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首页> 外文期刊>EURASIP journal on bioinformatics and systems biology >Discovering irregular pupil light responses to chromatic stimuli using waveform shapes of pupillograms
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Discovering irregular pupil light responses to chromatic stimuli using waveform shapes of pupillograms

机译：使用瞳孔图的波形形状发现瞳孔光对色刺激的不规则响应

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Background The waveforms of the pupillary light reflex (PLR) can be analyzed in a diagnostic test that allows for differentiation between disorders affecting photoreceptors and disorders affecting retinal ganglion cells, using various signal processing techniques. This procedure has been used on both healthy subjects and patients with age-related macular degeneration (AMD), as a simple diagnostic procedure is required for diagnosis. Results The Fourier descriptor technique is used to extract the features of PLR waveform shapes of pupillograms and their amplitudes. To detect those patients affected by AMD using the extracted features, multidimensional scaling (MDS) and clustering techniques were used to emphasize stimuli and subject differences. The detection performance of AMD using the features and the MDS technique shows only a qualitative tendency, however. To evaluate the detection performance quantitatively, a set of combined features was created to evaluate characteristics of the PLR waveform shapes in detail. Classification performance was compared across three categories (AMD patients, aged, and healthy subjects) using the Random Forest method, and weighted values were optimized using variations of the classification error rates. The results show that the error rates for healthy pupils and AMD-affected pupils were low when the value of the coefficient for a combination of PLR amplitudes and features of waveforms was optimized as 1.5. However, the error rates for patients with age-affected eyes was not low. Conclusions A classification procedure for AMD patients has been developed using the features of PLR waveform shapes and their amplitudes. The results show that the error rates for healthy PLRs and AMD PLRs were low when the Random Forest method was used to produce the classification. The classification of pupils of patients with age-affected eyes should be carefully considered in order to produce optimum results.
机译：背景技术可以在诊断测试中分析瞳孔光反射（PLR）的波形，该诊断测试可以使用各种信号处理技术来区分影响感光器的疾病和影响视网膜神经节细胞的疾病。由于诊断需要简单的诊断程序，因此该程序已用于健康受试者和患有年龄相关性黄斑变性（AMD）的患者。结果利用傅里叶描述符技术提取了瞳孔图的PLR波形形状及其幅度特征。为了使用提取的特征检测那些受AMD影响的患者，多维缩放（MDS）和聚类技术被用来强调刺激和受试者差异。但是，使用这些功能和MDS技术的AMD的检测性能仅显示出定性趋势。为了定量评估检测性能，创建了一组组合特征来详细评估PLR波形形状的特征。使用随机森林方法比较了三个类别（AMD患者，年龄和健康受试者）的分类性能，并使用分类错误率的变化优化了加权值。结果表明，当将PLR幅度和波形特征组合的系数的值优化为1.5时，健康学生和AMD受影响的学生的错误率较低。但是，患有年龄影响的眼睛患者的错误率并不低。结论利用PLR波形形状和幅度特征开发了针对AMD患者的分类程序。结果表明，使用随机森林法进行分类时，健康PLR和AMD PLR的错误率较低。为了产生最佳的结果，应该仔细考虑对有年龄影响的眼睛的学生的瞳孔分类。

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