TY - GEN
T1 - Performance Evaluation of Electrogastrogram (EGG) Signal Compression for Telemedicine Using Various Wavelet Transform
AU - Gokul, M.
AU - Sameera Fathimal, M.
AU - Jothiraj, S.
AU - Murugesan, Pradeep
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - This paper discusses the recording and compression analysis of an Electrogastrogram (EGG), a non-invasive instrument that visually represents the electrical activity of the stomach to diagnose stomach illnesses. The EGG signal’s compression is important in the diagnosis, prognosis, and survival analysis of all stomach-related disorders, especially in telemedicine applications where the patient is geographically isolated. Over the years, several signal compression algorithms have been presented. High cost, signal degradation, and a low compression ratio are just a few drawbacks that result in an inefficient signal at the receiver’s end. The advantages of EGG compression in digital domain for telemedicine applications are effective utilization of storage data, reduced data transmission rate, and efficient transmission bandwidth. Various wavelet transformations such as biorthogonal, coiflet, daubechies, haar, reverse biorthogonal, and symlet wavelet transforms are applied to EGG signals and examined using MATLAB software in this paper. The wavelet’s performance was evaluated to select the best wavelet for telemedicine. This is accomplished by a quantitative analysis of the recovery ratio, percent root mean square difference (PRD), and compression ratio (CR) measurements. The findings of this study in terms of determining the optimal signal compression performance can undoubtedly become a valuable asset in the telemedicine area for the transmission of quantitative biological signals.
AB - This paper discusses the recording and compression analysis of an Electrogastrogram (EGG), a non-invasive instrument that visually represents the electrical activity of the stomach to diagnose stomach illnesses. The EGG signal’s compression is important in the diagnosis, prognosis, and survival analysis of all stomach-related disorders, especially in telemedicine applications where the patient is geographically isolated. Over the years, several signal compression algorithms have been presented. High cost, signal degradation, and a low compression ratio are just a few drawbacks that result in an inefficient signal at the receiver’s end. The advantages of EGG compression in digital domain for telemedicine applications are effective utilization of storage data, reduced data transmission rate, and efficient transmission bandwidth. Various wavelet transformations such as biorthogonal, coiflet, daubechies, haar, reverse biorthogonal, and symlet wavelet transforms are applied to EGG signals and examined using MATLAB software in this paper. The wavelet’s performance was evaluated to select the best wavelet for telemedicine. This is accomplished by a quantitative analysis of the recovery ratio, percent root mean square difference (PRD), and compression ratio (CR) measurements. The findings of this study in terms of determining the optimal signal compression performance can undoubtedly become a valuable asset in the telemedicine area for the transmission of quantitative biological signals.
KW - Compression
KW - Electrogastrogram (EGG)
KW - Non-invasive
KW - Telemedicine and wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=85130410817&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-9447-9_17
DO - 10.1007/978-981-16-9447-9_17
M3 - Conference contribution
AN - SCOPUS:85130410817
SN - 9789811694462
T3 - Smart Innovation, Systems and Technologies
SP - 225
EP - 233
BT - Computational Intelligence in Data Mining - Proceedings of ICCIDM 2021
A2 - Nayak, Janmenjoy
A2 - Behera, H. S.
A2 - Naik, Bighnaraj
A2 - Vimal, S.
A2 - Pelusi, Danilo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Computational Intelligence in Data Mining, ICCIDM 2021
Y2 - 11 December 2021 through 12 December 2021
ER -