Evaluating CNN Methods for Epileptic Seizure Type Classification Using EEG Data
Epilepsy impacts around 6.38 per 1,000 people globally, presenting diagnostic challenges due to the complexity of seizures. Accurate classification of seizure types via Electroencephalogram (EEG) is critical for effective treatment and enhancing patient quality of life. However, the intricate charac...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Instituto de Investigación Biomédica y Sanitaria de Alicante (ISABIAL) |
| Repositorio: | r-ISABIAL. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica y Sanitaria de Alicante |
| OAI Identifier: | oai:isabial.fundanetsuite.com:p10139 |
| Acceso en línea: | https://isabial.portalinvestigacion.com/publicaciones10139 https://ieeexplore.ieee.org/document/10540033 |
| Access Level: | acceso abierto |
| Palabra clave: | Electroencephalography Convolutional neural networks Epilepsy Brain modeling Feature extraction Support vector machines Deep learning deep learning electroencephalography epileptic seizure separable convolutions |
| Sumario: | Epilepsy impacts around 6.38 per 1,000 people globally, presenting diagnostic challenges due to the complexity of seizures. Accurate classification of seizure types via Electroencephalogram (EEG) is critical for effective treatment and enhancing patient quality of life. However, the intricate characteristics of EEG data necessitate expert interpretation, a process that is both time-intensive and susceptible to human error. Recent advancements in Deep Learning (DL) have shown promise in EEG analysis, offering new avenues for seizure type classification. This study introduces two innovative deep learning architectures designed for seizure type classification within a seven-class framework: Network 1D Raw, which applies 1D Convolutions to Raw EEG signals, and Network 2D Conv, utilizing 2D convolutions on pre-computed spectrograms. Both architectures employ Separable Convolutions to enhance feature extraction efficiency, with the Network 1D Raw also incorporating a dilation rate technique for expanded analysis. Tested on the Temple University Hospital Seizure (TUSZ) dataset, these methods are evaluated using inter-patient 3-fold cross-validation. The Network 1D Raw achieved a weighted f1-score of $0.611 \pm 0.037$ , while the Network 2D Conv reached $0.599 \pm 0.052$ , both surpassing existing benchmarks. The Network 1D Raw demonstrated superior classification for Absence Seizure (ABSZ), Focal Non-specific Seizures (FNSZ), and Generalized Non-specific Seizures types (GNSZ) with AUPRs of $0.9400 \pm 0.0500$ , $0.8100\pm 0.0300$ , and $0.5500\pm 0.1800$ , with Network 2D Conv excelling in FNSZ Seizures and GNSZ classes with AUPRs of $0.8100 \pm 0.1100$ and $0.4000 \pm 0.0200$ . Our DL models advance seizure type classification, blending efficiency with accuracy. Network 1D Raw's compact design suits low-resource environments, aiding quick, precise diagnoses. Future work will focus on expanding dataset diversity, particularly for underrepresented seizure types, to further refine classification performance. |
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