Work place: Department of Communication Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India
E-mail: mkhota.mnnit@gmail.com
Website:
Research Interests: Signal Processing, Optimization
Biography
Malaya Kumar Hota is a Professor in the Department of Communication Engineering, School of Electronics Engineering at Vellore Institute of Technology, Vellore, Tamil Nadu, India. He was previously a Professor and Principal at Synergy Institute of Engineering and Technology, Dhenkanal, Odisha. He has more than twenty years of teaching and research experience. He received his M.Tech. in Electronics Engineering from Visvesvaraya National Institute of Technology, Nagpur, India, in 2002 and his Ph.D. in Electronics and Communication Engineering from Motilal Nehru National Institute of Technology, Allahabad, India, in 2011. He has authored or co-authored about thirty-five publications. He received one MODROBS grant from AICTE for the Modernization of Digital Signal Processing Lab. His biography has been included in Marquis Who’s Who in Science and Engineering and also in Marquis Who’s Who in the World. His main research interest is in digital signal processing, genomic signal processing, biomedical signal processing, seismic signal processing, and optimization techniques.
By Pavan G. Malghan Malaya Kumar Hota
DOI: https://doi.org/10.5815/ijigsp.2024.04.05, Pub. Date: 8 Aug. 2024
Removing undesirable artifacts in electrocardiogram signals is essential for biological signal processing as the signal gets distorted and makes appropriate investigation challenging. A primary source of distortion affecting recordings is the 50Hz power line interference. To get a high-quality recording, we used a filtering method based on an efficient decomposition technique known as variational mode extraction. This approach is similar to the variational mode decomposition methodology but with a few alterations in mathematical computation. First, it extracts the noise efficiently in a specific frequency band. Then, we apply the discrete wavelet transform to the signal, employing soft thresholding. As a result, it eliminates the extra noise and filters the electrocardiogram signal. We evaluated the efficacy of our proposed method using an arrhythmia database. Furthermore, we compared recent decomposition methods on six random signals using signal-to-noise ratios, mean square errors, correlation coefficients, and other signal features. Our method also efficiently eliminates varying amplitude of powerline noise and finally outperforms decomposition strategies regarding noise reduction and processing complexity across all signal parameters.
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