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使用垂直轴旋转和人工神经网络检测平衡障碍。

Detection of balance disorders using rotations around vertical axis and an artificial neural network.

机构信息

Department of Microelectronics and Computer Science, Lodz University of Technology, 221 Wolczanska Str., 93-005, Lodz, Poland.

Audiology and Phoniatrics Clinic, Nofer Institute of Occupational Medicine, Lodz, Poland.

出版信息

Sci Rep. 2022 May 6;12(1):7472. doi: 10.1038/s41598-022-11425-z.

DOI:10.1038/s41598-022-11425-z
PMID:35523836
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9076858/
Abstract

Vestibular impairments affect patients' movements and can result in difficulties with daily life activities. The main aim of this study is to answer the question whether a simple and short test such as rotation about a vertical axis can be an objective method of assessing balance dysfunction in patients with unilateral vestibular impairments. A 360˚ rotation test was performed using six MediPost devices. The analysis was performed in three ways: (1) the analytical approach based only on data from one sensor; (2) the analytical approach based on data from six sensors; (3) the artificial neural network (ANN) approach based on data from six sensors. For approaches 1 and 2 best results were obtained using maximum angular velocities (MAV) of rotation and rotation duration (RD), while approach 3 used 11 different features. The following sensitivities and specificities were achieved: for approach 1: MAV-80% and 60%, RD-69% and 74%; for approach 2: 61% and 85% and RD-74% and 56%; for approach 3: 88% and 84%. The ANN-based six-sensor approach revealed the best sensitivity and specificity among parameters studied, however one-sensor approach might be a simple screening test used e.g. for rehabilitation purposes.

摘要

前庭功能障碍会影响患者的运动能力,并导致日常生活活动困难。本研究的主要目的是回答一个问题,即像绕垂直轴旋转这样简单而简短的测试是否可以成为评估单侧前庭功能障碍患者平衡功能障碍的客观方法。使用六个 MediPost 设备进行了 360˚ 旋转测试。分析采用三种方法进行:(1)仅基于一个传感器数据的分析方法;(2)基于六个传感器数据的分析方法;(3)基于六个传感器数据的人工神经网络 (ANN) 方法。对于方法 1 和 2,最佳结果是使用旋转的最大角速度 (MAV) 和旋转持续时间 (RD) 获得的,而方法 3 使用了 11 个不同的特征。得到的以下灵敏度和特异性为:方法 1:MAV-80%和 60%,RD-69%和 74%;方法 2:61%和 85%,RD-74%和 56%;方法 3:88%和 84%。基于 ANN 的六传感器方法在研究的参数中显示出最佳的灵敏度和特异性,然而,单传感器方法可能是一种简单的筛查测试,例如用于康复目的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5d9/9076858/84f49af6e3fb/41598_2022_11425_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5d9/9076858/a6151aad1964/41598_2022_11425_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5d9/9076858/84f49af6e3fb/41598_2022_11425_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5d9/9076858/a6151aad1964/41598_2022_11425_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e5d9/9076858/84f49af6e3fb/41598_2022_11425_Fig2_HTML.jpg

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