6th International Conference on Pervasive Computing Technologies for Healthcare

Research Article

A Risk and Incidence Based Atrial Fibrillation Detection Scheme for Wearable Healthcare Computing Devices

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  • @INPROCEEDINGS{10.4108/icst.pervasivehealth.2012.248670,
        author={Redjem Bouhenguel and Imad Mahgoub},
        title={A Risk and Incidence Based Atrial Fibrillation Detection Scheme for Wearable Healthcare Computing Devices},
        proceedings={6th International Conference on Pervasive Computing Technologies for Healthcare},
        publisher={IEEE},
        proceedings_a={PERVASIVEHEALTH},
        year={2012},
        month={7},
        keywords={algorithms classification arrhythmia atrial fibrillation wearable computing real-time monitoring logistic regression model of atrial fibrillation},
        doi={10.4108/icst.pervasivehealth.2012.248670}
    }
    
  • Redjem Bouhenguel
    Imad Mahgoub
    Year: 2012
    A Risk and Incidence Based Atrial Fibrillation Detection Scheme for Wearable Healthcare Computing Devices
    PERVASIVEHEALTH
    ICST
    DOI: 10.4108/icst.pervasivehealth.2012.248670
Redjem Bouhenguel1,*, Imad Mahgoub1
  • 1: Florida Atlantic University
*Contact email: redjemb@gmail.com

Abstract

Today small, battery-operated electrocardiograph devices, known as Ambulatory Event Monitors, are used to monitor the heart’s rhythm and activity. These on-body healthcare devices typically require a long battery life and moreover efficient detection algorithms. They need the ability to automatically assess atrial fibrillation (A-Fib) risk, and detect the onset of A-Fib from EKG recordings for further clinical diagnosis and treatment. The focus of this paper is the design of a real-time early detection algorithm cascaded with an A-Fib risk assessment algorithm. We compare accuracy of machine learning schemes such as J48, Naïve Bayes, and Logistic Regression and choose the best algorithm to classify A-Fib from EKG medical data. Though all three algorithms have similar accuracy, the Logistic Regression model is selected for its easy portability to mobile devices. A-Fib risk factor is used to determine a monitoring schedule where the detection algorithm is triggered by the age dependent A-Fib incidence rate inside a circadian prevalence window. The design may provide a great public health benefit by predicting A-Fib risk and detecting A-Fib in order to prevent strokes and heart attacks. It also shows promising results in helping meet the needs for energy efficient real-time A-Fib monitoring, detecting and reporting.