A two-stage approach based on artificial neural networks for diagnosis of heart disease by ECG information

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.

To design the proposed approach, first ECG information for 861 refers to a number of medical centers in Arak city is collected and and data consulted is proccesed by specialists. Then 154 features from ECG as input variables in proposed approach has been specified. In the first stage of approach, an artificial neural network is designed to detect the status of the ECG in two situation as usable and unusable. Then, in the second stage, using the usable ECG information, an artificial neural network is designed to diagnose the presence or absence of heart disease. Finally, the performance of the two-stage approach is evaluated and its accuracy and precision in determining the status of the ECG as well as the disease status is determined.

Results:

 In the proposed approach, the neural network for the determining of ECG status has an precision of 97.1% and an accuracy of 97.3%, and also the neural network for the diagnosis of heart disease has an precision of 95.8% and an accuracy of 95.4%.

Considering the high effeciency of the proposed approach in the determining of ECG status and also diagnosing heart disease, it is possible to use this approach as a reliable assistant to assist the treatment staff.