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Die Rolle des EEG als Neuro-Marker für Patienten mit Depression: Ein systematischer Überblick

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Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML

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Zusammenfassung

Depressive Symptome können Gefühle der Melancholie, Interessenlosigkeit und Schwierigkeiten beim Erinnern und Konzentrieren umfassen. Die bestehenden Techniken zur Erkennung von Depressionen erfordern viel Interaktion mit Menschen, und die Ergebnisse sind stark abhängig vom Wissen und der Fähigkeit des Arztes, der sie durchführt. Die Elektroenzephalographie (EEG) ist ein potenzielles Werkzeug, das interessante Informationen liefert, die bei der Diagnose und Bewertung von Gehirnanomalien des Menschen mit ausgezeichneter Zeitauflösung verwendet werden können; jedoch stellt die Erkennung von Depressionen eine Herausforderung für Ingenieure und Wissenschaftler dar, um die personalisierte Gesundheitsversorgung zu unterstützen. Allerdings könnte das EEG einen Hinweis auf kognitiven Rückgang in Richtung Depressionsklassifikation liefern. Um einen neurophysiologischen Diagnoseindex für therapeutische Anwendungen zu erstellen, der empfindlich auf die Schwere der Depression reagiert, könnte es möglich sein, das EEG mit anderen biologischen, kognitiven Markern und Bildgebungsverfahren zu kombinieren. Das Ziel der aktuellen Studie ist es, die Grundaktivität des EEG bei depressiven Personen zu betonen, beginnend mit der Sammlung von EEG-Signalen und fortgesetzt durch EEG-Daten-Vorverarbeitungsschritte für die Signalverstärkung, lineare und nicht-lineare Eigenschaften. Der anschließende Fokus wird auf der Extraktion von EEG-Signalen liegen, um die großen Schwankungen der EEG-Signale zu berücksichtigen, gefolgt von Klassifizierungsansätzen zur Unterscheidung der Schwere der Depression. Daher hat die vorliegende Übersicht die Rolle der EEG-Signalverarbeitung und -analyse bei der Unterstützung von Ärzten und Klinikern bei der Bestimmung geeigneter Planungen und optimaler, effektiverer Behandlungsprogramme untersucht.

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  1. Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, University of Baghdad, Baghdad, Irak

    Noor Kamal Al-Qazzaz & Alaa A. Aldoori

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    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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Al-Qazzaz, N.K., Aldoori, A.A. (2024). Die Rolle des EEG als Neuro-Marker für Patienten mit Depression: Ein systematischer Überblick. 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_3

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