Neural Network Architecture Design for Feature Extraction of Ecg by Wavelet
نویسندگان
چکیده
Priyanka Agrawal student, electrical, mits, rgpv, gwalior, mp 474005, india† Dr. A. K. Wadhwani professor, electrical ,mits, rgpv gwalior, mp 474005, india Abstract : This paper deals with the designing of feed forward neural network (FFNN) with the effect of ANN parameters for feature extraction of ECG signal by employing wavelet decomposition. Extraction of ECG features has a significance role in disease diagnosis of heart.ECG signal is decomposed in to it’s higher and lower frequency components by using Daubechies wavelet then statistical features of all components are given as input of neural network. A Multi-layer Feed forward Neural Network (MFNN) employing back propagation algorithm is used for learning and to train the ANN. The ANN is designed and trained by MATLAB software. Effect of ANN parameters on error is also found out. Two different type of ECG signals has been taken from MIT-BIH: Normal rhythm(128 Hz) and Atrial fibrillation(250 Hz).
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