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Multimodal Classification of Epileptic Seizures based on Systematic Features Extraction from Brain and Motoric Signals

(Autor)

Buch | Softcover
204 Seiten
2025
Universitätsverlag Chemnitz
978-3-96100-238-2 (ISBN)

Lese- und Medienproben

Multimodal Classification of Epileptic Seizures based on Systematic Features Extraction from Brain and Motoric Signals - Achraf Djemal
CHF 26,45 inkl. MwSt
Epileptic seizures result from abnormal brain activity and involve both electrical and biomechanical signals. Accurate seizure classification is essential for clinical decision-making, yet conventional diagnostic methods, including self-reports and video monitoring, are limited in detecting seizure types. To overcome these limitations, this study investigates a multimodal approach combining electroencephalography (EEG), surface electromyography (sEMG), and inertial measurement unit (IMU) sensors. A synchronized compact wireless system was developed to capture the modalities, ensuring precise recording and analysis. A systematic signal processing pipeline was applied, including artifact removal, feature extraction, selection, evaluation, and machine learning-based classification. First, each modality was tested individually to assess its potential in seizure classification. The results revealed that a single modality was insufficient, with a maximum accuracy of 94%, highlighting the challenge of seizure similarity. To further explore multimodal classification, validation was conducted in a hospital setting. The results demonstrate that using independent component analysis (ICA) for preprocessing, feature selection techniques based on radar plots, distance metrics, and Big O notation, combined with the XGBoost classifier, led to a classification accuracy of 99%. These findings confirm that EEG, sEMG, and IMU complement each other, significantly enhancing seizure classification.
Erscheinungsdatum
Reihe/Serie Scientific Reports on Measurement and Sensor Technology ; 31
Verlagsort Chemnitz
Sprache englisch
Maße 148 x 210 mm
Gewicht 303 g
Themenwelt Technik Elektrotechnik / Energietechnik
Schlagworte epileptischer Anfall • Maschinelles Lernen • Muskelfunktionsprüfung • Signalverarbeitung • Systementwurf
ISBN-10 3-96100-238-X / 396100238X
ISBN-13 978-3-96100-238-2 / 9783961002382
Zustand Neuware
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