Chistiakov Sergey, Dolganov Anton, Constable Paul A, Zhdanov Aleksei, Kulyabin Mikhail, Thompson Dorothy A, Lee Irene O, Albasu Faisal, Borisov Vasilii, Ronkin Mikhail
Engineering School of Information Technologies, Telecommunications and Control Systems, Ural Federal University named after the First President of Russia B. N. Yeltsin, Yekaterinburg 620002, Russia.
Caring Futures Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, SA 5042, Australia.
Bioengineering (Basel). 2025 Sep 2;12(9):951. doi: 10.3390/bioengineering12090951.
The clinical electroretinogram (ERG) is a non-invasive diagnostic test used to assess the functional state of the retina by recording changes in the bioelectric potential following brief flashes of light. The recorded ERG waveform offers ways for diagnosing both retinal dystrophies and neurological disorders such as autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and Parkinson's disease. In this study, different time-series-based machine learning methods were used to classify ERG signals from ASD and typically developing individuals with the aim of interpreting the decisions made by the models to understand the classification process made by the models. Among the time-series classification (TSC) algorithms, the Random Convolutional Kernel Transform (ROCKET) algorithm showed the most accurate results with the fewest number of predictive errors. For the interpretation analysis of the model predictions, the SHapley Additive exPlanations (SHAP) algorithm was applied to each of the models' predictions, with the ROCKET and KNeighborsTimeSeriesClassifier (TS-KNN) algorithms showing more suitability for ASD classification as they provided better-defined explanations by discarding the uninformative non-physiological part of the ERG waveform baseline signal and focused on the time regions incorporating the clinically significant a- and b-waves of the ERG. With the potential broadening scope of practice for visual electrophysiology within neurological disorders, TSC may support the identification of important regions in the ERG time series to support the classification of neurological disorders and potential retinal diseases.
临床视网膜电图（ERG）是一种非侵入性诊断测试，通过记录短暂闪光后生物电位的变化来评估视网膜的功能状态。记录的ERG波形为诊断视网膜营养不良和神经疾病（如自闭症谱系障碍（ASD）、注意力缺陷多动障碍（ADHD）和帕金森病）提供了方法。在本研究中，使用了不同的基于时间序列的机器学习方法对来自ASD患者和发育正常个体的ERG信号进行分类，目的是解释模型做出的决策，以了解模型的分类过程。在时间序列分类（TSC）算法中，随机卷积核变换（ROCKET）算法显示出最准确的结果，预测误差最少。对于模型预测的解释分析，将SHapley加法解释（SHAP）算法应用于每个模型的预测，ROCKET算法和K近邻时间序列分类器（TS-KNN）算法显示出更适合ASD分类，因为它们通过舍弃ERG波形基线信号中无信息的非生理部分，提供了更明确的解释，并关注包含ERG临床上重要的a波和b波的时间区域。随着视觉电生理学在神经疾病中的潜在应用范围不断扩大，TSC可能有助于识别ERG时间序列中的重要区域，以支持神经疾病和潜在视网膜疾病的分类。
