可解释的极流分解用于后半规管BPPV中扭转性眼震的侧别判定

文献信息

中文摘要

目的： Dix-Hallpike手法检查中出现的扭转性眼震是后半规管良性阵发性位置性眩晕（pc-BPPV）中一项关键的前庭眼反射（VOR）体征。本研究开发了一个可解释框架，用于从临床红外记录中自动分类扭转性眼震方向并对异常事件进行时间定位。
方法： 红外Dix-Hallpike视频通过You Only Look Once第8版nano（YOLOv8n）瞳孔定位、动态虹膜掩膜和循环全对场变换（RAFT）光流进行处理。将光流向量投影到以瞳孔为中心的极坐标中，以提取切向和径向运动特征。采用带注意力的双向门控循环单元（BiGRU-attention）网络进行片段级三分类，并通过滑动窗口进行患者级推断。
结果： 五折交叉验证获得片段级准确率94.19% ± 0.88%，三个类别的平衡F1分数均良好。在独立的45例患者长视频测试集上，患者级准确率在每秒60帧（fps）时达到97.78%，在30 fps时达到100%（45/45）。时间定位的平均交并比（IoU）分别为0.899和0.896，命中率分别为96.8%和100%。
结论： 该极流框架能够对pc-BPPV相关的扭转-上跳性眼震进行生理学上可解释且稳健的分类。通过将眼动明确分解为切向和径向分量，该框架提供了可靠的侧别判定，同时展示了在临床上对异常眼震事件进行有意义时间定位的潜力。

英文摘要

OBJECTIVE: Torsional nystagmus during the Dix-Hallpike maneuver represents a cardinal vestibulo-ocular reflex (VOR) sign in posterior-canal benign paroxysmal positional vertigo (pc-BPPV). This study develops an interpretable framework for automated classification of torsional nystagmus direction and temporal localization of abnormal events from clinical infrared recordings.
METHODS: Infrared Dix-Hallpike videos were processed via You Only Look Once version 8 nano (YOLOv8n) pupil localization, dynamic iris masking, and Recurrent All-Pairs Field Transforms (RAFT) optical flow. Flow vectors were projected into pupil-centered polar coordinates to extract tangential and radial motion features. A bidirectional gated recurrent unit with attention (BiGRU-attention) network performed segment-level three-class classification with sliding-window patient-level inference.
RESULTS: Five-fold cross-validation yielded segment-level accuracy of 94.19% ± 0.88% with balanced F1-scores across all three classes. On an independent 45-patient long-video test set, patient-level accuracy reached 97.78% at 60 frames per second (fps) and 100% (45/45) at 30 fps. Temporal localization achieved mean intersection-over-union (IoU) of 0.899 and 0.896, with hit rates of 96.8 and 100%, respectively.
CONCLUSION: The polar-flow framework enables physiologically interpretable and robust classification of pc-BPPV-related torsional-upbeat nystagmus. By explicitly decomposing eye movements into tangential and radial components, it provides reliable lateralization while demonstrating the potential for clinically meaningful temporal localization of abnormal nystagmus events.