Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns.
Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson's Disease Classification of Gait Patterns.
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DOI：
10.1109/embc44109.2020.9176572
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Turaga P
中科院分区：
文献类型：
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作者：
Som A;Krishnamurthi N;Buman M;Turaga P
深度学习算法在不同医疗保健应用中的应用和使用正在稳步增长。然而，使用这样的算法可能被证明是具有挑战性的，因为它们需要大量的训练数据来捕获不同的可能变化。这使得它们很难在临床环境中使用，因为在大多数健康应用中，研究人员通常不得不使用有限的数据。更少的数据可能会导致深度学习模型过度拟合。在本文中，我们要问的是，如何使用来自不同环境、不同用例以及数据分布差异很大的数据。我们通过使用来自进行日常生活活动的健康受试者的单传感器加速度计数据（源数据集）来验证该用例，以提取与用于帕金森病分类的多传感器加速度计步态数据（目标数据集）相关的特征。我们使用源数据集训练预训练模型，并将其用作特征提取器。我们证明了为目标数据集提取的特征可以用于训练有效的分类模型。我们的预训练源模型由卷积自动编码器组成，目标分类模型是一个简单的多层感知器模型。我们探索了两种不同的预训练源模型，使用不同的活动组进行训练，并分析了预训练模型的选择对帕金森病分类任务的影响。
Application and use of deep learning algorithms for different healthcare applications is gaining interest at a steady pace. However, use of such algorithms can prove to be challenging as they require large amounts of training data that capture different possible variations. This makes it difficult to use them in a clinical setting since in most health applications researchers often have to work with limited data. Less data can cause the deep learning model to over-fit. In this paper, we ask how can we use data from a different environment, different use-case, with widely differing data distributions. We exemplify this use case by using single-sensor accelerometer data from healthy subjects performing activities of daily living - ADLs (source dataset), to extract features relevant to multi-sensor accelerometer gait data (target dataset) for Parkinson’s disease classification. We train the pre-trained model using the source dataset and use it as a feature extractor. We show that the features extracted for the target dataset can be used to train an effective classification model. Our pretrained source model consists of a convolutional autoencoder, and the target classification model is a simple multi-layer perceptron model. We explore two different pre-trained source models, trained using different activity groups, and analyze the influence the choice of pre-trained model has over the task of Parkinson’s disease classification.