论文标题
1-D旋转神经网络，用于分析苏格兰条件下学生尺寸变化的分析
1-D Convlutional Neural Networks for the Analysis of Pupil Size Variations in Scotopic Conditions
论文作者
论文摘要
众所周知，通过眼线轨道记录的对学生大小变化的系统分析是有关受试者唤醒和认知状态的丰富信息来源。当前的学生分析方法仅限于描述性统计，在处理广泛的受试者间可变性方面挣扎，并且必须与一系列长系列预处理信号操作相结合。在此，我们提出了一种数据驱动的方法，其中1D卷积神经网络直接应用于原始的学生大小数据。为了测试其有效性，我们将我们的方法应用于二元分类任务中，包括两组不同的受试者：一组帕金森氏病（PDS）的老年患者（PDS），该病例已经广泛报告了学生异常，以及一组健康的成年受试者（HCS）。在Scotopic条件下（完全黑暗，0勒克斯）收集了远程注册（10分钟）。对1D卷积神经网络模型进行了训练，以分类短程序列（注册为10至60秒）。该模型在持有测试集中提供了高平均精度的预测。释放数据集和代码以用于可重复性和基准测试目的。
It is well known that a systematic analysis of the pupil size variations, recorded by means of an eye-tracker, is a rich source of information about a subject's arousal and cognitive state. Current methods for pupil analysis are limited to descriptive statistics, struggle in handling the wide inter-subjects variability and must be coupled with a long series of pre-processing signal operations. In this we present a data-driven approach in which 1-D Convolutional Neural Networks are applied directly to the raw pupil size data. To test its effectiveness, we apply our method in a binary classification task with two different groups of subjects: a group of elderly patients with Parkinson disease (PDs), a condition in which pupil abnormalities have been extensively reported, and a group of healthy adults subjects (HCs). Long-range registration (10 minutes) of the pupil size were collected in scotopic conditions (complete darkness, 0 lux). 1-D convolutional neural network models are trained for classification of short-range sequences (10 to 60 seconds of registration). The model provides prediction with high average accuracy on a hold out test set. Dataset and codes are released for reproducibility and benchmarking purposes.
