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Publication Type : Conference Paper
Publisher : IEEE
Source : International Conference on Advances in Computing and Communication. November 6 – 7, 2019, Hyderabad, India.
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2019
Abstract : Phonocardiogram known as PCG plays a significant role in the early diagnosis of cardiac abnormalities. Phonocardiogram can be used as initial diagnostics tool in remote applications due to its simplicity and cost effectiveness. Instead of disease specific approach, the proposed work aims for the single architecture that could diagnose different cardiac abnormality from the PCG signals collected from various sources. Our study also shows the effectiveness of using Fast Fourier Transform (FFT) in signal processing applications. It avoids the trivial preprocessing and feature extraction mechanisms with the promising results.
Cite this Research Publication : Gopika, P., Sowmya, V., Gopalakrishnan, E. A. and Soman, K. P. “Performance Improvement of Deep Learning Architectures for Phonocardiogram Signal Classification using Fast Fourier Transform”. International Conference on Advances in Computing and Communication. November 6 – 7, 2019, Hyderabad, India.