Title
Computationally efficient algorithm for photoplethysmography-based atrial fibrillation detection using smartphones
Abstract
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.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8036773
2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Keywords
Field
DocType
Algorithms,Atrial Fibrillation,Electrocardiography,Humans,Photoplethysmography,Smartphone
Atrial fibrillation,Heart Rhythm,Receiver operating characteristic,Signal acquisition,Computer science,Photoplethysmogram,Support vector machine,Algorithm,Electronic engineering,Speech recognition,Forward selection,Entropy (information theory)
Conference
Volume
ISSN
ISBN
2017
1557-170X
978-1-5090-2810-8
Citations 
PageRank 
References 
1
0.38
7
Authors
4
Name
Order
Citations
PageRank
Tim Schäck131.77
Yosef Safi Harb210.38
Michael Muma314419.51
Abdelhak M. Zoubir41036148.03