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        <datestamp>2026-09-30T17:36:04Z</datestamp>
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          <dc:title>&lt;p&gt;Hypnograms over 8 hours comparing human expert annotations to our model predictions.&lt;/p&gt;</dc:title>
          <dc:creator>Amir Ali Vakili (25145354)</dc:creator>
          <dc:creator>Salar Jahanshiri (25145357)</dc:creator>
          <dc:creator>Armin Salimi-Badr (25145360)</dc:creator>
          <dc:subject>Space Science</dc:subject>
          <dc:subject>Neuroscience</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Information Systems not elsewhere classified</dc:subject>
          <dc:subject>Cancer</dc:subject>
          <dc:subject>work proposes temposleep</dc:subject>
          <dc:subject>widely available signal</dc:subject>
          <dc:subject>weighted loss functions</dc:subject>
          <dc:subject>scale feature extraction</dc:subject>
          <dc:subject>lacking clearly defined</dc:subject>
          <dc:subject>hierarchical sequence learning</dc:subject>
          <dc:subject>enhancing contextual representation</dc:subject>
          <dc:subject>scale temporal encoding</dc:subject>
          <dc:subject>proposed framework achieves</dc:subject>
          <dc:subject>automatic sleep staging</dc:subject>
          <dc:subject>address data imbalance</dc:subject>
          <dc:subject>challenging n1 stage</dc:subject>
          <dc:subject>aware temporal modeling</dc:subject>
          <dc:subject>temporal modeling</dc:subject>
          <dc:subject>aware framework</dc:subject>
          <dc:subject>n1 stage</dc:subject>
          <dc:subject>sleep disorders</dc:subject>
          <dc:subject>data augmentation</dc:subject>
          <dc:subject>field modeling</dc:subject>
          <dc:subject>unified multi</dc:subject>
          <dc:subject>substantial improvement</dc:subject>
          <dc:subject>study focuses</dc:subject>
          <dc:subject>stacked layers</dc:subject>
          <dc:subject>sleepedf datasets</dc:subject>
          <dc:subject>results indicate</dc:subject>
          <dc:subject>range dependencies</dc:subject>
          <dc:subject>providing insights</dc:subject>
          <dc:subject>previous methods</dc:subject>
          <dc:subject>prediction behavior</dc:subject>
          <dc:subject>potential application</dc:subject>
          <dc:subject>particular emphasis</dc:subject>
          <dc:subject>overall accuracy</dc:subject>
          <dc:subject>limited receptive</dc:subject>
          <dc:subject>insufficient interpretability</dc:subject>
          <dc:subject>improving detection</dc:subject>
          <dc:subject>healthcare due</dc:subject>
          <dc:subject>global prevalence</dc:subject>
          <dc:subject>final predictions</dc:subject>
          <dc:subject>epoch chunks</dc:subject>
          <dc:subject>eeg signals</dc:subject>
          <dc:subject>eeg ),</dc:subject>
          <dc:subject>critical task</dc:subject>
          <dc:subject>class imbalance</dc:subject>
          <dc:subject>channel electroencephalography</dc:subject>
          <dc:subject>black boxes</dc:subject>
          <dc:subject>46 %.</dc:subject>
          <dc:description>&lt;p&gt;The comparison of sleep stage sequences annotated by a human expert with those predicted by the proposed model across a complete overnight recording from a single subject in the SleepEDF-20 dataset.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-30T17:35:55Z</dc:date>
          <dc:type>Image</dc:type>
          <dc:type>Figure</dc:type>
          <dc:identifier>10.1371/journal.pone.0358241.g007</dc:identifier>
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