Automated diagnosis of cardiac state in healthcare systems using computational intelligence
by B. Samanta, C. Nataraj
International Journal of Services Operations and Informatics (IJSOI), Vol. 3, No. 2, 2008

Abstract: A study is presented for the diagnosis of a patient's heart conditions using recorded heart sounds and Computational Intelligence (CI) techniques. The digitally recorded heart sound signals are processed through Continuous Wavelet Transform (CWT) to extract time–frequency features for normal and abnormal heart conditions. The wavelet energy distributions are used as inputs to classifiers based on soft computing techniques such as Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Genetic Algorithms (GAs) for diagnosis of heart conditions. The number and the parameters of Membership Functions (MFs) used in ANFIS along with the features from wavelet energy distribution are selected using GAs, maximising the diagnosis success. ANFIS with GAs (GA-ANFIS) are trained with a subset of data with known heart conditions. The trained GA-ANFIS are tested using the other set of data (testing data), not used in training. The results are compared with Artificial Neural Network (ANN) and GA (GA-ANN). The results show the effectiveness of the proposed approach in automated diagnosis of cardiac state in healthcare systems.

Online publication date: Mon, 07-Jul-2008

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Services Operations and Informatics (IJSOI):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com