• CHARUGULLA PAVAN KUMAR
• ALUGONDA RAJANI

Heartbeats are crucial to the medical sciences' study of heart ailments because they reveal significant details about heart problems and irregular heart rhythms. Electrocardiogram (ECG) represents the electrical activity of the heart showing the regular contraction and relaxation of heart muscle. The heart condition is used to diagnose by an important tool called Electrocardiography. The ECG spectrogram is used for diagnosing the heart diseases. The different types of noises present in ECG signal are Base-Line Wander, Power-Line Interface, Muscle Artefacts, Electrode contact noise. One of these is arrhythmia, in which the heart's regular rhythm is altered by damage to its muscles and an electrolyte imbalance. A hybrid technique is utilized to identify and categorize arrhythmia by combining Multivariate Empirical Mode Decomposition (MEMD) and Artificial Neural Network (ANN). Multilayer feed forward neural networks are utilized for classification, and these networks are trained utilizing back propagation algorithms. Two key properties, the RR interval and Heart Rate, are retrieved from the ECG signal for the identification of Arrhythmia when MEMD is employed to denoise multichannel signals. Tachycardia and bradycardia are two subtypes of arrhythmia based on these characteristics. The Extraction of features and classification is to be done using Convolution neural network (CNN) classifier and the results obtained using CNN.

Baseline Wander, Powerline Interface, Muscle Artifacts, Arrhythmia, Tachycardia, Electrocardiogram, Multivariate Empirical Mode Decomposition (MEMD), Artificial Neural Network (ANN), Convolution Neural Network (CNN)

"DIAGNOSIS OF TACHYCARDIA ARRHYTHMIA USING MEMD AND CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.d117-d126, October-2022, Available :http://www.jetir.org/papers/JETIR2210327.pdf

"DIAGNOSIS OF TACHYCARDIA ARRHYTHMIA USING MEMD AND CONVOLUTIONAL NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppd117-d126, October-2022, Available at : http://www.jetir.org/papers/JETIR2210327.pdf