Hierarchical Deep Learning for Comprehensive Epileptic Seizure Analysis: From Detection to Fine-Grained Classification

Hierarchical Deep Learning for Comprehensive Epileptic Seizure Analysis: From Detection to Fine-Grained Classification

Epileptic seizure detection and classification from EEG recordings faces significant challenges due to extreme class imbalance. Analysis of the Temple University Hospital Seizure (TUSZ) dataset reveals imbalance ratios of 150:1 between common and rare seizure types, with high temporal heterogeneity...

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Bibliographic Details
Main Authors: Peter Akor, Godwin Enemali, Usman Muhammad, Rajiv Ranjan Singh, Hadi Larijani
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Information
Subjects:
imbalanced learning
cascaded neural networks
epileptic seizure classification
EEG analysis
hierarchical decomposition
class-weighted optimization
Online Access:https://www.mdpi.com/2078-2489/16/7/532
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