A Novel Method for Segmentation of QRS Complex on ECG Signals and Classification of Cardiovascular Diseases via a Hybrid Model Based on Machine Learning
Yazarlar (2)
Doç. Dr. Eftal ŞEHİRLİ Karabük Üniversitesi, Türkiye
Doç. Dr. Muhammed Kamil TURAN Karabük Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SCOPUS dergilerinde yayınlanan tam makale)
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| Dergi Adı | International Journal of Intelligent Systems and Applications in Engineering | ||
| Dergi ISSN | 2147-6799 Dergi Bilgileri (2021) | ||
| Dergi Tarandığı Indeksler | TR DİZİN | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2021 |
| Kabul Tarihi | – | Yayınlanma Tarihi | 31-03-2021 |
| Cilt / Sayı / Sayfa | 9 / 1 / 12–21 | DOI | 10.18201/ijisae.2021167932 |
| Makale Linki | http://dx.doi.org/10.18201/ijisae.2021167932 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
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| Özet |
| Most disease that affects the heart or blood vessels is referred to as cardiovascular disease (CVD). The main aim of this work is to build a system capable of modeling and predicting early syndromic cardiovascular diseases (CVD) based on electrocardiogram (ECG). The study considers the implementation of computationally intelligent system for detecting and classifying early syndromic assessment of CVD. The clinical and ECG recordings of patients diagnosed with pulmonary hypertension at the University of Uyo Teaching Hospital (UUTH) were obtained. The datasets were segmented into Demographic and ECG datasets. A quantitative research approach was used for the study with examination of several segments based on recommended framework. Three (3) classifier models were adopted to detect cardiac related problems using specified datasets. The classifiers such as; Random Forest Ensemble (RFE), Support Vector (SVM) Classifier and Artificial Neural Network (ANN) was employed for Machine Learning... |
| Anahtar Kelimeler |
| CVD | ECG signal | Machine learning | Signal processing |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 19 |
| Google Scholar | 19 |