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Biomedizinische Signalverarbeitung für die automatisierte Erkennung von Schlafarousals, basierend auf Multi-Physiologischen Signalen mit Ensemble-Lernmethoden

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Fortschritte in der nicht-invasiven biomedizinischen Signalverarbeitung mit ML

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Zusammenfassung

Schlafbezogene Atmungsstörungen wie Schlafapnoe und Hypopnoe sind potenziell ernste Störungen und können die Ursache für eine Vielzahl von körperlichen und geistigen Gesundheitsproblemen sein und zudem die Lebensqualität reduzieren. Daher sind Schlafstudien unerlässlich zur Identifizierung und Behandlung dieser Schlafstörungen. Diese Studie zielt darauf ab, Erregungsregionen, die durch nicht-apnoeischen und nicht-hypopnoeischen Schlaf in Polysomnographie-Signalen verursacht werden, mithilfe von Ensemble-Techniken zu erkennen. Der in dieser Studie verwendete Datensatz bezieht sich auf Polysomnographie-Messkanäle von 100 Patienten, die in der Physionet Challenge-Datenbank von 2018 bereitgestellt wurden. Die Daten wurden in kleine Epochen mit 50 % Überlappung aufgeteilt. Aus jeder Epoche wurden mehrere unterschiedliche Merkmale im Zeit- und Frequenzbereich extrahiert. Der Wilcoxon-Rangsummentest und der genetische Algorithmus-Optimierungsalgorithmus wurden verwendet, um einen Satz von Merkmalen mit den meisten diskriminierenden Informationen zu finden. Eine Technik zur Datenvermehrung wurde verwendet, um das Problem der unausgeglichenen Daten anzugehen. Für die endgültige Klassifikation wurden lineare Diskriminanzanalyse, logistische Regression, bagged tree aus der Bagging-Technik und LightGBM aus der Boosting-Methode angewendet. Basierend auf den Physionet Challenge-Indizes, der Fläche unter der Receiver-Operating-Characteristic-Kurve (AUROC) und der Fläche unter der Precision-Recall-Kurve (AUPRC), verglichen wir die Leistung der Klassifikatoren auf diesem Datensatz. Die höchste Leistung bei 20 Testpersonen betrug 0,497 für AUPRC und 0,878 für AUROC.

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Authors and Affiliations

  1. Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Iran

    Navabeh Sadat Jalili Shani & Mohammad Hasan Moradi

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  1. Navabeh Sadat Jalili Shani
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  2. Mohammad Hasan Moradi
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Correspondence to Mohammad Hasan Moradi .

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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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Jalili Shani, N.S., Moradi, M.H. (2024). Biomedizinische Signalverarbeitung für die automatisierte Erkennung von Schlafarousals, basierend auf Multi-Physiologischen Signalen mit Ensemble-Lernmethoden. 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_11

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