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A convolutional oculomotor representation to model parkinsonian fixational patterns from magnified videos
Pattern Analysis and Applications ( IF 3.7 ) Pub Date : 2020-10-21 , DOI: 10.1007/s10044-020-00922-4
Isail Salazar , Said Pertuz , William Contreras , Fabio Martínez

Oculomotor alterations are a promising biomarker to detect and characterize Parkinson’s disease (PD), even in prodromal stages. Nowadays, however, only global and simplified gaze trajectories are used to approximate the complex interactions between neuromotor commands and ocular muscles. Besides, the acquisition of such signals often requires sophisticated calibration and invasive settings. This work presents a novel imaging biomarker for PD assessment that models ocular fixational movements, recorded with conventional cameras. Firstly, a video acceleration magnification is performed to enhance small relevant fixation patterns on standard gaze video recordings. Hence, from each video are extracted a set of spatio-temporal slices, which thereafter are represented as convolutional feature maps, recovered as the first-layer responses of pre-trained CNN architectures. The feature maps are then efficiently encoded by means of covariance matrices to train a support vector machine and perform the disease classification. From a set of 130 recordings of 13 PD patients and 13 age-matched controls, the proposed approach achieved an average accuracy of 95.4% and an AUC of 0.984, following a leave-one-patient-out cross-validation scheme. The proposed imaging-based descriptor properly captures known disease tremor patterns, since PD classification performance is outstanding when augmented motion frequencies were fixed within tremor-related ranges. These results suggest a successful PD characterization from fixational eye motion patterns using ordinary videos.



中文翻译:

卷积动眼运动表示法,用于从放大的视频中模拟帕金森定律模式

动眼力改变是检测和表征帕金森氏病(PD)的有前途的生物标志物,即使在前驱期也是如此。然而,如今,仅使用全局和简化的凝视轨迹来近似神经运动指令和眼肌之间的复杂相互作用。此外,获取此类信号通常需要复杂的校准和侵入式设置。这项工作提出了一种用于PD评估的新型成像生物标记物,该标记物可以模拟用常规相机记录的眼固定运动。首先,执行视频加速放大以增强标准注视视频记录上的小的相关固定模式。因此,从每个视频中提取了一组时空切片,然后将其表示为卷积特征图,恢复为预先训练的CNN架构的第一层响应。然后借助协方差矩阵对特征图进行有效编码,以训练支持向量机并执行疾病分类。通过对130名13名PD患者和13名年龄匹配的对照进行的记录,在采用留一人出诊的交叉验证方案后,该方法的平均准确度为95.4%,AUC为0.984。由于将增强运动频率固定在与震颤相关的范围内时,PD分级性能非常出色,因此基于成像的描述符可以正确捕获已知的疾病震颤模式。这些结果表明,使用普通视频可以成功地从注视眼动模式进行PD表征。然后借助协方差矩阵对特征图进行有效编码,以训练支持向量机并执行疾病分类。通过对130名13名PD患者和13名年龄匹配的对照进行的记录,在采用留一人出诊的交叉验证方案后,该方法的平均准确度为95.4%,AUC为0.984。由于将增强运动频率固定在与震颤相关的范围内时,PD分级性能非常出色,因此基于成像的描述符可以正确捕获已知的疾病震颤模式。这些结果表明,使用普通视频可以成功地从注视眼动模式进行PD表征。然后借助协方差矩阵对特征图进行有效编码,以训练支持向量机并执行疾病分类。通过对130名13名PD患者和13名年龄匹配的对照进行的记录,在采用留一人出诊的交叉验证方案后,该方法的平均准确度为95.4%,AUC为0.984。由于将增强运动频率固定在与震颤相关的范围内时,PD分级性能非常出色,因此基于成像的描述符可以正确捕获已知的疾病震颤模式。这些结果表明,使用普通视频可以成功地从注视眼动模式进行PD表征。在采用“一人一出”交叉验证方案之后,该方法的平均准确度达到95.4%,AUC为0.984。由于将增强运动频率固定在与震颤相关的范围内时,PD分级性能非常出色,因此基于成像的描述符可以正确捕获已知的疾病震颤模式。这些结果表明,使用普通视频可以成功地从注视眼动模式进行PD表征。在采用“一人一出”交叉验证方案之后,该方法的平均准确度达到95.4%,AUC为0.984。由于将增强运动频率固定在与震颤相关的范围内时,PD分级性能非常出色,因此基于成像的描述符可以正确捕获已知的疾病震颤模式。这些结果表明,使用普通视频可以成功地从注视眼动模式进行PD表征。

更新日期:2020-10-30
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