Open AccessA Comparative Evaluation of Atrial Fibrillation Detection Methods in Koreans Based on Optical Recordings Using a Smartphone
Author(s) -
Keonsoo Lee,
Hyung Oh Choi,
Se Dong Min,
Jinseok Lee,
Brij B. Gupta,
Yunyoung Nam
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2700488
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
This paper evaluated three methods of atrial fibrillation (AF) detection in Korean patients using 149 records of photoplethysmography signals from 148 participants: the k-nearest neighbor (kNN), neural network (NN), and support vector machine (SVM) methods. The 149 records are preprocessed to calculate the root-mean square of the successive differences in the R-R intervals and Shannon entropy which are validated from x-means and Massachusetts Institute of Technology and Beth Israel Hospital database for the features for AF detection. A smartphone camera was used to obtain photoplethysmography signals. Clinicians labeled 29 records by referring to the electrocardiogram signals. These labeled records were used as a ground truth set to evaluate the accuracy of each method. In the experiments, the kNN, NN, and SVM methods achieved 98.65%, 99.32%, and 97.98% accuracies, respectively.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom