Neural classification of lung sounds using wavelet coefficients

By A. Kandaswamy, C. Sasthi Kumar, R. Ramanathan, S. Jayaraman, N. Malmurugan. Comput. Biol. Medicine, 2004

392 citations recorded by the source corpus.

Subfields: Mechanics of Materials, Pulmonary and Respiratory Medicine, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8dcf5728b5505e49e05d9c2183e7b7b6145fc0a5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanics of Materials

1. Abdulhamit Subasi

In Pulmonary and Respiratory Medicine

1. Abdulhamit Subasi

In Signal Processing

1. M. Ismail Gursoy
2. Abdulhamit Subasi
3. Alejandro Pazos
4. Daniel Rivero
5. Ling Guo
6. Amjed Subhi Al-Fahoum
7. Ausilah Alfraihat
8. Usha R. Acharya

Citations in the corpus

• EEG signal classification using PCA, ICA, LDA and support vector machines (2010, methodology): “These feature vectors, calculated for the frequency bands A5 and D3–D5, were used for classification of the EEG signals (Kandaswamy et al., 2004).”
• Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders (2013, methodology): “In order to decrease the dimensionality of the extracted feature vectors, statistics over the set of the wavelet coefficients were used [31].”
• EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks (2006, methodology): “In order to further decrease the dimensionality of the extracted feature vectors, statistics over the set of the wavelet coefficients was used [10],[11].”
• Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients (2005, methodology): “Subsequently it was proved that a feed-forward ANN with one hidden layer having pK1 neurons can exactly implement an arbitrary training set with p training samples (Kandaswamy et al., 2004).”