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        <datestamp>2026-10-02T04:32:47Z</datestamp>
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          <dc:title>Video 3_Interpretable polar-flow decomposition for torsional nystagmus lateralization in posterior-canal BPPV.mp4</dc:title>
          <dc:creator>Yanjun Li (46396)</dc:creator>
          <dc:creator>Xiaomin Xu (1448977)</dc:creator>
          <dc:creator>Lidan Gao (12509671)</dc:creator>
          <dc:creator>Qiqi Shao (9693175)</dc:creator>
          <dc:creator>Jie Chen (5892)</dc:creator>
          <dc:creator>Wenhuan Wang (8488221)</dc:creator>
          <dc:creator>Xiaoqing Li (186909)</dc:creator>
          <dc:creator>Congcong Sun (300214)</dc:creator>
          <dc:creator>Shike Hou (515964)</dc:creator>
          <dc:creator>Xiaokai Yang (8488215)</dc:creator>
          <dc:subject>Neurology and Neuromuscular Diseases</dc:subject>
          <dc:subject>benign paroxysmal positional vertigo</dc:subject>
          <dc:subject>Dix–Hallpike maneuver</dc:subject>
          <dc:subject>explainable artificial intelligence</dc:subject>
          <dc:subject>nystagmus lateralization</dc:subject>
          <dc:subject>optical flow</dc:subject>
          <dc:subject>posterior semicircular canal</dc:subject>
          <dc:subject>torsional nystagmus</dc:subject>
          <dc:subject>vestibulo-ocular reflex</dc:subject>
          <dc:description>Objective&lt;p&gt;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.&lt;/p&gt;Methods&lt;p&gt;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.&lt;/p&gt;Results&lt;p&gt;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.&lt;/p&gt;Conclusion&lt;p&gt;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.&lt;/p&gt;</dc:description>
          <dc:date>2026-10-02T04:32:47Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Media</dc:type>
          <dc:identifier>10.3389/fneur.2026.1963383.s002</dc:identifier>
          <dc:relation>https://figshare.com/articles/media/Video_3_Interpretable_polar-flow_decomposition_for_torsional_nystagmus_lateralization_in_posterior-canal_BPPV_mp4/34053207</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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