Paper Info
Title
Automatic Detection Of Atrial Fibrillation From Ballistocardiogram (Bcg) Using Wavelet Features And Machine Learning
Abstract
This paper presents an unobtrusive method for automatic detection of atrial fibrillation (AF) from single-channel ballistocardiogram (BCG) recordings during sleep. We developed a remote data acquisition system that measures BCG signals through an electromechanical-film sensor embedded into a bed's mattress and transmits the BCG data to a remote database on the cloud server. In the feasibility study, 12 AF patients' data were recorded during entire night of sleep. Each BCG recording was split into nonoverlapping 30s epochs labeled either AF or normal. Using the features extracted from stationary wavelet transform of these epochs, three popular machine learning classifiers (support vector machine, K-nearest neighbor, and ensembles) have been trained and evaluated on the set of 7816 epochs employing 30% hold-out validation. The results showed that all the trained classifiers could achieve an accuracy rate above 91.5%. The optimized ensembles model (Bagged Trees) could achieve accuracy, sensitivity, and specificity of 0.944, 0.970 and 0.891, respectively. These results suggest that the proposed BCG-based AF detection can be a potential initial screening and detection tool of AF in home-monitoring applications.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857059
2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Keywords
Field
DocType
Atrial fibrillation (AF), Ballistocardiogram (BCG), Machine Learning
Atrial fibrillation,Computer vision,Computer science,Support vector machine,Data acquisition,Feature extraction,Artificial intelligence,Stationary wavelet transform,Machine learning,Wavelet,Cloud computing
Conference
Volume
ISSN
Citations 
2019
1557-170X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Bin Yu115827.32
Biyong Zhang242.89
Lisheng Xu317839.09
Peng Fang400.34
Jun Hu531.83