CLASSIFICATION OF NOISES FROM BIO SIGNALS BY MACHINE LEARNING ALGORITHM

This project, titled "Classification of Noises from Bio signals using Machine Algorithm," delves into the vital realm of bio signal analysis for healthcare applications. For proper interpretation of cardiac illnesses for the   fetal, a noise-free FECG is often preferred. Collected Data sets from the open source called Physio.Net are employed into a Median filter for noise reduction and Lion Optimization techniques for feature extraction in MATLAB, this project involves the study of classification capabilities of Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) algorithms. The project focuses on analysing FECG data collected from open-source Physio.Net, with a 97% accuracy rate achieved by SVM, showcasing its potential for bio signal pattern recognition. This project offers valuable insights into improving bio signal analysis accuracy, error rate, MCC, and Kuppa, with implications for enhanced healthcare diagnostics and further advancements in medical signal processing.

Keywords— Noise removal, FECG Signal, Median Filter, SVM, KNN.