Physical Education Department, Shijiazhuang Information Engineering Vocational College, Shijiazhuang 05000, Hebei, China.
Physical Education and Health College, Hebei Normal University of Science and Technology, Qinhuangdao 066004, Hebei, China.
J Healthc Eng. 2021 Oct 27;2021:6382619. doi: 10.1155/2021/6382619. eCollection 2021.
Parkinson's disease is a common chronic disease that affects a large number of people. In the real world, however, Parkinson's disease can result in a loss of physical performance, which is classified as a movement disorder by clinicians. Parkinson's disease is currently diagnosed primarily through clinical symptoms, which are highly dependent on clinician experience. As a result, there is a need for effective early detection methods. Traditional machine learning algorithms filter out many inherently relevant features in the process of dimensionality reduction and feature classification, lowering the classification model's performance. To solve this problem and ensure high correlation between features while reducing dimensionality to achieve the goal of improving classification performance, this paper proposes a recurrent neural network classification model based on self attention and motion perception. Using a combination of self-attention mechanism and recurrent neural network, as well as wearable inertial sensors, the model classifies and trains the five brain area features extracted from MRI and DTI images (cerebral gray matter, white matter, cerebrospinal fluid density, and so on). Clinical and exercise data can be combined to produce characteristic parameters that can be used to describe movement sluggishness. The experimental results show that the model proposed in this paper improves the recognition performance of Parkinson's disease, which is better than the compared methods by 2.45% to 12.07%.
帕金森病是一种常见的慢性疾病，影响着大量人群。然而，在现实世界中，帕金森病会导致身体机能的丧失，这被临床医生归类为运动障碍。目前，帕金森病主要通过临床症状进行诊断，这些症状高度依赖于临床医生的经验。因此，需要有效的早期检测方法。传统的机器学习算法在降维和特征分类的过程中过滤掉了许多内在相关的特征，降低了分类模型的性能。为了解决这个问题，并确保在降低维度的同时保持特征之间的高度相关性，以达到提高分类性能的目的，本文提出了一种基于自注意力和运动感知的循环神经网络分类模型。该模型结合了自注意力机制和循环神经网络，以及可穿戴惯性传感器，对从 MRI 和 DTI 图像中提取的五个大脑区域特征（脑灰质、白质、脑脊液密度等）进行分类和训练。可以结合临床和运动数据，生成可用于描述运动迟缓的特征参数。实验结果表明，本文提出的模型提高了帕金森病的识别性能，比对比方法提高了 2.45%到 12.07%。
