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: this https URL
Abstract (translated)
我们提出Melaguard，一个专为从可穿戴兼容生理信号中检测神经血管不稳定性（NVI）而设计的多模态机器学习框架（轻量Transformer，120万参数，4头自注意力），该信号出现在结构性中风病理之前。该模型融合心率变异性（HRV）、外周灌注指数、血氧饱和度（SpO2）和双侧相位相干性，生成复合NVI评分，专为边缘推理设计（在Cortex-M4上最坏情况执行时间≤4毫秒）。NVI——即显性中风之前脑血管自动调节的结构前失调——现有单模态可穿戴设备无法检测。鉴于每年新增1220万中风病例，连续多模态生理监测为社区规模筛查提供了可行路径。三阶段独立验证：(1) 合成基准测试（n=10,000），AUC=0.88 [0.83-0.92]；(2) 临床队列PhysioNet CVES（n=172；84例中风，88例对照）——轻量Transformer达到AUC=0.755 [0.630-0.778]，优于LSTM（0.643）、随机森林（0.665）、支持向量机（0.472）；HRV-SDNN可区分中风（p=0.011）；(3) PPG流水线PhysioNet BIDMC（n=53）——脉搏率r=0.748，HRV替代指标r=0.690（相对于ECG金标准）。在PPG-BP（n=219）上的跨模态验证证实，PPG形态学可分类脑血管疾病，AUC=0.923 [0.869-0.968]。多模态融合持续优于单模态基线。代码：此https链接
URL
https://arxiv.org/abs/2603.20442
