University of Massachusetts Medical School Faculty Publications


Atrial fibrillation detection using an iPhone 4S

UMMS Affiliation

Division of Cardiovascular Medicine, Department of Medicine

Publication Date


Document Type



Biomedical Engineering and Bioengineering | Cardiology | Cardiovascular Diseases | Telemedicine


Atrial fibrillation (AF) affects three to five million Americans and is associated with significant morbidity and mortality. Existing methods to diagnose this paroxysmal arrhythmia are cumbersome and/or expensive. We hypothesized that an iPhone 4S can be used to detect AF based on its ability to record a pulsatile photoplethysmogram signal from a fingertip using the built-in camera lens. To investigate the capability of the iPhone 4S for AF detection, we first used two databases, the MIT-BIH AF and normal sinus rhythm (NSR) to derive discriminatory threshold values between two rhythms. Both databases include RR time series originating from 250 Hz sampled ECG recordings. We rescaled the RR time series to 30 Hz so that the RR time series resolution is 1/30 (s) which is equivalent to the resolution from an iPhone 4S. We investigated three statistical methods consisting of the root mean square of successive differences (RMSSD), the Shannon entropy (ShE) and the sample entropy (SampE), which have been proved to be useful tools for AF assessment. Using 64-beat segments from the MIT-BIH databases, we found the beat-to-beat accuracy value of 0.9405, 0.9300, and 0.9614 for RMSSD, ShE, and SampE, respectively. Using an iPhone 4S, we collected 2-min pulsatile time series from 25 prospectively recruited subjects with AF pre- and postelectrical cardioversion. Using derived threshold values of RMSSD, ShE and SampE from the MIT-BIH databases, we found the beat-to-beat accuracy of 0.9844, 0.8494, and 0.9522, respectively. It should be recognized that for clinical applications, the most relevant objective is to detect the presence of AF in the data. Using this criterion, we achieved an accuracy of 100% for both the MIT-BIH AF and iPhone 4S databases.


Atrial fibrillation (AF), cardioversion, iPhone, RR time series, root mean square of successive differences (RMSSD), sample entropy (SampE), Shannon entropy (ShE), smartphone

DOI of Published Version



Lee J, Reyes BA, McManus DD, Maitas O, Chon KH. Atrial fibrillation detection using an iPhone 4S. IEEE Trans Biomed Eng. 2013 Jan;60(1):203-6. doi: 10.1109/TBME.2012.2208112. Epub 2012 Jul 31. Erratum in: IEEE Trans Biomed Eng. 2014 Jun;61(6):1914. Mathias, Oscar [corrected to Maitas, Oscar]. PMID: 22868524. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

IEEE transactions on bio-medical engineering

PubMed ID