Dyskinesia Severity Estimation in Patients with Parkinson's Disease Using Wearable Sensors and A Deep LSTM Network.
Dyskinesia Severity Estimation in Patients with Parkinson's Disease Using Wearable Sensors and A Deep LSTM Network.
复制标题
使用可穿戴传感器和深度 LSTM 网络估计帕金森病患者的运动障碍严重程度。
DOI：
10.1109/embc44109.2020.9176847
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Ghoraani,Behnaz
中科院分区：
文献类型：
--
作者：
Hssayeni,MurtadhaD;Jimenez-Shahed,Joohi;Burack,MichelleA;Ghoraani,Behnaz
运动障碍是中晚期帕金森病患者可能出现的不自主运动异常。通过调整左旋多巴的剂量或频率，这些麻烦的运动障碍就会减少。然而，为了做出成功的调整，治疗医生需要患者在自然生活环境中所经历的运动障碍严重程度的信息。在这项工作中，我们使用从PD患者的上肢和下肢收集的运动数据以及基于长短期记忆的深度模型来估计运动障碍的严重程度。我们在14名患有运动障碍的PD患者的数据集上训练并验证了我们的模型。受试者进行各种日常生活活动，同时他们的运动障碍严重程度由神经科医生评定。我们开发的模型估计的运动障碍严重程度评分与神经科医生评定的运动障碍评分高度相关（r=0.86 （p< 0.001）和1.77 MAE(6%)），表明开发的方法在为运动障碍管理提供有效药物调整所需的信息方面具有潜力。
Dyskinesias are abnormal involuntary movements that patients with mid-stage and advanced Parkinson's disease (PD) may suffer from. These troublesome motor impairments are reduced by adjusting the dose or frequency of medication levodopa. However, to make a successful adjustment, the treating physician needs information about the severity rating of dyskinesia as patients experience in their natural living environment. In this work, we used movement data collected from the upper and lower extremities of PD patients along with a deep model based on Long Short-Term Memory to estimate the severity of dyskinesia. We trained and validated our model on a dataset of 14 PD subjects with dyskinesia. The subjects performed a variety of daily living activities while their dyskinesia severity was rated by a neurologist. The estimated dyskinesia severity ratings from our developed model highly correlated with the neurologist-rated dyskinesia scores (r=0.86 (p<; 0.001) and 1.77 MAE (6%)) indicating the potential of the developed the approach in providing the information required for effective medication adjustments for dyskinesia management.