Machine Learning-Based Classification Of Biomedical Signals

Abstract

The effective biological signal processing technique significantly enhances researchers' ability to investigate the mechanisms of life, thereby providing deeper insights into the connection between physiological structure and function, which in turn fosters significant biological breakthroughs. Additionally, a high-precision medical signal analysis approach can alleviate some of the burdens faced by physicians in clinical diagnosis, enabling them to develop more effective strategies for disease prevention and treatment, ultimately improving society's general health and lessening the mental and physical pain of patients.  EEG signals and mammary gland molybdenum target X-ray images (mammography) are two sample types of biomedical signals that are examined in this research using deep learning techniques, namely convolutional neural networks (CNNs).   The development of a novel multi-layer CNN-based breast mass classification system that combines a feature choice mechanism that mimics medical diagnostic processes with a CNN feature representation network tailored for breast masses is one notable achievement.