Detection of rhythmic discharges in newborn EEG signals.
This paper presents a scalp eletroencephalogram (EEG) rhythmic pattern detection scheme based on neural networks. rhythmic discharges detection is applicable to the majority of seizures seen in newborns, and is listed as detecting 90% of all the seizures. In this approach some features based on vari...
| Main Authors: | , , , |
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| Format: | Journal article |
| Jezik: | English |
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2006
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| _version_ | 1826264622616805376 |
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| author | Mohseni, H Mirghasemi, H Shamsollahi, M Zamani, MR |
| author_facet | Mohseni, H Mirghasemi, H Shamsollahi, M Zamani, MR |
| author_sort | Mohseni, H |
| collection | OXFORD |
| description | This paper presents a scalp eletroencephalogram (EEG) rhythmic pattern detection scheme based on neural networks. rhythmic discharges detection is applicable to the majority of seizures seen in newborns, and is listed as detecting 90% of all the seizures. In this approach some features based on various methods are extracted and compared by a modified multilayer neural network in order to find rhythmic discharges. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed. |
| first_indexed | 2024-03-06T20:10:48Z |
| format | Journal article |
| id | oxford-uuid:2a7eb629-a6d1-47f2-97cc-fc75b7297f03 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T20:10:48Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | oxford-uuid:2a7eb629-a6d1-47f2-97cc-fc75b7297f032022-03-26T12:25:21ZDetection of rhythmic discharges in newborn EEG signals.Journal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:2a7eb629-a6d1-47f2-97cc-fc75b7297f03EnglishSymplectic Elements at Oxford2006Mohseni, HMirghasemi, HShamsollahi, MZamani, MRThis paper presents a scalp eletroencephalogram (EEG) rhythmic pattern detection scheme based on neural networks. rhythmic discharges detection is applicable to the majority of seizures seen in newborns, and is listed as detecting 90% of all the seizures. In this approach some features based on various methods are extracted and compared by a modified multilayer neural network in order to find rhythmic discharges. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed. |
| spellingShingle | Mohseni, H Mirghasemi, H Shamsollahi, M Zamani, MR Detection of rhythmic discharges in newborn EEG signals. |
| title | Detection of rhythmic discharges in newborn EEG signals. |
| title_full | Detection of rhythmic discharges in newborn EEG signals. |
| title_fullStr | Detection of rhythmic discharges in newborn EEG signals. |
| title_full_unstemmed | Detection of rhythmic discharges in newborn EEG signals. |
| title_short | Detection of rhythmic discharges in newborn EEG signals. |
| title_sort | detection of rhythmic discharges in newborn eeg signals |
| work_keys_str_mv | AT mohsenih detectionofrhythmicdischargesinnewborneegsignals AT mirghasemih detectionofrhythmicdischargesinnewborneegsignals AT shamsollahim detectionofrhythmicdischargesinnewborneegsignals AT zamanimr detectionofrhythmicdischargesinnewborneegsignals |