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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Novel mathematical algorithm for pupillometric data analysis
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Novel mathematical algorithm for pupillometric data analysis

机译：用于光度数据分析的新型数学算法

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摘要

Pupillometry is used clinically to evaluate retinal and optic nerve function by measuring pupillary response to light stimuli. We have developed a mathematical algorithm to automate and expedite the analysis of non-filtered, non-calculated pupillometric data obtained from mouse pupillary light reflex recordings, obtained from dynamic pupillary diameter recordings following exposure of varying light intensities. The non-filtered, non-calculated pupillometric data is filtered through a low pass finite impulse response (FIR) filter. Thresholding is used to remove data caused by eye blinking, loss of pupil tracking, and/or head movement. Twelve physiologically relevant parameters were extracted from the collected data: (1) baseline diameter, (2) minimum diameter, (3) response amplitude, (4) re-dilation amplitude, (5) percent of baseline diameter, (6) response time, (7) re-dilation time, (8) average constriction velocity, (9) average re-dilation velocity, (10) maximum constriction velocity, (11) maximum re-dilation velocity, and (12) onset latency. No significant differences were noted between parameters derived from algorithm calculated values and manually derived results (p ≥ 0.05). This mathematical algorithm will expedite endpoint data derivation and eliminate human error in the manual calculation of pupillometric parameters from non-filtered, non-calculated pupillometric values. Subsequently, these values can be used as reference metrics for characterizing the natural history of retinal disease. Furthermore, it will be instrumental in the assessment of functional visual recovery in humans and pre-clinical models of retinal degeneration and optic nerve disease following pharmacological or gene-based therapies.
机译：眼睑测量法在临床上通过测量瞳孔对光刺激的反应来评估视网膜和视神经功能。我们已经开发出一种数学算法来自动化和加速对从鼠标瞳孔光反射记录获得的未过滤，未计算出的瞳孔测量数据的分析，这些数据是在曝光不同光强度后从动态瞳孔直径记录获得的。未过滤，未计算的瞳孔测量数据通过低通有限脉冲响应（FIR）滤波器进行过滤。阈值用于删除由眨眼，瞳孔跟踪丢失和/或头部移动引起的数据。从收集的数据中提取出十二个生理相关参数：（1）基线直径，（2）最小直径，（3）响应幅度，（4）再扩张幅度，（5）基线直径的百分比，（6）响应时间，（7）再扩张时间，（8）平均收缩速度，（9）平均再扩张速度，（10）最大收缩速度，（11）最大再扩张速度和（12）发作潜伏期。从算法计算值得出的参数与手动得出的结果之间没有显着差异（p≥0.05）。这种数学算法将加快端点数据的推导，并从未过滤，未计算的瞳孔测量值的人工计算瞳孔测量参数中消除人为错误。随后，这些值可用作表征视网膜疾病自然史的参考指标。此外，在药物或基于基因的疗法后，它将有助于评估人类的功能性视觉恢复以及视网膜变性和视神经疾病的临床前模型。

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