IoT-Based Cardiac Arrest Prediction Through Heart Variability Analysis

  • Santosh KumarEmail author
  • Usha Manasi Mohapatra
  • Debabrata Singh
  • Dilip Kumar Choubey
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1089)


Current machine learning methods for sudden cardiac arrest have not been tested against physically active heart rates. Developments in wearable technology and advancements in non-intrusive heart rate monitors may allow for a future where people can stream their heart rate readings, with the readings automatically analyzed by robust machine learning algorithms which will alert cardiac arrest risk. This paper presents a new sudden cardiac arrest prediction technique, a random forest classifier implementation, a prospective physical activity heart rate dataset, and an Internet of things solution toward heart rate monitoring and sudden cardiac arrest warning. In this paper, five minutes advance warning is provided with 97.03% accuracy and a 0.9485 F-score for the classification of sudden cardiac arrest prediction. The result shows the efficiency of our method compared to other existing methods.


Heart rate variability (HRV) Random forest Heart rate monitoring band (HRM) IoT 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Santosh Kumar
    • 1
    Email author
  • Usha Manasi Mohapatra
    • 1
  • Debabrata Singh
    • 1
  • Dilip Kumar Choubey
    • 2
  1. 1.Department of CSITInstitute of Technical Education and Research S‘O’A Deemed to be UniversityBhubaneswarIndia
  2. 2.Department of Computer Science & Engineering National Institute of TechnologyPatnaIndia

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