Feature Extraction Based on Pan Tompkins Algorithm from ECG Signals and Diagnosis of Arrhythmia Using Multilayer Perceptron Neural Network
Yazarlar (2)
Dr. Öğr. Üyesi Ersin ERSOY Tokat Gaziosmanpaşa Üniversitesi, Türkiye
Prof. Dr. Mahmut HEKİM Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale)
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| Dergi Adı | Journal of New Results in Science | ||
| Dergi ISSN | 1304-7981 | ||
| Dergi Tarandığı Indeksler | ROOTINDEXING, GOOGLE SCHOLAR, RESEARCHBIB ACADEMIC RESEARCH INDEX, DIRECTORY RESEARCH JOURNALS INDEXING (DRJI), JOUR INFORMATICS, SCHOLARS IMPACT FACTOR, PUBLONS, ELECTRONIC JOURNALS LIBRARY, INTERNATIONAL INNOVATIVE JOURNAL IMPACT FACTOR (IIJIF), GLOBAL IMPACT FACTOR, GENERAL IMPACT FACTOR, JOURNAL INDEX, COSMOS IMPACT FACTOR, WORLDCAT, SCIENTIFIC INDEXING SERVICES, IMPACT FACTOR SERVICES FOR INTERNATIONAL JOURNALS (IFSIJ), INTERNATIONAL ACCREDITATION AND RESEARCH COUNCIL IARC(JCRR): JOURNAL IMPACT FACTOR, JOURNAL FACTOR (JF). | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2016 |
| UAK Araştırma Alanları |
Yapay Zeka
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| Özet |
| In this study, Electrocardiogram (ECG) signals giving information about the state and functioning of the heart are divided into segments, waves and intervals by resting upon temporal limitations and feature vector of each section is obtained by means of arithmetic mean which is one of basic statistical parameters. Arrhythmia (rhythm disorders) occurring in the heart are diagnosed by the obtained feature vectors used as the inputs into multilayer perceptron neural network (MLPNN) model. For this purpose, ECG signals are divided into sections that are 10-minute-equal-length. These sections are divided into subsections (segment and intervals) which are admitted for each segment and wave interval and give information on arrhythmia by temporal limitations and arithmetic average of each interval is used as the inputs into the model of MLPNN for the diagnosis of arrhythmia. As a conclusion, it is proved that the proposed approach has reached to high accuracy rates of classification for the diagnosis of arrhythmia through ECG signals. |
| Anahtar Kelimeler |
