Abstract:
Atrial Fibrillation (AF) with high mortality rate needs to be monitored and detected accurately.
To record Photoplethysmography (PPG) signal, wrist-watches are used. The PPG signal gets
corrupted by motion and noise artifacts during monitoring, and is detected using proposed Motion
and Noise artifact (MNA) algorithm based on accelerometer reading and Time-Frequency spectra
(TFS) of PPG signal. Since AF is indicated as varying pulse-to-pulse intervals in a PPG signal,
AF detection is made using root mean square of successive differences and sample entropy
features, discriminating AF from Normal Sinus Rhythm (NSR). The presence of Premature Atrial
and Ventricular Contraction (PAC/PVC) in subjects due to its randomness may also lead to false
AF detection. The poincaré plot based PAC/PVC detection implemented in this research not only
separates PAC/PVC from NSR and AF but also improves the accuracy of AF and NSR detection.
The proposed Convolutional Neural Network (CNN) on Time-Frequency spectra (TFS)
ultimately proves to give better results for classification of PPG signal into AF, NSR and
PAC/PVC. The results for training and testing are validated on University of Massachusetts
Medical Center (UMMC) Simband and Medical Information Mart for Intensive Care III databases
with higher accuracy, sensitivity and specificity values.
