• Rajshekhargouda Patil
• Anitha S Prasad
• Manjunath Desai
• Shivakumar Yankanchi
• Md. Juned R

Cardiovascular diseases are one of the world’s leading causes of death. Electrocardiography is the most common way to monitor heart activity. Various heart disorders can be detected by analysis of electrocardiogram (ECG) signal abnormalities. Classification of ECG signals into normal and abnormal using machine learning models, through a sufficient amount of training data gives better insight into the patient condition. Machine Learning models are elegant and efficient. Some good models can be developed provided there is a wide range of reliable data available. Scaled Rolling mean is used for R peak detection. Various features of an ECG signal are fed as the input to different classification models and their performance is evaluated. MIT-BIH Arrhythmia Database is considered for evaluation.

ECG Analysis, Support Vector Machines, K – Nearest Neighbours

"ECG Signal Analysis using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.291-296, June 2019, Available :http://www.jetir.org/papers/JETIR1906M40.pdf

"ECG Signal Analysis using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp291-296, June 2019, Available at : http://www.jetir.org/papers/JETIR1906M40.pdf