Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis.
Automated Detection of Stereotypical Motor Movements in Autism Spectrum Disorder Using Recurrence Quantification Analysis.
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DOI：
10.3389/fninf.2017.00009
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发表时间：
2017
影响因子：
3.5
通讯作者：
Goodwin MS
中科院分区：
文献类型：
--
作者：
Großekathöfer U;Manyakov NV;Mihajlović V;Pandina G;Skalkin A;Ness S;Bangerter A;Goodwin MS
最近的一些研究使用加速度计特征作为机器学习分类器的输入，显示出自动检测自闭症谱系障碍（ASD）患者的刻板运动（SMM）的有希望的结果。然而，在不同类型的加速度计及其在身体上的位置上复制这些结果仍然是一个挑战。我们引入了一组新的功能，在这个领域的基础上，递归图和定量分析，是方向不变的，能够捕捉非线性动态的SMM。将这些功能应用于现有的包含加速度数据的已发布数据集，与当前最先进的已发布结果相比，我们的准确性平均提高了9%。此外，我们提供的证据表明，一个单一的躯干传感器可以自动检测多种类型的SMM在ASD，我们的方法允许识别SMM与高精度的个人时，使用一个人独立的分类器。
A number of recent studies using accelerometer features as input to machine learning classifiers show promising results for automatically detecting stereotypical motor movements (SMM) in individuals with Autism Spectrum Disorder (ASD). However, replicating these results across different types of accelerometers and their position on the body still remains a challenge. We introduce a new set of features in this domain based on recurrence plot and quantification analyses that are orientation invariant and able to capture non-linear dynamics of SMM. Applying these features to an existing published data set containing acceleration data, we achieve up to 9% average increase in accuracy compared to current state-of-the-art published results. Furthermore, we provide evidence that a single torso sensor can automatically detect multiple types of SMM in ASD, and that our approach allows recognition of SMM with high accuracy in individuals when using a person-independent classifier.