AUTOMATED DETECTION OF CARDIAC ARRYHYTHMIA USING RECURRENT NEURAL NETWORK

Registration ID: IJNRD_200598

Published ID: IJNRD2306576

DOI: Click Here to Get

P.Arun Kumar , A.Manohor Reddy , K. Neeraj , Dr.Pregya Poonia

Automated face recognition, Convolutional neural networks(CNN),Image or video frame, machine learning(ML).

Cardiac arrhythmia is a condition where heartbeat is irregular. The goal of this paper is to apply deep learning techniques in the diagnosis of cardiac arrhythmia using ECG signals with minimal possible data pre-processing. We employ convolutional neural network (CNN), recurrent structures such as recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) and hybrid of CNN and recurrent structures to automatically detect the abnormality. Unlike the conventional analysis methods, deep learning algorithms don't have feature extraction-based analysis methods. The optimal parameters for deep learning techniques are chosen by conducting various trails of experiments. All trials of experiments are run for 1000 epochs with learning rate in the range . We obtain five-fold cross validation accuracy of 0.834 in distinguishing normal and abnormal (cardiac arrhythmia) ECG with CNN-LSTM. Moreover, the accuracy obtained by other hybrid architectures of deep learning algorithms is comparable to the CNN-LSTM.

Published Paper   E-Certificate

P.Arun Kumar, A.Manohor Reddy , K. Neeraj , & Dr.Pregya Poonia (June-2023). AUTOMATED DETECTION OF CARDIAC ARRYHYTHMIA USING RECURRENT NEURAL NETWORK. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(6), f739-f744. https://ijnrd.org/papers/IJNRD2306576.pdf

Volume 8 Issue 6, June-2023

Pages : f739-f744

Paper Reg. ID: IJNRD_200598

Published Paper Id: IJNRD2306576

Downloads: 000121985

Research Area: Engineering

Country: -, -, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2306576.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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