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Fortschritte in der Analyse des Elektrokardiogramms im Kontext des Massenscreenings: Technologische Trends und Anwendung der KI-Anomalieerkennung

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Zusammenfassung

Die Elektrokardiographie ist nach wie vor die am weitesten verbreitete Methode der Funktionsdiagnose. Das Kapitel richtet sich an die Debatte über die Entwicklung und die aktuelle Einstellung zur Herzinsuffizienz-Screening-Elektrokardiographie und überprüft die klinischen Praktiken der Anwendung von Fern-Elektrokardiogramm (EKG)-Aufzeichnungsgeräten, die Menge und Herkunft der Daten, die mit EKG-Geräten mit verschiedenen Anzahl von Sensoren unter Verwendung verschiedener moderner Methoden der mathematischen Transformation des EKG-Signals, d. h. der vierten Generation der EKG-Analyse, gesammelt werden können. Der Schwerpunkt liegt auf der Anwendung der modernen Methode des maschinellen Lernens – Anomalieerkennung zur Herzaktivitätsanalyse. Die Anomalieerkennung ist eine der Methoden des maschinellen Lernens, die jene Datensätze identifiziert, die von einem Konzept der Normalität abweichen. Solche Proben stellen Neuheiten oder Ausreißer in der Datensatz dar und enthalten oft wichtige Informationen. Als Beispiel für die Anwendung der Anomalieerkennung in der biomedizinischen Signalanalyse wird das Problem der Identifizierung der subtilen Abweichungen von der Bevölkerungsnorm auf der Basis des EKG vorgestellt. Die Zeit-Magnitude-Merkmale, die aus sechs Leitungen des Signal Average EKG abgeleitet sind, werden im Isolation Forest Anomaly (IFA) Detektor verwendet, um die Entfernung des einzelnen EKG von der Gruppe der normalen Kontrollen zu quantifizieren. Die Eingangsdaten zur IFA-Technik bestehen aus verschiedenen Baumhöhen sowie mehreren Verschmutzungsfaktoren. Zum Vergleich wurden fünf verschiedene Gruppen untersucht: Patienten mit nachgewiesenen koronaren Herzkrankheiten, Militärpersonal mit Minenexplosionsverletzungen, COVID-19-Überlebende und zwei Untergruppen, die Teilnehmer eines weit verbreiteten Screenings in einem der ländlichen Gebiete der Ukraine betreffen.

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Notes

  1. 1.

    Die in diesem Buch zu findende Bezeichnung der „Rasse“ bzw. „rassisch“ gibt das im Amerikanischen vorherrschende Konzept von „race“ wieder: Gemeint ist die Einteilung der US-amerikanischen Bevölkerung nach geographischer Herkunft seiner Vorfahren wie Schwarze/Afroamerikaner, Weiße/Amerikaner europäischer Abstammung, Lateinamerikaner, Asiaten. In diesem Buch ist der Begriff „Rasse“ ausschließlich in dieser Bedeutung zu verstehen.

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Authors and Affiliations

  1. Glushkov Institute of Cybernetics of National Academy of Science of Ukraine, Kyiv, Ukraine

    Illya Chaikovsky

  2. Electronic Engineering Department, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine

    Anton Popov

  3. Faculty of Applied Sciences, Ukrainian Catholic University, Lviv, Ukraine

    Anton Popov

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  1. Illya Chaikovsky
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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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Chaikovsky, I., Popov, A. (2024). Fortschritte in der Analyse des Elektrokardiogramms im Kontext des Massenscreenings: Technologische Trends und Anwendung der KI-Anomalieerkennung. 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_5

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