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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MDPI AG
2021-08-01
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| Online Access: | https://www.mdpi.com/1424-8220/21/15/5222 |
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| author | Liang-Hung Wang Ze-Hong Yan Yi-Ting Yang Jun-Ying Chen Tao Yang I-Chun Kuo Patricia Angela R. Abu Pao-Cheng Huang Chiung-An Chen Shih-Lun Chen |
| author_facet | Liang-Hung Wang Ze-Hong Yan Yi-Ting Yang Jun-Ying Chen Tao Yang I-Chun Kuo Patricia Angela R. Abu Pao-Cheng Huang Chiung-An Chen Shih-Lun Chen |
| author_sort | Liang-Hung Wang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T04:41:39Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T04:41:39Z |
| publishDate | 2021-08-01 |
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| spelling | doaj.art-1ccd1f2830ff4bf78e3ffccc479d78a02023-12-03T13:19:27ZengMDPI AGSensors1424-82202021-08-012115522210.3390/s21155222A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation ArrhythmiaLiang-Hung Wang0Ze-Hong Yan1Yi-Ting Yang2Jun-Ying Chen3Tao Yang4I-Chun Kuo5Patricia Angela R. Abu6Pao-Cheng Huang7Chiung-An Chen8Shih-Lun Chen9Department of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, ChinaDepartment of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, ChinaDepartment of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, ChinaDepartment of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, ChinaDepartment of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, ChinaDepartment of Microelectronics, College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, ChinaDepartment of Information Systems and Computer Science, Ateneo de Manila University, Quezon 1108, PhilippinesCollege of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, ChinaDepartment of Electrical Engineering, Ming Chi University of Technology, New Taipei 243303, TaiwanDepartment of Electronic Engineering, Chung Yuan Christian University, Taoyuan 320314, TaiwanAtrial 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.https://www.mdpi.com/1424-8220/21/15/5222ECG signalatrial fibrillationsupport vector machineimage-to-dataprediction |
| spellingShingle | Liang-Hung Wang Ze-Hong Yan Yi-Ting Yang Jun-Ying Chen Tao Yang I-Chun Kuo Patricia Angela R. Abu Pao-Cheng Huang Chiung-An Chen Shih-Lun Chen A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia Sensors ECG signal atrial fibrillation support vector machine image-to-data prediction |
| title | 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_short | 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 |
| topic | ECG signal atrial fibrillation support vector machine image-to-data prediction |
| url | https://www.mdpi.com/1424-8220/21/15/5222 |
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