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  • This paper addresses automated classification of newborn sleep electroencephalogram (EEG) using hierarchical clustering. Newborn EEG plays an important role in determining the maturity level of neonatal brain. For accurate classification it is necessary to determine and/or calculate the most informative features. In our approach we use a method based on power spectral density (PSD) applied to each EEG channel. We also use features derived from EOG; EMG; ECG and PNG signals. The goal of the classifiers was to separate different classes of the PSG recording correctly (and minimize the classification error).
  • This paper addresses automated classification of newborn sleep electroencephalogram (EEG) using hierarchical clustering. Newborn EEG plays an important role in determining the maturity level of neonatal brain. For accurate classification it is necessary to determine and/or calculate the most informative features. In our approach we use a method based on power spectral density (PSD) applied to each EEG channel. We also use features derived from EOG; EMG; ECG and PNG signals. The goal of the classifiers was to separate different classes of the PSG recording correctly (and minimize the classification error). (en)
Title
  • Using Hierarchical Clustering for Newborn EEG Signal Classification
  • Using Hierarchical Clustering for Newborn EEG Signal Classification (en)
skos:prefLabel
  • Using Hierarchical Clustering for Newborn EEG Signal Classification
  • Using Hierarchical Clustering for Newborn EEG Signal Classification (en)
skos:notation
  • RIV/68407700:21460/09:00160966!RIV10-AV0-21460___
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  • P(1ET101210512)
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  • 347982
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  • RIV/68407700:21460/09:00160966
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  • hierarchical clustering; EEG signal; classification (en)
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  • [5F388F1DC46F]
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  • Praha
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  • Praha
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  • Workshop 09
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  • Krajča, Vladimír
  • Gerla, Václav
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number of pages
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  • České vysoké učení technické v Praze
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  • 978-80-01-04286-1
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  • 21460
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