A Two-stage Approach Using Artificial Neural Networks for Diagnosis of Heart Diseases Based on ECG Data

Abstract

Background and Aim Most heart diseases show symptoms on ECG, but diagnosing heart disease with ECG requires the knowledge and experience of medical specialized. Because these specialists may not always be available, it is necessary to design tools to diagnose heart disease in these situations. In this paper, a two-stage approach based on artificial neural networks is designed to diagnose heart disease using ECG information.In this study, we aim to propose a two-stage approach using artificial neural network (ANN) to diagnose heart disease based ECG data. Methods & Materials To design the proposed approach, first the ECG data of 861 patients referred to medical centers in Arak, Iran were collected. The data were examined based on the opinions of specialists. Then, 154 features from ECG were used as inputs to the proposed model. In the first stage, an ANN was used to detect the ECG status (usable and unusable). In the second stage, using the usable ECG data, an ANN was used to diagnose the presence or absence of heart disease. Finally, the performance of the two-stage approach was evaluated and its accuracy and precision in determining the ECG quality and heart disease diagnosis were determined. Ethical Considerations This study was approved by the ethics committee of Arak University of Medical Sciences (Code: IR.ARAKMU.REC.1400.138). Results In the proposed approach, the ANN used for the determining the ECG status had a precision of 97.1% and an accuracy of 97.3%. The ANN used for the diagnosis of heart disease had a precision of 95.8% and an accuracy of 95.4%. Conclusion Considering the high efficiency of the proposed approach in determining of ECG status and diagnosing heart disease, it is possible to use this approach to help the treatment staff.