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Performance Improvement of Deep Learning Architectures for Phonocardiogram Signal Classification using Fast Fourier Transform

Publication Type : Conference Paper

Publisher : IEEE

Source : International Conference on Advances in Computing and Communication. November 6 – 7, 2019, Hyderabad, India.

Url : https://ieeexplore.ieee.org/document/8986216

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.

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