Hssayeni Murtadha D, Jimenez-Shahed Joohi, Burack Michelle A, Ghoraani Behnaz
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:6001-6004. doi: 10.1109/EMBC44109.2020.9176847.
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.
异动症是中晚期帕金森病（PD）患者可能会出现的异常不自主运动。通过调整左旋多巴药物的剂量或服用频率，可以减轻这些令人困扰的运动障碍。然而，为了成功进行调整，主治医生需要了解患者在自然生活环境中所经历的异动症严重程度分级信息。在这项研究中，我们利用从帕金森病患者上下肢收集的运动数据，结合基于长短期记忆网络的深度模型来估计异动症的严重程度。我们在一个包含14名患有异动症的帕金森病受试者的数据集上对模型进行了训练和验证。受试者进行了各种日常生活活动，同时由神经科医生对他们的异动症严重程度进行评分。我们开发的模型所估计的异动症严重程度分级与神经科医生评定的异动症评分高度相关（r = 0.86（p < 0.001），平均绝对误差为1.77（6%）），这表明该方法有潜力提供有效管理异动症所需的药物调整信息。
