Ecg Heartbeat Classification: Conceptual Understanding through Cnn & Rnn – A Machine Learning Approach
P. Rama Santosh Naidu1, G. Lavanya Devi2, Kondapalli Venkata Ramana3

1P. Rama Santosh Naidu, Assistant Professor in Andhra University College of Engineering, (Andhra Pradesh), India.
2Dr. G. Lavanya Devi, Assistant Professor Department of CS & SE Andhra University College of Engineering, (Andhra Pradesh), India.
3Dr. K. Venkata Ramana, Assistant Professor in Andhra University College of Engineering, (Andhra Pradesh), India.

Manuscript received on November 12, 2020. | Revised Manuscript received on December 01, 2020. | Manuscript published on December 10, 2021. | PP: 143-147 | Volume-10 Issue-2, December 2020 | Retrieval Number: 100.1/ijitee.B82851210220| DOI: 10.35940/ijitee.B8285.1210220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In recent days Machine Learning has become major study aspect in various applications that includes medical care where convenient discovery of anomalies in ECG signals plays an important role in monitoring patient’s condition regularly. This study concentrates on various Machine Learning techniques applied for classification of ECG signals which include CNN and RNN. In the past few years, it is being observed that CNN is playing a dominant role in feature extraction from which we can infer that machine learning techniques have been showing accuracy and progress in classification of ECG signals. Therefore, this paper includes Convolutional Neural Network and Recurrent Neural Network which is being classified into two types for better results from considerably increased depth. 
Keywords: Basic CNN, Deep Residual CNN, Convolution layer, Max pool block.