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Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method

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Τίτλος: Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method
Συγγραφείς: Li, Peihao, Piliouras, Evangelos, Poghosyan, Vahe, AlHameed, Majed, Laleg-Kirati, Taous-Meriem
Πηγή: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Engineering in Medicine & Biology Society (EMBC), 2021 43rd Annual International Conference of the IEEE. :928-931 Nov, 2021
Relation: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Βάση Δεδομένων: IEEE Xplore Digital Library
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  Data: Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method
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  Data: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Engineering in Medicine & Biology Society (EMBC), 2021 43rd Annual International Conference of the IEEE. :928-931 Nov, 2021
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RecordInfo BibRecord:
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        Value: 10.1109/EMBC46164.2021.9631028
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      – TitleFull: Automatic Detection of Epileptiform EEG Discharges based on the Semi-Classical Signal Analysis (SCSA) method
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            NameFull: Li, Peihao
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            NameFull: Piliouras, Evangelos
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            NameFull: Poghosyan, Vahe
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            NameFull: AlHameed, Majed
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            NameFull: Laleg-Kirati, Taous-Meriem
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              Y: 2021
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