Paper Details

ECG observes the movements in the heart by sensing the electrical variations in the human skin by usingsmall sensors which are implanted on the chest of the patients. In traditional work many algorithms have been developedfor detecting heart disease from which it is concluded that most of the work in biomedical ECG analysis for theprediction or detection was done only up-to the feature extraction, no further decision system development field washighlighted. So, in the proposed work, after getting the information from the ECG, dataset standalone system hasdeveloped in this paper. Considering this fact, a novel approach has identified termed as Aritifical neural network wherethe dataset is trained and then classified in order to detect the type of disease a person is suffering from. Specifically,proposed system works on two types of diseases such as Bradycardia and Tachycardia. Apart from this, it also classifiedeither the person is normal or not and if not then which type of disease he or she has. The proposed system is comparedwith the traditional approach using MATLAB software tool and performance analysis are carried out in the end of thepaper. The evaluation of simulation analysis confirmed that proposed system is more efficient and accurate incomparison with the traditional wavelet based approach in terms of overall success rate and error rate of the system

Anurag Krishna Shukla, MTech Scholar, Atul Kumar Shrivastava, Assistant Professor, “A combined approach WNN for ECG feature based disease classification”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 9, pp. 315-322, September 2017.