S1 Dataset -

Rett syndrome, a rare genetic neurodevelopmental disorder in humans, does not have an effective cure. However, multiple therapies and medications exist to treat symptoms and improve patients’ quality of life. As research continues to discover and evaluate new medications for Rett syndrome patients, there remains a lack of objective physiological and motor activity-based (physio-motor) biomarkers that enable the measurement of the effect of these medications on the change in patients’ Rett syndrome severity. In our work, using a commercially available wearable chest patch, we recorded simultaneous electrocardiogram and three-axis acceleration from 20 patients suffering from Rett syndrome along with the corresponding Clinical Global Impression—Severity score, which measures the overall disease severity on a 7-point Likert scale. We derived physio-motor features from these recordings that captured heart rate variability, activity metrics, and the interactions between heart rate and activity. Further, we developed machine learning (ML) models to classify high-severity Rett patients from low-severity Rett patients using the derived physio-motor features. For the best-trained model, we obtained a pooled area under the receiver operating curve equal to 0.92 via a leave-one-out-patient cross-validation approach. Finally, we computed the feature popularity scores for all the trained ML models and identified physio-motor biomarkers for Rett syndrome.

瑞特综合征（Rett syndrome）是一种罕见的人类遗传性神经发育障碍，目前尚无有效治愈方案。不过现有多种疗法与药物可用于对症干预，改善患者的生活质量。随着针对瑞特综合征患者的新药研发与疗效评估研究持续推进，目前仍缺乏可客观量化药物对患者瑞特综合征严重程度变化影响的、基于生理与运动活动的（physio-motor）生物标志物。本研究采用市售可穿戴胸部贴片，同步采集了20名瑞特综合征患者的心电图与三轴加速度数据，并同步记录了对应的临床总体印象-严重程度评分（Clinical Global Impression—Severity score）——该评分采用7级李克特（Likert）量表对疾病总体严重程度进行量化。研究团队从上述采集数据中提取了涵盖心率变异性、活动指标以及心率与活动间关联的生理运动特征。在此基础上，本研究基于提取的生理运动特征构建机器学习（ML）模型，以区分高严重程度与低严重程度的瑞特综合征患者。经留一患者交叉验证法评估，最优训练模型的汇总受试者工作特征曲线下面积可达0.92。最后，本研究为所有训练完成的机器学习模型计算了特征流行度评分，并识别出了可用于瑞特综合征辅助诊断的生理运动生物标志物。