Controlling Body Sources of Noise Generated by Niddle Electrogram Machines : A New EMG Idea for Skipping Traditional Approaches
(2021) 2021 International Conference on Science and Contemporary Technologies, ICSCT 2021- Abstract
Different signal frequencies from body tissues, muscles fat etc affects the EMG signals. The signal frequencies from body organ can considered as noise which affects the output and hence for better signal output noised should be minimised. Traditional EMG machines supports analogue filtering while residual noises carried by signal channels. For good and better signal aggregation and useful data gathering machine generated noises need to be cleared out for maintaining proper accuracy regardless of signal distortions. In this work, an artifact reduction method is proposed for the EMG machines that can detect noise, randomized trials or points through sequential predictions. Through the proposed prediction method the data points will be... (More)
Different signal frequencies from body tissues, muscles fat etc affects the EMG signals. The signal frequencies from body organ can considered as noise which affects the output and hence for better signal output noised should be minimised. Traditional EMG machines supports analogue filtering while residual noises carried by signal channels. For good and better signal aggregation and useful data gathering machine generated noises need to be cleared out for maintaining proper accuracy regardless of signal distortions. In this work, an artifact reduction method is proposed for the EMG machines that can detect noise, randomized trials or points through sequential predictions. Through the proposed prediction method the data points will be reshaped and re-sampled with variance checking. It ensures maximization of SNR of the EMG signals. The reduction method also works with a digital sequence filter which shapes the noisy inputs. The shaped input then merged with a sequential output with less loss of information. Since the noise reduction process is a prediction based filter method, it will be independent of signal frequencies related with body organs like-body tissues, muscles fat etc. It reduces the variance and low discrepancy also.
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- author
- Tamanna, Iffat ; Mahi, Md Julkar Nayeen ; Ahmed, Shamim ; Kader, Manzur LU ; Haque, Ahasanul and Biswas, Milon
- organization
- publishing date
- 2021
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Body frequency, Electrograms (EMG), Niddle EMGs, Quasi-random, SNR
- host publication
- 2021 International Conference on Science and Contemporary Technologies, ICSCT 2021
- article number
- 175726
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2021 International Conference on Science and Contemporary Technologies, ICSCT 2021
- conference location
- Dhaka, Bangladesh
- conference dates
- 2021-08-05 - 2021-08-07
- external identifiers
-
- scopus:85123982234
- ISBN
- 9781665421324
- DOI
- 10.1109/ICSCT53883.2021.9642546
- language
- English
- LU publication?
- yes
- id
- 8fe9ef8d-2e50-48ee-ab38-6d15cd6549ba
- date added to LUP
- 2022-04-05 15:38:49
- date last changed
- 2022-04-29 00:01:35
@inproceedings{8fe9ef8d-2e50-48ee-ab38-6d15cd6549ba,
abstract = {{<p>Different signal frequencies from body tissues, muscles fat etc affects the EMG signals. The signal frequencies from body organ can considered as noise which affects the output and hence for better signal output noised should be minimised. Traditional EMG machines supports analogue filtering while residual noises carried by signal channels. For good and better signal aggregation and useful data gathering machine generated noises need to be cleared out for maintaining proper accuracy regardless of signal distortions. In this work, an artifact reduction method is proposed for the EMG machines that can detect noise, randomized trials or points through sequential predictions. Through the proposed prediction method the data points will be reshaped and re-sampled with variance checking. It ensures maximization of SNR of the EMG signals. The reduction method also works with a digital sequence filter which shapes the noisy inputs. The shaped input then merged with a sequential output with less loss of information. Since the noise reduction process is a prediction based filter method, it will be independent of signal frequencies related with body organs like-body tissues, muscles fat etc. It reduces the variance and low discrepancy also. </p>}},
author = {{Tamanna, Iffat and Mahi, Md Julkar Nayeen and Ahmed, Shamim and Kader, Manzur and Haque, Ahasanul and Biswas, Milon}},
booktitle = {{2021 International Conference on Science and Contemporary Technologies, ICSCT 2021}},
isbn = {{9781665421324}},
keywords = {{Body frequency; Electrograms (EMG); Niddle EMGs; Quasi-random; SNR}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Controlling Body Sources of Noise Generated by Niddle Electrogram Machines : A New EMG Idea for Skipping Traditional Approaches}},
url = {{http://dx.doi.org/10.1109/ICSCT53883.2021.9642546}},
doi = {{10.1109/ICSCT53883.2021.9642546}},
year = {{2021}},
}