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Automated ECG Diagnosis

Upasani D.E.
<span title="">2012</span> <i title="IOSR Journals"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xkfue5ht4jb3xjr2swegjpni24" style="color: black;">IOSR Journal of Engineering</a> </i> &nbsp;
Myocardial ischemia & other cardiac disorder diagnosis using long duration electrocardiographic recordings is a simple and non-invasive method that needs further development in order be used in the everyday  ...  Several techniques that automate ischemia& other cardiac detection have been proposed during the last decade which are under evaluation.  ...  This network is trained using the beats contained in the initial 15% of the ECG.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.9790/3021-020512651269">doi:10.9790/3021-020512651269</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ihuvjetstveyhgxuybquzbh2oe">fatcat:ihuvjetstveyhgxuybquzbh2oe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180602122505/http://www.iosrjen.org/Papers/vol2_issue5/BE2512651269.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/16/41/1641543b1af528411a09ded522928b9a731d4334.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.9790/3021-020512651269"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm

M Ashtiyani, S Navaei Lavasani, A Asgharzadeh Alvar, M R Deevband
<span title="2018-08-21">2018</span> <i title="Salvia Medical Sciences Ltd"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ihyerw5bw5ayhhal3suktlyvma" style="color: black;">Journal of Biomedical Physics and Engineering</a> </i> &nbsp;
The approach contains 4 stages including HRV signal extraction from each ECG signal, feature extraction using DWT (entropy, mean, variance, kurtosis and spectral component β), best features selection by  ...  (NSR)), were selected from the MIT/BIH arrhythmia database.  ...  Acknowledgment The authors would like to acknowledge the Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31661/jbpe.v0i0.614">doi:10.31661/jbpe.v0i0.614</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/er5zd6djlnfotmxiiuny67z5qi">fatcat:er5zd6djlnfotmxiiuny67z5qi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190429044616/http://www.jbpe.org/Journal_OJS/JBPE/index.php/jbpe/article/download/614/479" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8b/f6/8bf6882ace6b88f80d2bb19f0c509eec7f5e9f78.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.31661/jbpe.v0i0.614"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Automated Method for Discrimination of Arrhythmias Using Time, Frequency, and Nonlinear Features of Electrocardiogram Signals

Shirin Hajeb-Mohammadalipour, Mohsen Ahmadi, Reza Shahghadami, Ki Chon
<span title="2018-06-29">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
of AF versus non-AF segments. (3) Premature ventricular contraction (PVC) detection on every non-AF segment, using a time domain feature, a frequency domain feature, and two features that characterize  ...  These studies consist of three major phases: preprocessing, extracting features, and classification of various types of arrhythmias in each ECG data segment.  ...  Conflicts of Interest: The authors declare no conflict of interest. Sensors 2018, 18, 2090  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s18072090">doi:10.3390/s18072090</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29966276">pmid:29966276</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6068712/">pmcid:PMC6068712</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xzusgs2gvrdz5ol3cit4hroafy">fatcat:xzusgs2gvrdz5ol3cit4hroafy</a> </span>
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Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification

M.I. Owis, A.H. Abou-Zied, A.-B.M. Youssef, Y.M. Kadah
<span title="">2002</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nrcoa2vuhjcvfctty6zgus57um" style="color: black;">IEEE Transactions on Biomedical Engineering</a> </i> &nbsp;
Abstract-We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization.  ...  The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia  ...  CONCLUSION The use of ECG signal features from nonlinear dynamical modeling was studied. The results from a large data set of actual ECG signals from five different classes were presented.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tbme.2002.1010858">doi:10.1109/tbme.2002.1010858</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/12083309">pmid:12083309</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vkpr2ympsngtxewguww6fvmnrq">fatcat:vkpr2ympsngtxewguww6fvmnrq</a> </span>
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A Review on Deep Learning Methods for ECG Arrhythmia Classification

Zahra Ebrahimi, Mohammad Loni, Masoud Daneshtalab, Arash Gharehbaghi
<span title="">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/d5wanubrsjhehcfo2eijwbv55m" style="color: black;">Expert Systems with Applications: X</a> </i> &nbsp;
Automated detection of 505 arrhythmias using different intervals of tachycardia ecg segments with convolutional neural network. Information sciences, 405, 81-90. Acharya, U. R., Fujita, H., Oh, S.  ...  proposed a multiple-feature-branch Convolutional Neural Network (MFB-CNN) for 315 automated myocardial (MI) detection and localization using ECG.  ...  interest The authors declare that they have no conflict of interest in this paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.eswax.2020.100033">doi:10.1016/j.eswax.2020.100033</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gpdtrhy2ejcl3cqpdgjctworje">fatcat:gpdtrhy2ejcl3cqpdgjctworje</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200621023750/https://pdf.sciencedirectassets.com/321099/AIP/1-s2.0-S2590188520300123/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjELL%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCICNwom3kk24CXsqtI8ce4hhQs3IliOgH3ArUfhNubaduAiAHFUhNst55JpvJiLcWFwV%2FypXQFmxDdJoiwLss0SgJ4Sq0Awg6EAMaDDA1OTAwMzU0Njg2NSIMwy0VinGVjj3w3SASKpEDnZ753DjlOn7ZbqsrD9hxZaEp%2BO5KZNKprSOdAo2HdS3Jfdj%2BBEIyy5%2FfXJZ1CbZRfuPrP%2FAxw4bj96OpnSx5wsN%2B30%2FnC%2BAY%2BlLAwcJZmEvvpwGDTbKxA53VzYEKr%2FkCYkqtb0YEoWNg9%2BHHNhnjBdWb472d2gDL9lwJJEELGsem1rni0rWNWYAsK1VmzkKMtr0aTw5FHDIOfkdu9LACLR1ordshgibThodDKfB6OJnWp9L1q%2FBg7rXuU4SnSxu2WiHHGKvppL7e5Vd99vwVe2au82mg9MS5XTObcmjqbiDABwQb7TmuUvVUa%2Bfyd2q3hYu1zAr1HYbyqIl9hQJoEDKcJXMikzs2onyt5tlQVuq967UlGY0PMtZWiUT1vbRI8yHBm%2FRY%2BJfedvK6I4EHxf60C00hOI0vcs0CwbdJ0mDVdmO%2BlKk0votWqs43LVaJUDpJldjk1M8Uh1OSU70CHpIXqx07PyOYKnyG566DiCq8MrBJ0va74tNoWNiMmEYOHN1yKknb0Uph3s2VhimgOZMw5eW69wU67AFtN0dCE7Fd3TfaLWoU6SfC6D3ogNPeGo%2BmOBM5mYqTPy%2F9uc9iy1WXl5PTOOk7OU4BMqmGfDiJLHxbpVy%2F%2BLE9kOfVXKbPddfYzXYHz%2F3b9BGI1%2FLhWeUKb3YCMAhdLmj1ipPjJtbIQyBw3nLD7aPTT8GuveIHN0Rmt%2FmVWX2%2BzjcQwobxlvwWOTscjDCbXJnj8hR8EsoGwo%2FTcLuRWkSQYt0EabZEzdFyHkNAIXJe0XPLC1%2BuTwU8Ftfv5aatKgcv8w8IVWEmraJng3cnPkQhlaCg94q9R%2FdUi2nmQyb%2B%2ByDqahIh2K3gYr%2BYBQ%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20200621T023745Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTY47VVJHWM%2F20200621%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=c341181e718c5c595088f34b8447447a5d87cb9d7b2b3358eab15c602323c62b&amp;hash=135c58195542652616f2cacb751ea21c9e98c3aee39cc4974ef53cf7a7b9fa7a&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S2590188520300123&amp;tid=spdf-153ed2bd-f4fe-407c-a344-801896b85d5f&amp;sid=8e4d64463789e7498d0940130e335e1a7258gxrqa&amp;type=client" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.eswax.2020.100033"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Supraventricular Tachycardia Detection and Classification Model of ECG signal Using Machine Learning [article]

Pampa Howladar, Manodipan Sahoo
<span title="2021-12-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This work presents a supraventricular arrhythmia prediction model consisting of a few stages, including filtering of noise, a unique collection of ECG characteristics, and automated learning classifying  ...  Investigation on the electrocardiogram (ECG) signals is an essential way to diagnose heart disease since the ECG process is noninvasive and easy to use.  ...  Using the feature R-peak, we can easily calculate heart Input: R-peak value collected from algorithm 1 of a recorded rate beat (HBR), shown in Fig. 8.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.12953v1">arXiv:2112.12953v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/juwpfyjdgjb3hgjlcfr7t5y4cq">fatcat:juwpfyjdgjb3hgjlcfr7t5y4cq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220106025804/https://arxiv.org/ftp/arxiv/papers/2112/2112.12953.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7f/ba/7fbaddd8e637fdf53e289908e7a4bb395b58615d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.12953v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces

Manuel M. Casas, Roberto L. Avitia, Felix F. Gonzalez-Navarro, Jose A. Cardenas-Haro, Marco A. Reyna
<span title="">2018</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cswd2rqrire6lgrsm56kv4adue" style="color: black;">Journal of Healthcare Engineering</a> </i> &nbsp;
In this work, 80 different features from 108,653 ECG classified beats of the gold-standard MIT-BIH database were extracted in order to classify the Normal, PVC, and other kind of ECG beats.  ...  This gave us a promising path in the development of automated mechanisms for the detection of PVC complexes.  ...  Acknowledgments e authors want to express their gratitude to the National Council of Science and Technology (Conacyt) for its scholarships and materials in support for this research.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2018/2694768">doi:10.1155/2018/2694768</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29861881">pmid:29861881</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5971262/">pmcid:PMC5971262</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g2yoriskizb6pmx3lun6fkdpya">fatcat:g2yoriskizb6pmx3lun6fkdpya</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200208114921/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5971262&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/eb/2a/eb2afdbbe31b3f5a75aae2a9e1b9ba3eddcdf4c2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2018/2694768"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5971262" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Usefulness of Nonlinear Analysis of ECG Signals for Prediction of Inducibility of Sustained Ventricular Tachycardia by Programmed Ventricular Stimulation in Patients with Complex Spontaneous Ventricular Arrhythmias

Ornella Durin, Claudio Pedrinazzi, Giorgio Donato, Rita Pizzi, Giuseppe Inama
<span title="">2008</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3tsl7y5ianb3znz7e2o4zjf7r4" style="color: black;">Annals of Noninvasive Electrocardiology</a> </i> &nbsp;
The aim of our study was to assess the effectiveness of the nonlinear analysis (NLA) of ECG in predicting the results of invasive electrophysiologic study (EPS) in patients with ventricular arrhythmias  ...  Ann Noninvasive Electrocardiol 2008;13(3):219-227 ventricular arrhythmias; ECG; nonlinear analysis; electrophysiologic study  ...  These data were finally analyzed by nonlinear mathematical functions, as explained in detail below. Nonlinear techniques for the automated detection of arrhythmias have been proposed in the past.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/j.1542-474x.2008.00224.x">doi:10.1111/j.1542-474x.2008.00224.x</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/18713321">pmid:18713321</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bxwsycpanfb57om4hhxgaonjhe">fatcat:bxwsycpanfb57om4hhxgaonjhe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170923021351/https://air.unimi.it/retrieve/handle/2434/140458/262090/2008%20ANE.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ea/37/ea375513a77c3ba7903c1d1c1b8900becacb9f10.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1111/j.1542-474x.2008.00224.x"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification

V. S. R. Kumari
<span title="">2015</span> <i title="Science Publishing Group"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h4oojzivc5c5rnp4nf5nv7zy4m" style="color: black;">Communications</a> </i> &nbsp;
Symlet extracts RR intervals from ECG data as features while symmetric uncertainty assures feature reduction. GA optimizes learning rate and momentum.  ...  ECGs help in identifying cardiac arrhythmia because they have diagnostic information. ECG arrhythmia detection accuracy improves by using machine learning and data mining methods.  ...  Classification of eight different types of arrhythmia was done using a Probabilistic Neural Network (PNN) classifier from ECG beats.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11648/j.com.20150305.21">doi:10.11648/j.com.20150305.21</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oxwvx6ehnzdyfk7jzqid73q55a">fatcat:oxwvx6ehnzdyfk7jzqid73q55a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170815000111/http://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20150305.21.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/53/b6/53b684c34a0821b72961a6020b79848a4c094121.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11648/j.com.20150305.21"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Novel Approach for Different Morphological Characterization of ECG Signal [chapter]

R. Harikumar, S. N. Shivappriya
<span title="">2013</span> <i title="Springer India"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/buexlaapefetfatuvtspxuwmve" style="color: black;">Lecture Notes in Electrical Engineering</a> </i> &nbsp;
From the benchmark data bases: MIT-BIH Arrhythmia, QT and European ST-T database the ECG is fetched then the noise is removed from the digitized ECG signal.  ...  The earlier detection of Cardiac arrhythmia of ECG waves is important to prevent cardiac disorders.  ...  Acknowledgments The authors thank the Management and the Principal of Bannari Amman Institute of Technology, Sathyamangalam and Kumaraguru college of Technology, Coimbatore for providing excellent computing  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-81-322-0997-3_2">doi:10.1007/978-81-322-0997-3_2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gklsztjhtbctdp3sdfiw2eyez4">fatcat:gklsztjhtbctdp3sdfiw2eyez4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200710075159/https://www.springer.com/cda/content/document/cda_downloaddocument/9788132209966-c2.pdf?SGWID=0-0-45-1376248-p174730956" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/aa/1b/aa1b0c7fb29664e2414bfd794a7e7aea8dc8b735.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-81-322-0997-3_2"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Efficient Algorithm for Early Detection of Myocardial Ischemia using PCA based Features

H. S. Niranjana Murthy, M. Meenakshi
<span title="2016-10-28">2016</span> <i title="Indian Society for Education and Environment"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wffwpj3q45g5zfjzfeyagk5uea" style="color: black;">Indian Journal of Science and Technology</a> </i> &nbsp;
The extraction of clinically useful features is carried out by selecting ST-T complex from ECG beat samples followed by dimensionality reduction using PCA.  ...  Extracting the features from ECG signal is helpful in detecting cardiac ischemia, but difficult when the size of the ECG data is huge.  ...  Very little effort is made towards automated diagnosis of arrhythmia from ECG signal, particularly myocardial ischemia.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17485/ijst/2016/v9i39/95788">doi:10.17485/ijst/2016/v9i39/95788</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gujmubfmfnenfmfw6dvechcyea">fatcat:gujmubfmfnenfmfw6dvechcyea</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190427172005/http://www.indjst.org/index.php/indjst/article/download/95788/74477" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17485/ijst/2016/v9i39/95788"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Wavelet based detection of ventricular arrhythmias with neural network classifier

Sankara Subramanian Arumugam, Gurusamy Gurusamy, Selvakumar Gopalasamy
<span title="">2009</span> <i title="Scientific Research Publishing, Inc,"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ok4mzlk3yfcl5gpvlswfyknepa" style="color: black;">Journal of Biomedical Science and Engineering</a> </i> &nbsp;
This paper presents an algorithm based on the wavelet decomposition, for feature extraction from the Electrocardiogram (ECG) signal and recognition of three types of Ventricular Arrhythmias using neural  ...  The algorithm is applied on the ECG registrations from the MIT-BIH arrhythmia and malignant ventricular arrhythmia databases. We applied Daubechies 4 wavelet in our algorithm.  ...  Conventionally, a typical heart beat is identified from the ECG and the component waves of the QRS, T and P waves are characterized using measurements such as magnitude, duration and area (Figure 1) .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/jbise.2009.26064">doi:10.4236/jbise.2009.26064</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ubbt6h3k5netjal3hx2nc4m344">fatcat:ubbt6h3k5netjal3hx2nc4m344</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20140910071115/http://www.scirp.org/journal/PaperDownload.aspx?paperID=800" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9e/01/9e013baef3351476281b1628bf9546733937d1f3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/jbise.2009.26064"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Automatic detection and classification of cardiac arrhythmia using neural network

N N. S. V Rama Raju, V Malleswara Rao, I Srinivasa Rao
<span title="2018-07-11">2018</span> <i title="Science Publishing Corporation"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/piy2nrvrjrfcfoz5nmre6zwa4i" style="color: black;">International Journal of Engineering &amp; Technology</a> </i> &nbsp;
After segmentation both feature of DWT and DTCWT is combined for feature extraction, statistical feature has been calculated to re-duce the overhead of classifier.  ...  of ECG classification claims 98 -99 % of accuracy under different training and testing situation.  ...  The procedure for feature extraction using DT-CWT is described as follows: • A window of 256 samples around R-vertex (out of which 128 from left and 128 from right) is selected for the extraction of QRS  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14419/ijet.v7i3.14084">doi:10.14419/ijet.v7i3.14084</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4drgkztpefeb3ewvung5twutni">fatcat:4drgkztpefeb3ewvung5twutni</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/201904270212/https://www.sciencepubco.com/index.php/ijet/article/download/14084/6398" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/eb/32/eb32a685cf844c634add60211544a8df60e24bb0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.14419/ijet.v7i3.14084"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Identification of Premature Ventricular Contraction in ECG Signals – A Review

V. Sharmila
<span title="2018-02-28">2018</span> <i title="International Journal for Research in Applied Science and Engineering Technology (IJRASET)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hsp44774azcezeyiq4kuzpfh5a" style="color: black;">International Journal for Research in Applied Science and Engineering Technology</a> </i> &nbsp;
This paper presents an exhaustive review of several methods used in identifying PVC arrhythmia in ECG signals.  ...  The electrocardiogram (ECG) signal is the graphical representation of electrical activity of the heart. Diagnosis of most of the cardiac problems requires ECG feature extraction.  ...  analysis of nonlinear ECG data is its attractive feature of tracking the instantaneous changes in the energy of nonlinear signals.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22214/ijraset.2018.2033">doi:10.22214/ijraset.2018.2033</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gk6dvkn76zfmlprp7k46eznpha">fatcat:gk6dvkn76zfmlprp7k46eznpha</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200212000124/http://ijraset.com/fileserve.php?FID=13459" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/d4/38/d4387f2cebe2d47851b57d80023702d50d559472.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.22214/ijraset.2018.2033"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Identification of Premature Ventricular Contraction (PVC) Caused by Disturbances in Calcium and Potassium Ion Concentrations Using Artificial Neural Networks

Júlio César Dillinger Conway, Caroline Araújo Raposo, Sergio Diaz Contreras, Jadson Cláudio Belchior
<span title="">2014</span> <i title="Scientific Research Publishing, Inc,"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pw3myjtulrgx5bhs6f5qrvmste" style="color: black;">Health (Irvine, Calif.)</a> </i> &nbsp;
An arrhythmia database of a widely used experimental data was considered to simulate different ECG signals and also for training and validation of the methodology.  ...  The procedure can be, in principle, used to identify changes in the morphology of the ECG signal due to alterations in calcium and potassium concentrations.  ...  [8] proposed a methodology based on nonlinear dynamics of the ECG signals for arrhythmia characterization, using correlation dimension and Lyapunov exponent to model five different classes of ECG signals  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/health.2014.611162">doi:10.4236/health.2014.611162</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c5vfl4paebay3pgzjspula44by">fatcat:c5vfl4paebay3pgzjspula44by</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170923022111/http://file.scirp.org/pdf/Health_2014052910021572.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fb/fc/fbfc7810c53af6cbdb95b3e756c91c3d072e701f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/health.2014.611162"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>
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