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Überblick über die Klassifizierung von EEG-Signalen mit maschinellem Lernen und Deep-Learning-Techniken

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Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML

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Zusammenfassung

Elektroenzephalographie-Signale (EEG-Signale) werden weit verbreitet für die Prognose und Diagnose mehrerer Störungen verwendet, wie zum Beispiel Epilepsie, Schizophrenie, Parkinson-Krankheit usw. Es wurde in der Literatur gezeigt, dass EEG-Signale mit maschinellen Lernverfahren funktionieren. Sie erfordern jedoch eine manuelle Extraktion von Merkmalen im Voraus, die von Datensatz zu Datensatz oder je nach Krankheitsanwendung variieren können. Tiefes Lernen hat andererseits die Fähigkeit, die rohen Signale zu verarbeiten und Daten zu klassifizieren, ohne dass Fachwissen oder manuell extrahierte Merkmale erforderlich sind, es fehlt jedoch ein gutes Verständnis und Interpretierbarkeit. In diesem Kapitel werden verschiedene Techniken des maschinellen Lernens diskutiert, einschließlich Methoden zur Extraktion und Auswahl von Merkmalen aus gefilterten Signalen und zur Klassifizierung dieser ausgewählten Merkmale für klinische Anwendungen. Wir haben auch zwei Fallstudien besprochen, d.h. die Erkennung von Epilepsie und Schizophrenie. Diese Fallstudien verwenden eine Architektur, die tiefes Lernen mit traditionellen ML-Techniken kombiniert und deren Ergebnisse vergleicht. Mit diesem hybriden Modell wird eine Genauigkeit von 94,9 % auf der Grundlage von EEG-Signalen von epileptischen und normalen Probanden erreicht, während eine Genauigkeit von 98 % bei der Erkennung von Schizophrenie mit nur drei EEG-Kanälen erreicht wird. Das letztere Ergebnis ist bedeutend, da es mit anderen modernen Techniken vergleichbar ist, während weniger Daten und Rechenleistung benötigt werden.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, GIK Institute, Topi, Pakistan

    Fatima Hassan & Syed Fawad Hussain

  2. School of Computer Science, University of Birmingham, Birmingham, UK

    Syed Fawad Hussain

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  1. Fatima Hassan
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Correspondence to Syed Fawad Hussain .

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Editors and Affiliations

  1. Electrical and Computer Engineering Department, Effat University, Jeddah, Saudi Arabia

    Saeed Mian Qaisar

  2. Department of Electronic Engineering Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar, Malaysia

    Humaira Nisar

  3. Institute of Biomedicine Faculty of Medicine, University of Turku, Turku, Finland

    Abdulhamit Subasi

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Hassan, F., Hussain, S.F. (2024). Überblick über die Klassifizierung von EEG-Signalen mit maschinellem Lernen und Deep-Learning-Techniken. In: Qaisar, S.M., Nisar, H., Subasi, A. (eds) Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML. Springer Vieweg, Cham. https://doi.org/10.1007/978-3-031-52856-9_7

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