Pupillary Light Reflex Data Processing Based on an Adaptive Linear Tracking Differentiator

The pupillary light reflex (PLR), an important physiological response for assessing the health of the nervous system, is widely used in clinical settings to monitor brain disorders, neurological injuries, and autonomic nervous system functions. However, although existing pupil detection techniques can accurately identify the pupil, they still face issues such as insufficient algorithm robustness and sensitivity to noise when processing PLR data, especially in calculating pupil dynamic characteristics such as pupil dilation duration. To address this issue, this paper proposes a PLR data processing method based on an adaptive linear tracking differentiator. This method first utilizes linear interpolation to fill in the missing data and removes sudden noise outliers using an amplitude limiting filter. Subsequently, an adaptive second-order linear tracking differentiator is employed to smooth the data, and averaging through bidirectional differentiation is used to eliminate lag. The experimental results show that the adaptive linear tracking differentiator can effectively filter out high-frequency noise while preserving the dynamic trend of the signal. In particular, it significantly reduces data fluctuations and enhances the accuracy and stability of the data in the smoothing process of pupil diameter variation rates.