Neural network classification of homomorphic segmented heart sounds |
| |
| Affiliation: | 1. Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso, Italy;2. Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy;3. Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy;1. Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi, UAE;2. Jordan University of Science and Technology, Department of Biomedical Engineering, Irbid, Jordan |
| |
| Abstract: | A novel method for segmentation of heart sounds (HSs) into single cardiac cycle (S1-Systole-S2-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. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
