A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD); and most existing algorithms are usually designed for the diagnosis (i.e.; feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict...

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Main Authors: Wang, Liang-Hung, Yan, Ze-Hong, Yang, Yi-Ting, Chen, Jun-Ying, Yang, Tao, Kuo, I-Chun, Abu, Patricia Angela R, Huang, Pao-Cheng, Chen, Chiung-An, Chen, Shih-Lun
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Published: Archīum Ateneo 2021
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Online Access:https://archium.ateneo.edu/discs-faculty-pubs/215
https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1219&context=discs-faculty-pubs
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spelling ph-ateneo-arc.discs-faculty-pubs-12192022-01-31T05:00:32Z A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia Wang, Liang-Hung Yan, Ze-Hong Yang, Yi-Ting Chen, Jun-Ying Yang, Tao Kuo, I-Chun Abu, Patricia Angela R Huang, Pao-Cheng Chen, Chiung-An Chen, Shih-Lun Atrial fibrillation (AF) is the most common cardiovascular disease (CVD); and most existing algorithms are usually designed for the diagnosis (i.e.; feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper; we utilized the MIT-BIH AF Database (AFDB); which is composed of data from normal people and patients with AF and onset characteristics; and the AFPDB database (i.e.; PAF Prediction Challenge Database); which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF); and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction; we regarded diagnosis and prediction as two classification problems; adopted the traditional support vector machine (SVM) algorithm; and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process; the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases; the sensitivity; specificity; and accuracy measures were 99.2% and 99.2%; 99.2% and 93.3%; and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases; respectively. Moreover; the sensitivity; specificity; and accuracy were 94.2%; 79.7%; and 87.0%; respectively; when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels. 2021-08-01T07:00:00Z text application/pdf https://archium.ateneo.edu/discs-faculty-pubs/215 https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1219&context=discs-faculty-pubs Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo ECG signal atrial fibrillation support vector machine image-to-data prediction Cardiology Engineering Medicine and Health Sciences
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic ECG signal
atrial fibrillation
support vector machine
image-to-data
prediction
Cardiology
Engineering
Medicine and Health Sciences
spellingShingle ECG signal
atrial fibrillation
support vector machine
image-to-data
prediction
Cardiology
Engineering
Medicine and Health Sciences
Wang, Liang-Hung
Yan, Ze-Hong
Yang, Yi-Ting
Chen, Jun-Ying
Yang, Tao
Kuo, I-Chun
Abu, Patricia Angela R
Huang, Pao-Cheng
Chen, Chiung-An
Chen, Shih-Lun
A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia
description Atrial fibrillation (AF) is the most common cardiovascular disease (CVD); and most existing algorithms are usually designed for the diagnosis (i.e.; feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper; we utilized the MIT-BIH AF Database (AFDB); which is composed of data from normal people and patients with AF and onset characteristics; and the AFPDB database (i.e.; PAF Prediction Challenge Database); which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF); and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction; we regarded diagnosis and prediction as two classification problems; adopted the traditional support vector machine (SVM) algorithm; and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process; the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases; the sensitivity; specificity; and accuracy measures were 99.2% and 99.2%; 99.2% and 93.3%; and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases; respectively. Moreover; the sensitivity; specificity; and accuracy were 94.2%; 79.7%; and 87.0%; respectively; when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.
format text
author Wang, Liang-Hung
Yan, Ze-Hong
Yang, Yi-Ting
Chen, Jun-Ying
Yang, Tao
Kuo, I-Chun
Abu, Patricia Angela R
Huang, Pao-Cheng
Chen, Chiung-An
Chen, Shih-Lun
author_facet Wang, Liang-Hung
Yan, Ze-Hong
Yang, Yi-Ting
Chen, Jun-Ying
Yang, Tao
Kuo, I-Chun
Abu, Patricia Angela R
Huang, Pao-Cheng
Chen, Chiung-An
Chen, Shih-Lun
author_sort Wang, Liang-Hung
title A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia
title_short A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia
title_full A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia
title_fullStr A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia
title_full_unstemmed A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia
title_sort classification and prediction hybrid model construction with the iqpso-svm algorithm for atrial fibrillation arrhythmia
publisher Archīum Ateneo
publishDate 2021
url https://archium.ateneo.edu/discs-faculty-pubs/215
https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1219&context=discs-faculty-pubs
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