“Atrial fibrillation (AF) is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity and the most common type of arrhythmia. Its diagnosis and the initiation of treatment, however, currently requires electrocardiogram (ECG)-based heart rhythm monitoring. The photoplethysmogram (PPG) offers an alternative method, which is convenient in terms of its recording and allows for self-monitoring, thus relieving clinical staff and enabling early AF diagnosis. We introduce a PPG-based AF detection algorithm using smartphones that has a low computational cost and low memory requirements. In particular, we propose a modified PPG signal acquisition, explore new statistical discriminating features and propose simple classification equations by using sequential forward selection (SFS) and support vector machines (SVM). The algorithm is applied to clinical data and evaluated in terms of receiver operating characteristic (ROC) curve and statistical measures. The combination of Shannon entropy and the median of the peak rise height achieves perfect detection of AF on the recorded data, highlighting the potential of PPG for reliable AF detection.
Atrial fibrillation (AF) is the most common type of arrhythmia and one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity in the world . In 2010, it was estimated that 33.5 million people world-wide have AF  and, according to , almost one in four middle-aged adults in the US and Europe will develop AF. However, undiagnosed AF is common, especially in older populations and in patients with heart failure  and effective AF treatments could reduce risk for complications from AF. Thus, a main challenge is the early and reliable detection of AF. Currently, the AF diagnosis requires heart rhythm monitoring with an electrocardiogram (ECG) to reveal irregular heart beat patterns.”