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A Feature-Enhanced Hybrid CNN-BiLSTM Framework for Multi-Label Classification of Pathological High-Frequency Oscillations in Intracranial EEG Signals

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Bibliographic Details
Published in:International Journal of Image, Graphics and Signal Processing
Format: Online Article RSS Article
Published: 2026
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container_title International Journal of Image, Graphics and Signal Processing
description
discipline_display Image and Video Processing
discipline_facet Image and Video Processing
format Online Article
RSS Article
genre Journal Article
id rss_article:68129
institution FRELIP
journal_source_facet International Journal of Image, Graphics and Signal Processing
last_indexed 2026-06-20T21:28:46.356Z
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle A Feature-Enhanced Hybrid CNN-BiLSTM Framework for Multi-Label Classification of Pathological High-Frequency Oscillations in Intracranial EEG Signals
Image and Video Processing
General
Image and Video Processing
sub_discipline_display General
sub_discipline_facet General
subject_display Image and Video Processing
General
Image and Video Processing
subject_facet Image and Video Processing
General
Image and Video Processing
title A Feature-Enhanced Hybrid CNN-BiLSTM Framework for Multi-Label Classification of Pathological High-Frequency Oscillations in Intracranial EEG Signals
title_alt Un marco híbrido CNN-BiLSTM mejorado con características para clasificación multietiqueta de oscilaciones de alta frecuencia patológicas en señales EEG intracraneales
Un cadre hybride CNN-BiLSTM à caractéristiques améliorées pour la classification multi-étiquettes des oscillations haute fréquence pathologiques dans les signaux EEG intracrâniens
Uma Estrutura Híbrida CNN-BiLSTM com Características Aprimoradas para Classificação Multirrótulo de Oscilações de Alta Frequência Patológicas em Sinais de EEG Intracraniano
title_auth A Feature-Enhanced Hybrid CNN-BiLSTM Framework for Multi-Label Classification of Pathological High-Frequency Oscillations in Intracranial EEG Signals
title_es_txt Un marco híbrido CNN-BiLSTM mejorado con características para clasificación multietiqueta de oscilaciones de alta frecuencia patológicas en señales EEG intracraneales
title_fr_txt Un cadre hybride CNN-BiLSTM à caractéristiques améliorées pour la classification multi-étiquettes des oscillations haute fréquence pathologiques dans les signaux EEG intracrâniens
title_full A Feature-Enhanced Hybrid CNN-BiLSTM Framework for Multi-Label Classification of Pathological High-Frequency Oscillations in Intracranial EEG Signals
title_fullStr A Feature-Enhanced Hybrid CNN-BiLSTM Framework for Multi-Label Classification of Pathological High-Frequency Oscillations in Intracranial EEG Signals
title_full_unstemmed A Feature-Enhanced Hybrid CNN-BiLSTM Framework for Multi-Label Classification of Pathological High-Frequency Oscillations in Intracranial EEG Signals
title_pt_txt Uma Estrutura Híbrida CNN-BiLSTM com Características Aprimoradas para Classificação Multirrótulo de Oscilações de Alta Frequência Patológicas em Sinais de EEG Intracraniano
title_short A Feature-Enhanced Hybrid CNN-BiLSTM Framework for Multi-Label Classification of Pathological High-Frequency Oscillations in Intracranial EEG Signals
title_sort a feature-enhanced hybrid cnn-bilstm framework for multi-label classification of pathological high-frequency oscillations in intracranial eeg signals
topic Image and Video Processing
General
Image and Video Processing
url https://www.mecs-press.org/ijigsp/ijigsp-v18-n3/v18n3-1.html