Predictive diagnosis of fatal heart rhythms using wearables

Jeno I. Szep, Salim Hariri, Zain I Khalpey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Sudden cardiac death causes more than 300,000 deaths annually in the US. Our research goal is to develop a continuous cardiac monitoring system that utilizes current wearable devices and is capable of not just detecting arrhythmia but also to predict life-threatening arrhythmia a few minutes before it would actually happen. The monitoring system should provide a diagnosis based on analyzing a few-minutes of heart-rate data streams. In order to verify the feasibility of this approach, we have developed a prototype and evaluated its capabilities. The prototype is based on a two-tier data analytics approach and utilizes multiple gradient boosting machine learning models. The system was tested for predicting four different life-threatening arrhythmias solely on realistic heart-rate readings and also tested the atrial fibrillation recognition capability. The prototype scored 91.6% and 93.9% accuracy respectively. These preliminary results validate the feasibility of our approach to predict arrhythmia in real-time from heart-rate observations.

Original languageEnglish (US)
Title of host publication2019 Spring Simulation Conference, SpringSim 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781510883888
StatePublished - Apr 2019
Event2019 Spring Simulation Conference, SpringSim 2019 - Tucson, United States
Duration: Apr 29 2019May 2 2019

Publication series

Name2019 Spring Simulation Conference, SpringSim 2019


Conference2019 Spring Simulation Conference, SpringSim 2019
Country/TerritoryUnited States


  • Heart Monitoring
  • Machine Learning
  • Prediction of Arrhythmia
  • Smartwatch

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization
  • Modeling and Simulation


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