用可穿戴设备非侵入式检测脊髓损伤者的自主神经反射异常，提升早期预警能力。
Detection of Autonomic Dysreflexia in Individuals With Spinal Cord Injury Using Multimodal Wearable Sensors
- 融合心电、心率等多模态信号，用可解释机器学习识别血压飙升征兆。
- 心率与心电特征预测准确率达AUC 0.93，显著优于其他生理信号。
- 模型抗传感器丢失，适合长期居家监测，助力临床及时干预。
自主神经反射异常（AD）是脊髓损伤（SCI）患者可能出现的危及生命的病症，表现为突发严重血压升高。早期准确检测对预防心血管并发症至关重要，但现有方法或为侵入式，或依赖主观症状报告，难以日常应用。本研究提出一种非侵入式、可解释的机器学习框架，利用多模态可穿戴传感器检测AD。数据来自27名慢性SCI患者在尿动力学检查期间采集的生理信号，包括心电图（ECG）、光电容积脉搏波（PPG）、生物阻抗（BioZ）、体温、呼吸频率（RR）和心率（HR），覆盖三款商用设备。通过同步袖带血压测量获得客观的AD标签。经信号预处理与特征提取后，采用BorutaSHAP进行鲁棒特征选择，并使用SHAP值保证可解释性。训练了针对不同模态和设备的弱学习器，再通过堆叠集成元模型聚合。交叉验证按参与者分层，确保泛化性。结果显示，心率与心电衍生特征最具信息量，尤其反映节律形态与变异性。最近邻集成模型表现最佳（宏平均F1=0.77±0.03），显著优于基线模型。各模态中，心率达到最高AUC（0.93），其次为心电（0.88）和光电容积脉搏波（0.86）。呼吸频率与体温特征贡献较小，与其数据缺失及特异性低一致。模型对传感器掉线具有鲁棒性，且与临床AD事件高度吻合。该成果为个体化、实时监测提供了重要路径。
原文摘要 · Abstract (English)
Autonomic Dysreflexia (AD) is a potentially life-threatening condition characterized by sudden, severe blood pressure (BP) spikes in individuals with spinal cord injury (SCI). Early, accurate detection is essential to prevent cardiovascular complications, yet current monitoring methods are either invasive or rely on subjective symptom reporting, limiting applicability in daily file. This study presents a non-invasive, explainable machine learning framework for detecting AD using multimodal wearable sensors. Data were collected from 27 individuals with chronic SCI during urodynamic studies, including electrocardiography (ECG), photoplethysmography (PPG), bioimpedance (BioZ), temperature, respiratory rate (RR), and heart rate (HR), across three commercial devices. Objective AD labels were derived from synchronized cuff-based BP measurements. Following signal preprocessing and feature extraction, BorutaSHAP was used for robust feature selection, and SHAP values for explainability. We trained modality- and device-specific weak learners and aggregated them using a stacked ensemble meta-model. Cross-validation was stratified by participants to ensure generalizability. HR- and ECG-derived features were identified as the most informative, particularly those capturing rhythm morphology and variability. The Nearest Centroid ensemble yielded the highest performance (Macro F1 = 0.77+/-0.03), significantly outperforming baseline models. Among modalities, HR achieved the highest area under the curve (AUC = 0.93), followed by ECG (0.88) and PPG (0.86). RR and temperature features contributed less to overall accuracy, consistent with missing data and low specificity. The model proved robust to sensor dropout and aligned well with clinical AD events. These results represent an important step toward personalized, real-time monitoring for individuals with SCI.
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