UMass Chan Medical School Faculty Publications


Arrhythmia discrimination using a smart phone

UMMS Affiliation

Department of Medicine, Division of Cardiovascular Medicine

Publication Date


Document Type



Biomedical Devices and Instrumentation | Cardiovascular Diseases | Medical Biotechnology | Telemedicine


We hypothesize that our smartphone-based arrhythmia discrimination algorithm with data acquisition approach reliably differentiates between normal sinus rhythm (NSR), atrial fibrillation (AF), premature ventricular contractions (PVCs) and premature atrial contraction (PACs) in a diverse group of patients having these common arrhythmias. We combine root mean square of successive RR differences and Shannon entropy with Poincare plot (or turning point ratio method) and pulse rise and fall times to increase the sensitivity of AF discrimination and add new capabilities of PVC and PAC identification. To investigate the capability of the smartphone-based algorithm for arrhythmia discrimination, 99 subjects, including 88 study participants with AF at baseline and in NSR after electrical cardioversion, as well as seven participants with PACs and four with PVCs were recruited. Using a smartphone, we collected 2-min pulsatile time series from each recruited subject. This clinical application results show that the proposed method detects NSR with specificity of 0.9886, and discriminates PVCs and PACs from AF with sensitivities of 0.9684 and 0.9783, respectively.

DOI of Published Version



IEEE J Biomed Health Inform. 2015 May;19(3):815-24. doi: 10.1109/JBHI.2015.2418195. Epub 2015 Mar 31. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

IEEE journal of biomedical and health informatics

PubMed ID