Machine learning based techniques for ECG noise removal and feature extraction

Abstract

Machine learning (ML) is being applied to all aspects of life with the development of artificial intelligence (AI). This paper explores the application of machine learning technology in electrocardiogram (ECG) analysis to diagnose and classify a patient's current cardiac disease, predict possible future diseases, and provide a personalised treatment plan. However, several challenges have been highlighted. First, individual ECG signals display variability, causing concern about effective diagnosis based on changing ECG data. Second, different diseases can produce similar ECG results, requiring powerful classification algorithms to accurately classify diseases. Finally, using patient information to predict the probability of future heart attacks is critical to developing appropriate prevention and treatment strategies. Overcoming these challenges could revolutionise the field of cardiology. It could enable precise and proactive medical intervention. The study highlights the potential of machine learning to improve cardiovascular care and personalised medicine and emphasises the importance of addressing key challenges to maximise its impact in clinical practice.