrated melanin ionic conduction increases AC component amplitude. Note: Experimental fabrication constitutes planned future work (Section \ref{['sec:future']}).
  • Figure 3: Synthetic biosignal trajectories over 60 s comparing stable (green) and neurovascular instability (red) conditions across four NVI modalities. Perturbation onset at $t{=}30$ s (dashed vertical). Bottom panel: composite NVI Score with alert threshold at 80 (dashed orange). Generated from physiologically informed parametric models with Gaussian noise perturbation taelman2009hrv.
  • Figure 4: NVI dynamics under controlled perturbation and recovery. (A) NVI score decline vs. perturbation intensity (mean ${\pm}$ SD, 100 Monte Carlo simulations); Alert Level 1 (NVI $<$ 80, orange) and Level 2 (NVI $<$ 60, red) thresholds. (B) NVI recovery following signal normalisation; exponential recovery model with $\tau{=}60$ s.
  • Figure 5: Model comparison on synthetic validation data ($n{=}10{,}000$; 5-fold CV). (A) ROC curves; shaded region shows bootstrap 95% CI for Transformer-lite. (B) Performance metrics comparison (AUC, accuracy, sensitivity, specificity); Transformer-lite achieves superior AUC ${=}\,0.88$.
  • ...and 3 more figures