A New Medical Decision Support System for Diagnosing HFrEF and HFpEF using ECG and Machine Learning Techniques

• Published in: IEEE access Vol. 10; p. 1
• Main Authors: Kavas, Pinar Ozen, Bozkurt, Mehmet Recep, Kocayigit, Ibrahim, Bilgin, Cahit
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Cardiac arrest; Classification; Decision support systems; Decision trees; Diagnosis; Diseases; Echocardiography; Ejection fraction; Electrocardiography; Feature extraction; Heart Failure; HFpEF; HFrEF; Machine learning; Medical diagnostic imaging; Signs and symptoms
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2022.3213065

As heart failure (HF) is a growing epidemic, no case should be overlooked in the diagnosis of HF. Two subtypes by left ventricular ejection fraction (LVEF) of HF are HF with reduced ejection fraction (HFrEF) (LVEF ≤ 40%) and HF with preserved ejection fraction (HFpEF) (LVEF ≥ 50%). HFrEF is easier to diagnose. However, the diagnosis of HFpEF is more complex and difficult even for specialists. The diagnosis of HFpEF is a problem that is being tried to be solved in medicine. Since LVEF appears normal (LVEF ≥ 50% in healthy individuals), HFpEF can be confused with chest diseases due to some similar symptoms. The diagnosis of HF subtypes is ideally made using echocardiography. Echocardiography should be performed in all patients with HF; however, it is expensive and requires specialists. Even in high-resource regions, this test is not always performed, and treatment may need to be initiated before the echocardiographic data are obtained. For such situations, economical and practical systems are required. In this study, a medical decision support system was developed to detect HFrEF and HFpEF cases using only 3-lead ECG. From the ECG data of 61 volunteers, 37 features were extracted, of which 16 were Yule-Walker and Burg's method parameters, and 21 were in the time domain. Consequently, 37 features were reduced by feature selection and triple classification was performed with only 4 features with maximum accuracy. The aim of this study was to determine whether the individuals with HF symptoms were HFrEF, HFpEF, or healthy. Four machine learning algorithms were used for classification. The best classification accuracy rate was 100% for k-NN, and significant results were obtained from the other three algorithms: SVMs, Decision Trees, and Ensemble Bagged Trees.