Neural network classification of homomorphic segmented heart sounds

作者：Gupta Cota Navin^*; Palaniappan Ramaswamy; Swaminathan Sundaram; Krishnan Shankar M

来源：Applied Soft Computing, 2007, 7(1): 286-297.

DOI：10.1016/j.asoc.2005.06.006

• grow and learn neural network
• K-means clustering
• multilayer perceptron-backpropagation neural network
• phonocardiogram signals
• segmentation of heart sounds
• wavelet transform

A novel method for segmentation of heart sounds (HSs) into single cardiac cycle (S-1-Systole-S-2-Diastole) using homomorphic filtering and K-means clustering is presented. Feature vectors were formed after segmentation by using Daubechies-2 wavelet detail coefficients at the second decomposition level. These feature vectors were then used as input to the neural networks. Grow and Learn (GAL) and Multilayer perceptron-Backpropagation (MLP-BP) neural networks were used for classification of three different HSs ( Normal, Systolic murmur and Diastolic murmur). It was observed that the classification performance of GAL was similar to MLP-BP. However, the training and testing times of GAL were lower as compared to MLP-BP. The proposed framework could be a potential solution for automatic analysis of HSs that may be implemented in real time for classification of HSs.

• 出版日期2007-1
• 单位南阳理工学院