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Artificial Intelligence of Things for Early Detection of Cardiac Diseases

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Machine Learning for Critical Internet of Medical Things

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Abstract

Cardiovascular disease (CVD) is now the primary cause for morbidity and mortality around the world; with the substantial improvements in prognosis and care, the rate can be reduced to a large extent. However, conventional ECG disorder detection models display substantial quotes of misdiagnosis because of the restrictions of the skills of extracted features. So strategies and techniques should be devised for alertness and care to keep away from the unexpected death of humans with coronary heart diseases. This chapter addresses the control of cardiovascular diseases with well-timed prognosis detection, remedy, and monitoring. It is determined that the wearable devices are more efficient for handling situations of cardiovascular diseases. The study proposes the Artificial Intelligence of Things (AIoT) for ECG and cardiac disorder recognition. In this chapter, we detailed cloud-based artificial intelligence framework for atrial fibrillation which are recently used and in brief defined the experimentation result.

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Mar Baselios Christian College of Engineering and Technology, Kuttikanam, Kerala, India

    Tomina Thomas

  2. Department of Computer Science and Engineering, St. Joseph’s College of Engineering and Technology, Palai, Kerala, India

    Anna N. Kurian

Authors
  1. Tomina Thomas
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  2. Anna N. Kurian
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Editor information

Editors and Affiliations

  1. Artificial intelligence department, Near East University, Nicosia, Turkey

    Fadi Al-Turjman

  2. Graduate School, Duy Tan University, Da Nang, Vietnam

    Anand Nayyar

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Thomas, T., Kurian, A.N. (2022). Artificial Intelligence of Things for Early Detection of Cardiac Diseases. In: Al-Turjman, F., Nayyar, A. (eds) Machine Learning for Critical Internet of Medical Things. Springer, Cham. https://doi.org/10.1007/978-3-030-80928-7_4

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  • DOI: https://doi.org/10.1007/978-3-030-80928-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80927-0

  • Online ISBN: 978-3-030-80928-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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