用自注意力网络填补眼球追踪数据缺失，提升神经退行性疾病分析可靠性
Imputation of Missing Data in Smooth Pursuit Eye Movements Using a Self-Attention-based Deep Learning Approach
- 基于自注意力机制的深度学习模型，自动学习序列间依赖关系填补缺失值
- 在帕金森患者与健康人共5504条数据上，误差指标均显著优于现有方法
- 对大段缺失数据仍保持鲁棒性，适合神经疾病筛查中的不完整眼动分析
时间序列中缺失数据是生物医学信号分析的关键挑战，尤其在平滑追踪眼动数据中，常因眨眼和追踪丢失产生间隙，影响有意义生物标志物的提取。本文提出一种基于自注意力机制的时间序列插补框架，利用深度学习捕捉序列内部依赖，并通过定制自编码器进一步优化重建结果。实验基于172名帕金森患者及健康对照者的5,504条眼动序列，结果显示该方法在均方误差、平均绝对误差、相对误差等时域指标上显著优于现有技术，同时有效保留了信号的频域特性。即使在大段数据缺失情况下仍表现稳健，为神经退行性疾病筛查与监测中的不完整数据处理提供了可靠解决方案。
原文摘要 · Abstract (English)
Missing data is a relevant issue in time series, especially in biomedical sequences such as those corresponding to smooth pursuit eye movements, which often contain gaps due to eye blinks and track losses, complicating the analysis and extraction of meaningful biomarkers. In this paper, a novel imputation framework is proposed using Self-Attention-based Imputation networks for time series, which leverages the power of deep learning and self-attention mechanisms to impute missing data. We further refine the imputed data using a custom made autoencoder, tailored to represent smooth pursuit eye movement sequences. The proposed approach was implemented using 5,504 sequences from 172 Parkinsonian patients and healthy controls. Results show a significant improvement in the accuracy of reconstructed eye movement sequences with respect to other state of the art techniques, substantially reducing the values for common time domain error metrics such as the mean absolute error, mean relative error, and root mean square error, while also preserving the signal's frequency domain characteristics. Moreover, it demonstrates robustness when large intervals of data are missing. This method offers an alternative solution for robustly handling missing data in time series, enhancing the reliability of smooth pursuit analysis for the screening and monitoring of neurodegenerative disorders.
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