Deep Learning for Medication Assessment of Individuals with Parkinson's Disease Using Wearable Sensors.
Deep Learning for Medication Assessment of Individuals with Parkinson's Disease Using Wearable Sensors.
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DOI：
10.1109/embc.2018.8513344
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发表时间：
2018-07-01
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影响因子：
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通讯作者：
Ghoraani, Behnaz
中科院分区：
文献类型：
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作者：
Hssayeni, Murtadha D;Adams, Jamie L;Ghoraani, Behnaz
运动在“关闭”状态（药物无益处）和“开启”状态（药物有最佳益处）之间的波动是中期和晚期帕金森病（PD）患者临床管理的主要焦点。在这项工作中，开发了一种基于长短期记忆（LSTM）作为深度学习方法的自动算法，使用可穿戴传感器识别PD患者在各种日常生活活动中的运动波动。该网络在两个数据集（即数据集1和数据集2）上进行评估，其中包括19名PD患者的记录，使用基于主题的留一交叉验证。设计的LSTM网络仅使用一个踝关节传感器，在数据集1和数据集2上的平均分类率分别为73%和77%，取得了很好的结果。
Motor fluctuations between "OFF" state (with no benefit from medication) and " ON" state (with optimum benefit from medication) are a major focus of clinical managements in individuals with mid-stage and advance Parkinson's disease (PD). In this work, an automated algorithm based on Long Short-Term Memory (LSTM) as a deep learning method is developed to identify motor fluctuations in individuals with PD using wearable sensors during a variety of daily living activities. This network was evaluated on two datasets i.e., Dataset 1 and Dataset 2) that included recordings of 19 individuals with PD using subject-based leave-one-out cross-validation. The designed LSTM network yielded promising results using only one ankle sensor with an average classification rate of 73% and 77% for Dataset 1 and Dataset 2, respectively.