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A risk and Incidence Based Atrial Fibrillation Detection Scheme for wearable healthcare computing devices

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2 Author(s)
Bouhenguel, R. ; Dept. of Comput., Electr. Eng. & Comput. Sci., Florida Atlantic Univ., Boca Raton, FL, USA ; Mahgoub, I.

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.

Published in:

Pervasive Computing Technologies for Healthcare (PervasiveHealth), 2012 6th International Conference on

Date of Conference:

21-24 May 2012