|
|||||||||||||||
|
INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
||||||||||||||
Issue: April 2024
Volume 9 | Issue 4
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.76
Issue per Year : 12
Volume Published : 9
Issue Published : 95
Article Submitted :
Article Published :
Total Authors :
Total Reviewer :
Total Countries :
Indexing Partner
Join RMS/Earn 300
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | AUTOMATED DETECTION OF CARDIAC ARRYHYTHMIA USING RECURRENT NEURAL NETWORK |
| Authors Name: | P.Arun Kumar , A.Manohor Reddy , K. Neeraj , Dr.Pregya Poonia |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_200598
|
| Published Paper Id: | IJNRD2306576 |
| Published In: | Volume 8 Issue 6, June-2023 |
| DOI: | |
| Abstract: | 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. |
| Keywords: | Automated face recognition, Convolutional neural networks(CNN),Image or video frame, machine learning(ML). |
| Cite Article: | "AUTOMATED DETECTION OF CARDIAC ARRYHYTHMIA USING RECURRENT NEURAL NETWORK", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 6, page no.f739-f744, June-2023, Available :http://www.ijnrd.org/papers/IJNRD2306576.pdf |
| Downloads: | 000118753 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID:IJNRD2306576 Registration ID: 200598 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): Page No: f739-f744 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2306576 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2306576 |
| Share Article: |
|
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijnrd.org
| Semantic Scholar | Microsaoft Academic | ORCID | Zenodo |
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | PUBLON |
| DRJI | SSRN | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Social Media
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Join RMS/Earn 300
| Copyright © 2024 - All Rights Reserved - IJNRD |
Facebook Twitter Instagram LinkedIn