Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network
There are a variety of electrocardiogram based methods to detect myocardial infarction (MI) patients. This study used the signal averaged electrocardiogram (SAECG) and its wavelet coefficient as an index to detect MI. Orthogonal leads signals from 50 acute myocardial infarction (AMI) and 50 healthy...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2013-01-01
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| Series: | Journal of Medical Signals and Sensors |
| Subjects: | |
| Online Access: | http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2013;volume=3;issue=4;spage=225;epage=230;aulast=Keshtkar |
| _version_ | 1818557437918052352 |
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| author | Ahmad Keshtkar Hadi Seyedarabi Peyman Sheikhzadeh Seyed Hossein Rasta |
| author_facet | Ahmad Keshtkar Hadi Seyedarabi Peyman Sheikhzadeh Seyed Hossein Rasta |
| author_sort | Ahmad Keshtkar |
| collection | DOAJ |
| description | There are a variety of electrocardiogram based methods to detect myocardial infarction (MI) patients. This study used the signal averaged electrocardiogram (SAECG) and its wavelet coefficient as an index to detect MI. Orthogonal leads signals from 50 acute myocardial infarction (AMI) and 50 healthy subjects were selected from the national metrology institute of Germany (PTB diagnostic
database). They were filtered and discrete wavelet transformed was exerted on them. Four conventional features and two new features introduced in this study were extracted from SAECG and its wavelet decompositions. Finally for data classification, probabilistic neural network were used. This method was able to detect and discriminate AMI patients from healthy subjects using the probabilistic neural network, which shows 93.0% sensitivity at 86.0% specificity with 89.5% accuracy. This technique and the new extracted features showed good promise in the identification of MI patients. However, the sensitivity and specificity is comparable with other findings and has high accuracy although we extracted only 6 features. |
| first_indexed | 2024-12-13T23:59:29Z |
| format | Article |
| id | doaj.art-05515805b10c49ee9b8bfda8b58e8eba |
| institution | Directory Open Access Journal |
| issn | 2228-7477 |
| language | English |
| last_indexed | 2024-12-13T23:59:29Z |
| publishDate | 2013-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Medical Signals and Sensors |
| spelling | doaj.art-05515805b10c49ee9b8bfda8b58e8eba2022-12-21T23:26:23ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772013-01-0134225230Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural networkAhmad KeshtkarHadi SeyedarabiPeyman SheikhzadehSeyed Hossein RastaThere are a variety of electrocardiogram based methods to detect myocardial infarction (MI) patients. This study used the signal averaged electrocardiogram (SAECG) and its wavelet coefficient as an index to detect MI. Orthogonal leads signals from 50 acute myocardial infarction (AMI) and 50 healthy subjects were selected from the national metrology institute of Germany (PTB diagnostic database). They were filtered and discrete wavelet transformed was exerted on them. Four conventional features and two new features introduced in this study were extracted from SAECG and its wavelet decompositions. Finally for data classification, probabilistic neural network were used. This method was able to detect and discriminate AMI patients from healthy subjects using the probabilistic neural network, which shows 93.0% sensitivity at 86.0% specificity with 89.5% accuracy. This technique and the new extracted features showed good promise in the identification of MI patients. However, the sensitivity and specificity is comparable with other findings and has high accuracy although we extracted only 6 features.http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2013;volume=3;issue=4;spage=225;epage=230;aulast=KeshtkarDiscrete wavelet transformelectrocardiogrammyocardial infarctionprobabilistic neural network |
| spellingShingle | Ahmad Keshtkar Hadi Seyedarabi Peyman Sheikhzadeh Seyed Hossein Rasta Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network Journal of Medical Signals and Sensors Discrete wavelet transform electrocardiogram myocardial infarction probabilistic neural network |
| title | Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network |
| title_full | Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network |
| title_fullStr | Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network |
| title_full_unstemmed | Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network |
| title_short | Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network |
| title_sort | discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network |
| topic | Discrete wavelet transform electrocardiogram myocardial infarction probabilistic neural network |
| url | http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2013;volume=3;issue=4;spage=225;epage=230;aulast=Keshtkar |
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