• N.Naveen
• Potnuri Gayatri

A condition known as cardiac arrhythmia causes an irregular heartbeat that can be either too slow or too fast. Faulty electrical impulses that control the heartbeats cause it to occur. Certain severe arrhythmia diseases can lead to sudden cardiac death. Because of this, the main objective of electrocardiogram (ECG) examination is to accurately identify arrhythmias as life-threatening in order to offer an appropriate therapy and save lives. ECG signals are waveforms (P, QRS, and T) that represent the electrical activity of the human heart. Each waveform's duration, organisation, and spacing between different peaks are used to determine whether there are any heart issues. The parameters of the AR signal model are then determined by doing an autoregressive (AR) analysis on the signals. The training dataset neatly separates the groups of recovered AR features for three different types of ECG, giving each ECG signal in the training dataset good connection classification and heart condition diagnosis. To more accurately assess ECG signals, a novel method based on fractional Fourier transform (FFT) algorithms and two-event-related moving averages (TERMAs) is proposed. This study may aid in the examination of the most recent cutting-edge techniques used in arrhythmia situation detection. Our proposed machine learning method features cross-database training and testing with enhanced properties. Keywords: Cardiac arrhythmia, irregular heartbeat, electrical impulses, electrocardiogram (ECG), P wave, QRS complex, T wave, autoregressive (AR) analysis, AR signal model, heart condition diagnosis, fractional Fourier transform (FFT), two-event-related moving averages (TERMAs), machine learning, cross-database training, arrhythmia situation detection.

crime analysis, prediction analysis, machine learning, decision trees, pattern detection, K-Nearest Neighbour, Random Forest, boosted decision trees.

"ARRYTHMIA CLASSIFICATION USING ECG DATA VALUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.j86-j93, June-2023, Available :http://www.jetir.org/papers/JETIR2306914.pdf

"ARRYTHMIA CLASSIFICATION USING ECG DATA VALUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. ppj86-j93, June-2023, Available at : http://www.jetir.org/papers/JETIR2306914.pdf