Journal article Open Access

Ecg Heartbeat Classification: Conceptual Understanding through Cnn & Rnn – A Machine Learning Approach

P. Rama Santosh Naidu; G. Lavanya Devi; Kondapalli Venkata Ramana


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    <dct:title>Ecg Heartbeat Classification: Conceptual Understanding through Cnn &amp; Rnn – A Machine Learning Approach</dct:title>
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    <dcat:keyword>Basic CNN, Deep Residual CNN, Convolution layer, Max pool block</dcat:keyword>
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    <dct:description>&lt;p&gt;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&amp;#39;s condition regularly. This study concentrates on various MachineLearning 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.&lt;/p&gt;</dct:description>
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