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Verbesserung der Erkennung des Arbeitsgedächtnisses von Demenz-Patienten mithilfe von Entropie-basierten Merkmalen und dem Local Tangent Space Alignment Algorithmus

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Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML

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Zusammenfassung

Die Erkennung von Demenz stellt eine Barriere für die Weiterentwicklung der individualisierten Gesundheitsversorgung dar. Die nicht-lineare Natur von Elektroenzephalographie (EEG)-Signalen wurde mit Entropien charakterisiert. In einem Gedächtnistest (WM) wurden in dieser Studie die EEGs von 5 Patienten mit vaskulärer Demenz (VD), 15 Patienten mit schlaganfallbedingter leichter kognitiver Beeinträchtigung (SMCI) und 15 gesunden normalen Kontrollteilnehmern (NC) bewertet. Ein vierstufiger Rahmen für die automatische Identifizierung von Demenz wird bereitgestellt, wobei die erste Stufe die neu entwickelte automatische unabhängige Komponentenanalyse und Wavelet (AICA-WT)-Methode verwendet. In der zweiten Stufe wurden nicht-lineare Entropie-Merkmale mit Fuzzy-Entropie (FuzzEn), Fluktuations-basierte Dispersion-Entropie (FDispEn) und Bubble-Entropie (BubbEn) genutzt, um verschiedene dynamische Eigenschaften aus Mehrkanal-EEG-Signalen von Patienten mit Demenz zu extrahieren. Eine statistische Untersuchung der individuellen Leistung wurde mithilfe der Varianzanalyse (ANOVA) durchgeführt, um den Grad der EEG-Komplexität über die Gehirnregionen hinweg zu bestimmen. Danach wurde der nicht-lineare lokale Tangentenraum-Alignment (LSTA)-Dimensionsreduktionsansatz verwendet, um die automatische Diagnose von Demenz-Patienten zu verbessern. Mit k-nächsten Nachbarn (kNN), Support Vector Machine (SVM) und Entscheidungsbaum (DT)-Klassifikatoren wurde schließlich die Beeinträchtigung von Patienten nach einem Schlaganfall identifiziert. BubbEn wird ausgewählt, um einen neuen BubbEn-LTSA-Mapping-Prozess zu entwickeln, um das innovative AICA-WT-BubbEn-LTSA-Demenzerkennungs-Framework zu erstellen, das die Grundlage für eine automatisierte VD-Erkennung bildet.

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Authors and Affiliations

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

    Noor Kamal Al-Qazzaz

  2. Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia, UKM Bangi, Bangi, Selangor, Malaysia

    Sawal Hamid Bin Mohd Ali

  3. Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, UPM Serdang, Serdang, Selangor, Malaysia

    Siti Anom Ahmad

  4. Malaysian Research Institute of Ageing (MyAgeing™), Universiti Putra Malaysia, Serdang, Serdang, Selangor, Malaysia

    Siti Anom Ahmad

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  1. Noor Kamal Al-Qazzaz
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  3. Siti Anom Ahmad
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Correspondence to Noor Kamal Al-Qazzaz .

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  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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Al-Qazzaz, N.K., Ali, S.H.B.M., Ahmad, S.A. (2024). Verbesserung der Erkennung des Arbeitsgedächtnisses von Demenz-Patienten mithilfe von Entropie-basierten Merkmalen und dem Local Tangent Space Alignment Algorithmus. 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_14

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