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Advances in the Analysis of Electrocardiogram in Context of Mass Screening: Technological Trends and Application of AI Anomaly Detection

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Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning

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Abstract

Electrocardiography is still the most wide-spread method of functional diagnosis. The chapter has been targeted towards the debate on evolution and current attitude on the heart failure screening electrocardiography, reviewing the clinical practices of applying remote electrocardiogram (ECG) recording gadgets, the quantity and origin of data possible to be collected with ECG gadgets having various number of sensors using different modern methods of mathematical transformation of ECG signal, i.e. fourth generation ECG analysis. Accent has been made towards the application of the modern machine learning method – anomaly detection to heart activity analysis. Anomaly detection is one of the machine learning methods which identifies the data samples who deviate from some concept of normality. Such samples represent novelty, or outliers in the dataset, and often carry important information. As an example of application of anomaly detection in biomedical signal analysis, the problem of identifying the subtle deviations from the population norm based on the ECG is presented. The time-magnitude features derived from six leads of Signal Averaged ECG are used in the Isolation Forest anomaly (IFA) detector to quantify the distance of the single ECG from the cluster of normal controls. Input data to the IFA technique consists of diverse tree amounts as well as several pollution factors. For comparison, five different groups were examined: patients with proven coronary artery diseases, military personnel with mine-explosive injuries, COVID-19 survivors, and two subgroups involving participants of widespread-screening in one of the countryside areas in Ukraine.

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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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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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Chaikovsky, I., Popov, A. (2023). Advances in the Analysis of Electrocardiogram in Context of Mass Screening: Technological Trends and Application of AI Anomaly Detection. In: Qaisar, S.M., Nisar, H., Subasi, A. (eds) Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-031-23239-8_5

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