Publication Type : Conference Paper
Publisher : Springer
Source : In International Conference on Advances in Computing and Data Sciences, pp. 538-549. Springer, Cham, 2021.
Url : https://link.springer.com/chapter/10.1007/978-3-030-81462-5_48
Campus : Coimbatore
School : School of Engineering
Department : Center for Computational Engineering and Networking (CEN)
Year : 2021
Abstract : Phonocardiogram (PCG) assumes a critical part in the early determination of heart irregularities. Phono-cardiogram can be utilized as an underlying diagnostics apparatus in far-off applications because of its effortlessness and cost-adequacy. The proposed work targets utilising a CNN architecture, with multiple preprocessing strategies like converting to Spectrogram or normalizing the signals, which analyze various cardiovascular anomalies from PCG signals gathered from different sources. Our study shows the viability of utilising Spectrogram and Normalization of signals in cardiac abnormalities identification. This work avoids feature extraction and trivial pre-processing mechanisms, and we have achieved promising results.
Cite this Research Publication : Kesav, R. Sai, M. Bhanu Prakash, Krishanth Kumar, V. Sowmya, and K. P. Soman. "Performance Improvement in Deep Learning Architecture for Phonocardiogram Signal Classification Using Spectrogram." In International Conference on Advances in Computing and Data Sciences, pp. 538-549. Springer, Cham, 2021.