Supplementary Material for: Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study

Introduction: Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning. Methods: Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls). Results: These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (p < 0.037). Discussion: Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.

引言：吞咽困难（吞咽障碍）在神经性疾病患者中颇为常见，并可能导致误吸、窒息和营养不良。吞咽障碍的常规诊断方法包括昂贵的侵入性成像程序或主观的床旁定性检查。可穿戴传感器作为无创且客观测量与吞咽相关的生理信号的替代方案，前景广阔。然而，将复杂的信号整合成敏感且具有临床意义的吞咽功能指标仍然是一项持续的挑战。为了填补这一空白，本研究提出两种新颖的数字监测工具，旨在利用可穿戴传感器数据和机器学习技术评估吞咽。方法：根据曼吞咽能力评估（MASA）标准，对比了中风后吞咽障碍患者与非吞咽障碍对照者在吞咽不同质地的食物和液体时的生物特征吞咽和呼吸信号。开发了两种机器学习方法，用于（1）对每次吞咽的障碍程度进行分类，并附有模型置信度评分，以提供透明的临床决策支持；（2）计算每次吞咽与无吞咽障碍表现的相似度。针对特定任务的模型使用来自505次吞咽（其中321次来自患者，184次来自对照者）的吞咽运动学和呼吸特征进行训练。结果：这些模型提供了敏感的指标，用以评估每次吞咽的障碍程度。两种方法均显示了吞咽的个体差异和患者特异性变化，这些变化仅凭MASA无法捕捉。编码呼吸-吞咽协调的传感器测量值是反映吞咽障碍存在和严重程度的重要特征。与唾液吞咽或液体啜饮相比，糊状物吞咽与对照者的差异更大（p < 0.037）。讨论：开发可解释的工具对于优化新型基于传感器的测量技术的临床应用至关重要。本研究提出的概念验证模型提供了具体且可传达的证据，以跟踪吞咽障碍随时间的恢复情况。通过优化训练方案和实际验证，这些工具可在临床和社区中自动测量和监测不同障碍程度的患者的吞咽情况。