TY - GEN
T1 - Automated screening of multiple cardiac arrhythmias using perceptual color representation-based intelligent classifier
AU - Lin, Chia Hung
AU - Kan, Chung Dann
AU - Chen, Pi Yun
AU - Lai, Hsiang Yueh
AU - Chen, Wei Ling
AU - Kuo, Ying Che
N1 - Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/2/19
Y1 - 2019/2/19
N2 - Cardiac arrhythmias are time-varying, non-period, irregular, or complex fluctuations in time-domain waveforms, revealing multiple morphologies, including supraventricular, junctional, and ventricular arrhythmias, and conduction abnormalities. Given that visual examinations are time-consuming, this study proposes a screening method for identifying the classes of multiple cardiac arrhythmias using the discrete fractional-order integration (DFOI) and the perceptual color representation-based intelligent classifier. The DFOI operation with the specific fractional order is employed to extract the QRS features with the finite computations and finite power series in a specific timing duration. DFOI can deal with the non-period and irregular time-varying signals. The proposed intelligent classifier consists of Bayesian network and color relation analysis (CRA). This classifier is employed to identify the ectopic classes with perceptual color representation. Using the arrhythmia database of Massachusetts Institute of Technology-Beth Israel Hospital database, the proposed intelligent classifier demonstrated high efficiency in recognizing electrocardiography signals.
AB - Cardiac arrhythmias are time-varying, non-period, irregular, or complex fluctuations in time-domain waveforms, revealing multiple morphologies, including supraventricular, junctional, and ventricular arrhythmias, and conduction abnormalities. Given that visual examinations are time-consuming, this study proposes a screening method for identifying the classes of multiple cardiac arrhythmias using the discrete fractional-order integration (DFOI) and the perceptual color representation-based intelligent classifier. The DFOI operation with the specific fractional order is employed to extract the QRS features with the finite computations and finite power series in a specific timing duration. DFOI can deal with the non-period and irregular time-varying signals. The proposed intelligent classifier consists of Bayesian network and color relation analysis (CRA). This classifier is employed to identify the ectopic classes with perceptual color representation. Using the arrhythmia database of Massachusetts Institute of Technology-Beth Israel Hospital database, the proposed intelligent classifier demonstrated high efficiency in recognizing electrocardiography signals.
UR - http://www.scopus.com/inward/record.url?scp=85063197870&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85063197870&partnerID=8YFLogxK
U2 - 10.1109/IS3C.2018.00058
DO - 10.1109/IS3C.2018.00058
M3 - Conference contribution
AN - SCOPUS:85063197870
T3 - Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018
SP - 201
EP - 204
BT - Proceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Symposium on Computer, Consumer and Control, IS3C 2018
Y2 - 6 December 2018 through 8 December 2018
ER -