Title: Detection of Autonomic Dysreflexia in Individuals With Spinal Cord Injury Using Multimodal Wearable Sensors Show Chinese title

Title: 使用多模式可穿戴传感器检测脊髓损伤个体的自主神经反射异常

Abstract: 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. Show Chinese abstract

Abstract: 自主性高血压危象（AD）是一种可能危及生命的状况，其特征是脊髓损伤（SCI）个体出现突然的严重血压（BP）升高。 早期准确检测对于预防心血管并发症至关重要，但目前的监测方法要么具有侵入性，要么依赖于主观症状报告，限制了在日常文件中的适用性。 本研究提出了一种非侵入性的、可解释的机器学习框架，用于通过多模态可穿戴传感器检测AD。 数据是在泌尿动力学研究中从27名慢性SCI患者那里收集的，包括心电图（ECG）、光电容积描记法（PPG）、生物阻抗（BioZ）、体温、呼吸频率（RR）和心率（HR），涵盖了三种商用设备。 客观的AD标签来自同步的袖带式血压测量。 在信号预处理和特征提取之后，使用BorutaSHAP进行稳健的特征选择，并使用SHAP值进行可解释性分析。 我们训练了模态和设备特定的弱学习器，并使用堆叠集成元模型进行聚合。 交叉验证按参与者分层，以确保泛化能力。 HR和ECG衍生的特征被确定为最有信息量的，特别是那些捕捉节奏形态和变异性的特征。 最近邻集成取得了最高性能（宏F1 = 0.77±0.03），显著优于基线模型。 在模态中，HR实现了最高的曲线下面积（AUC = 0.93），其次是ECG（0.88）和PPG（0.86）。 RR和体温特征对整体准确性贡献较小，这与数据缺失和低特异性一致。 该模型对传感器丢失具有鲁棒性，并与临床AD事件高度吻合。 这些结果代表了向SCI患者个性化实时监测迈出的重要一步。