Smartphone app for atrial fibrillation
WORCESTER, MASSACHUSETTS. The gold standard for detecting atrial fibrillation (AF) is the 12-lead electrocardiogram (ECG). Whilst this is an excellent choice for AF diagnosis in a hospital or clinical setting, it is not practical for afibbers who wish to determine whether or not they are actually experiencing an episode at a particular point in time.
A team of electrophysiologists, engineers, and mathematicians associated with the University of Massachusetts and Worcester Polytechnic Institute now propose that a smartphone can be used to quickly and accurately determine whether an afibber is in AF or in normal sinus rhythm (NSR). The user places their right index or second finger on the smartphone camera for 2 minutes. Applications software built into the phone uses some fairly complicated algorithms (root mean square of successive RR differences and Shannon entropy) to analyse the pulse waves picked up by the camera and then displays pulse rate and indicates whether one is in AF or NSR.
The team tested their idea on 76 persistent afibbers scheduled for cardioversion using an iPhone 4S. The average age of the study participants was 65 years, 59% were male, and 40% had either coronary artery disease or heart failure. Clinical characteristics and algorithm means before and after cardioversion were as follows:
A team of electrophysiologists, engineers, and mathematicians associated with the University of Massachusetts and Worcester Polytechnic Institute now propose that a smartphone can be used to quickly and accurately determine whether an afibber is in AF or in normal sinus rhythm (NSR). The user places their right index or second finger on the smartphone camera for 2 minutes. Applications software built into the phone uses some fairly complicated algorithms (root mean square of successive RR differences and Shannon entropy) to analyse the pulse waves picked up by the camera and then displays pulse rate and indicates whether one is in AF or NSR.
The team tested their idea on 76 persistent afibbers scheduled for cardioversion using an iPhone 4S. The average age of the study participants was 65 years, 59% were male, and 40% had either coronary artery disease or heart failure. Clinical characteristics and algorithm means before and after cardioversion were as follows:
* RMSSD/mean = root mean square of successive RR difference The algorithm combining RMSSD/mean and Shannon entropy was 100% accurate in diagnosing irregular pulse and 96% accurate in diagnosing NSR. The researchers conclude that the iPhone 4S equipped with the new app reliably distinguishes an irregular pulse (AF) from pulse wave forms obtained during NSR.
McManus, DD, et al. A novel application for the detection of irregular pulse using an iPhone 4S in patients with atrial fibrillation. Heart Rhythm, Vol. 10, March 2013, pp. 315-19
McManus, DD, et al. A novel application for the detection of irregular pulse using an iPhone 4S in patients with atrial fibrillation. Heart Rhythm, Vol. 10, March 2013, pp. 315-19
The AFIB Report, Forum and Database do not provide medical advice. Do not attempt self-diagnosis or self-treatment based on our reports.
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