Open source Java-based ECG analysis software and Android app for Atrial Fibrillation screening

The development of mHealth applications could facilitate the decrease of the healthcare costs in both high income and low to middle income regions. However, it is essential that mHealth software is validated on public databases. Moreover, public scrutiny of the algorithms is likely to lead to faster and lower cost innovation. In this paper, we therefore present a novel Java-based Android application offering advanced Electrocardiogram (ECG) processing techniques, including signal quality analysis and Atrial Fibrillation (AF) screening.

[1]  G. Clifford,et al.  Wireless technology in disease management and medicine. , 2012, Annual review of medicine.

[2]  M. Ezekowitz,et al.  2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines and the Heart Rhythm Society. , 2014, Circulation.

[3]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[4]  Qiao Li,et al.  ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction , 2013, IEEE Transactions on Biomedical Engineering.

[5]  Samuel Wann,et al.  [Guidelines for the management of patients with atrial fibrillation. Executive summary]. , 2006, Revista espanola de cardiologia.

[6]  Gari D. Clifford,et al.  Signal processing methods for heart rate variability , 2002 .

[7]  Willis J. Tompkins,et al.  Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database , 1986, IEEE Transactions on Biomedical Engineering.

[8]  Silvia G. Priori,et al.  ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association task force on practice guidelines and the European society of cardiology committee for PRAC , 2006 .

[9]  L. Sörnmo,et al.  Delineation of the QRS complex using the envelope of the e.c.g. , 1983, Medical and Biological Engineering and Computing.

[10]  George B. Moody,et al.  A robust open-source algorithm to detect onset and duration of QRS complexes , 2003, Computers in Cardiology, 2003.

[11]  Ikaro Silva,et al.  Improving the quality of ECGs collected using mobile phones: The PhysioNet/Computing in Cardiology Challenge 2011 , 2011, 2011 Computing in Cardiology.

[12]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[13]  J. R. Moorman,et al.  Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. , 2011, American journal of physiology. Heart and circulatory physiology.

[14]  Qiao Li,et al.  Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach , 2014, IEEE Transactions on Biomedical Engineering.

[15]  Adrian Burns,et al.  SHIMMER™ – A Wireless Sensor Platform for Noninvasive Biomedical Research , 2010, IEEE Sensors Journal.