REVOLUTIONIZING HEALTHCARE: CNN APPLICATIONS IN BIOMEDICAL SIGNAL ANALYSIS

Abstract

The evolution of biomedical signal analysis has a rich history dating back to the early 20th century. With the advent of signal recording and machine loading capabilities, researchers embarked on the journey of designing automated analysis systems. The electrocardiograph (ECG), introduced in 1902, marked a significant milestone in conveying essential information about the structure and function of the heart. Augustus Waller, a British physiologist, furthered this progress by presenting the first human ECG in 1937, utilizing a capillary electrometer and chest electrodes. Subsequently, Denny-Brown's work in 1938 laid the groundwork for the identification of fasciculation potentials and their extraction from fibrillations, while Lambert and Eaton's research in 1957 elucidated the electrophysiologic aspects of myasthenic syndrome linked to lung cancer. In the realm of EEG signals, visual examination persisted until the late 1960s when digital tools were discovered.

The significance of signal processing and the differentiation of biomedical signals has grown over the years, facilitating the classification of normal and abnormal signals for disease identification. Time series analyses have emerged as valuable tools to monitor the patient's health status over time, offering clinicians valuable insights for decision-making. To effectively analyze biomedical signals across multiple dimensions, a sequence of steps is essential: pre-processing, feature extraction, and classification.

In recent years, machine learning techniques have revolutionized scientific and engineering research, including the biomedical field. Supervised learning, often referred to as classification techniques, plays a pivotal role in abnormality detection and diagnosis. This paper explores the applications of machine learning techniques, particularly supervised learning, as powerful tools for biomedical signal analysis, emphasizing their role in the detection and diagnosis of abnormalities in the context of healthcare and medical research.