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Part 1: Document Description
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Citation |
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Title: |
Subject-wise Sleep Stage Data |
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Identification Number: |
doi:10.21979/N9/UD1IM9 |
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Distributor: |
DR-NTU (Data) |
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Date of Distribution: |
2022-05-27 |
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Version: |
1 |
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Bibliographic Citation: |
Ragab, Mohamed; Eldele, Emadeldeen, 2022, "Subject-wise Sleep Stage Data", https://doi.org/10.21979/N9/UD1IM9, DR-NTU (Data), V1 |
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Citation |
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Title: |
Subject-wise Sleep Stage Data |
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Identification Number: |
doi:10.21979/N9/UD1IM9 |
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Authoring Entity: |
Ragab, Mohamed (Nanyang Technological University) |
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Eldele, Emadeldeen (Nanyang Technological University) |
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Software used in Production: |
Python |
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Distributor: |
DR-NTU (Data) |
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Access Authority: |
Ragab, Mohamed |
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Depositor: |
Ragab, Mohamed |
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Date of Deposit: |
2022-03-06 |
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Holdings Information: |
https://doi.org/10.21979/N9/UD1IM9 |
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Study Scope |
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Keywords: |
Computer and Information Science, Engineering, Computer and Information Science, Engineering, Domain Adaptation, Sleep Stages, EEG |
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Abstract: |
Sleep stage classification (SSC) problem aims to classify the electroencephalography (EEG) signals into five stages i.e. Wake (W), Non-Rapid Eye Movement stages (N1, N2, N3), and Rapid Eye Movement (REM). We adopted Sleep-EDF dataset (Goldberger et al., 2000), which contains EEG readings from 20 healthy subjects |
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Kind of Data: |
EEG data |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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dhedfreader.py |
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text/x-python |
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generate_train_val_test.py |
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preprocess_sleep_edf.py |
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text/x-python |
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