Computing and Monitoring various Biopotential signals using Machine Learning algorithms

• Published in: E3S web of conferences Vol. 391; p. 1106
• Main Authors: Sri Vidya, Devi P., Sai, Krishna C.H., Sai Srinivas, P., Ashraf, S.K., Sushith, K.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 01.01.2023
• Subjects: Algorithms; Computation; Decision trees; Human body; Learning algorithms; Machine learning; Observational learning; Python; Signal monitoring; Software; Support vector machines
• ISSN: 2267-1242; 2555-0403; 2267-1242
• DOI: 10.1051/e3sconf/202339101106

Nowadays health care units play a vital role of the human existence after the pandemic periods. It is very essential to monitor the potential signals of the human body for survival on regular basis. In this paper extracting the values of different biopotential signals produced in human body, monitoring and analysing them using various machine learning algorithms. Monitoring involves observing and checking the progress or quality of data over a period of time and keeping it under system review. The beauty of effective computing is to make machine more emphatic to the user. Machine with the capability of human electrical signal recognition can look inside the user’s body. This paper generalises the view of training of the bio potentials signals data in the MATLAB software as well in python software. Analysis with different machine learning algorithms like K-Nearest Neighbours (KNN), Decision tree (DT), Logistic Regression (LR), Support Vector Machine(SVM) are used in the training ,testing and validation of the data. Better performance is achieved with these algorithms.