• R.Vinoth
• R.Sasireka
• R.Vasantha Baskar

: Signal processing techniques are an obvious choice for real-time analysis of electrocardiography (ECG) signals. However, classical signal processing techniques are unable to deal with the non stationary nature of the ECG signal. In this context, this project presents a new approach, i.e., discrete orthogonal stock well transform using discrete cosine transform for efficient representation of the ECG signal in time–frequency space. These time–frequency features are further reduced in lower dimensional space using principal component analysis, representing the morphological characteristics of the ECG signal. In addition, the dynamic features (i.e., RR-interval information) are computed and concatenated to the morphological features to constitute the final feature set, which is utilized to classify the ECG signals using RBFNN based support vector machine (SVM). In order to improve the classification performance, optimization technique is employed for gradually tuning the learning parameters of the SVM classifier. In this project, ECG data exhibiting 16 classes of the most frequently occurring arrhythmic events are taken from the benchmark MIT-BIH arrhythmia database for the validation of the proposed methodology. This project is developed using Matlab simulation

ECG, DCT

"ECG SIGNAL USING DCT-ALGORITHM AND OPTIMIZED RBFNN CLASSIFIER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.127-134, May-2019, Available :http://www.jetir.org/papers/JETIRBJ06025.pdf

"ECG SIGNAL USING DCT-ALGORITHM AND OPTIMIZED RBFNN CLASSIFIER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp127-134, May-2019, Available at : http://www.jetir.org/papers/JETIRBJ06025.pdf