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Various Networks Used for ECG Signals, Heart Beats and ECG Feature's Classification
2020
International Journal of Engineering and Advanced Technology
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Different ECG signals as well as ECG parameters such as heart beats, features can be classified according to requirement. In this paper different classification networks have studied. ...
ECG is a graphical representation of heart's electrical activity such as electrical reploarization and depolarization of heart. ...
Further , sequential minimal optimized support vector machine (SMO-SVM) classifier has been used for classification of ECG heart beats based on extracted features into five beat types (i.e. left bundle ...
doi:10.35940/ijeat.c6234.029320
fatcat:xxq2ftb3svbq5hfib6ppf227fa
A Personalized Arrhythmia Monitoring Platform
2018
Scientific Reports
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These coefficients or features are classified using artificial bee colony (ABC) optimized twin least-square support vector machine (LSTSVM) for classifying the different categories of ECG signals. ...
The feature extraction and classification techniques are significant tools widely used in the automated classification of arrhythmias. ...
Acknowledgements The authors acknowledge the Department of Science and Technology, Government of India for sponsoring this research work under DST-INSPIRE Fellowship [IF120841] Scheme. ...
doi:10.1038/s41598-018-29690-2
pmid:30061754
pmcid:PMC6065378
fatcat:dctl7jdsyfabrcaulqesexsw2a
ECG Beats Fast Classification Base on Sparse Dictionaries
[article]
2020
arXiv
pre-print
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Feature extraction plays an important role in Electrocardiogram (ECG) Beats classification system. ...
In VQ method, a set of dictionaries corresponding to segments of ECG beats is trained, and VQ codes are used to represent each heartbeat. ...
Our method can increases the diversity of dictionary structure and makes the feature more efficient. Finally, we use support vector machine (SVM) (Vapnik V N, 1998) as classifier. ...
arXiv:2009.03792v1
fatcat:tnsrl2vwgncgpkfrfvz3ayawye
Disease Classification and Biomarker Discovery Using ECG Data
2015
BioMed Research International
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With the development of personal ECG monitors, large amounts of ECGs are recorded and stored; therefore, fast and efficient algorithms are called for to analyze the data and make diagnosis. ...
In this paper, an efficient and easy-to-interpret procedure of cardiac disease classification is developed through novel feature extraction methods and comparison of classifiers. ...
Acknowledgments The work was supported by the Science Foundation of Shanghai (Project no. 14ZR1412900), the 111 Project (B14019), and Program of Shanghai Subject Chief Scientist (14XD1401600). ...
doi:10.1155/2015/680381
pmid:26688816
pmcid:PMC4672117
fatcat:yf236hibefc2tjdw55cedue64y
Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review
2013
Journal of Healthcare Engineering
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In particular, we focus on features commonly used for heartbeat classification. ...
Considering the vast literature in the field and the limited space of this review, we dedicated a detailed discussion only to a few classifiers (Artificial Neural Networks and Support Vector Machines) ...
This work was conducted partially under the financial support of the FARO (Finanziamento per l'Avvio di Ricerche Originali) Programme in the framework of the project "Un sistema elettronico di elaborazione ...
doi:10.1260/2040-2295.4.4.465
pmid:24287428
fatcat:z373jqpajjai7lbovclm46nmfe
A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
2019
Frontiers in Physics
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We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. ...
These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches. ...
This d-dimensional vector (d = 63) is the input for the classification algorithm.
Classification Algorithm: Echo State Network Our classifier is built upon an ESN with a ring topology. ...
doi:10.3389/fphy.2019.00103
fatcat:gyxtvpbdlnht7lnbp7qjlsdbge
Efficient Detection of Ventricular Late Potentials on ECG Signals Based on Wavelet Denoising and SVM Classification
2019
Information
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Five features were then extracted and used as inputs of a classifier based on a machine learning approach. ...
Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/info10110328
fatcat:ykmyd734rbh4nd5fqvrbhpxvcy
Origins of ECG and Evolution of Automated DSP Techniques: A Review
[article]
2021
arXiv
pre-print
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This review focuses on the evolution of the ECG, and covers the most recent signal processing schemes with milestones over last 150 years in a systematic manner. ...
Over the years researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. ...
This work is supported by Science and Engineering Research Board (SERB) (Grant Number:DST SERB Grant CRG/2019/004747) under Department of Science and Technology (DST), Government of India. ...
arXiv:2105.08938v1
fatcat:4bf6ojrlqrg5hlhdwe2gbi2xua
Origins of ECG and Evolution of Automated DSP Techniques: A Review
2021
IEEE Access
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This work is supported by Department of Science and Technology (Grant No. :DST SERB Grant CRG/2019/004747), Government of India. ...
ACKNOWLEDGMENT The authors would like to thank Dhirubhai Ambani Institute of Information and Communication Technology for the research support. ...
Support Vector Machine (SVM) is also widely used for classification of different types of signals. ...
doi:10.1109/access.2021.3119630
fatcat:kbxghskiu5f5hc3vrmi6yrysba
Stages-Based ECG Signal Analysis from Traditional Signal Processing to Machine Learning Approaches: A Survey
2020
IEEE Access
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and ECG signal classification along with comparative discussions among the reviewed studies. ...
We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering ...
Researchers have detected arrhythmias using SVM [96] , [98] , [101] with Sequential Minimal Optimization-SVM (SMO-SVM)) [102] , Multi-class Support Vector Machine (MSVM)/Complex Support Vector Machine ...
doi:10.1109/access.2020.3026968
fatcat:33s5hrmwkvhnzetozcv3hlwkcu
Development of an embedded device for real-time detection of atrial fibrillation and atrial flutter in single-channel ECG, using optimised classification based on a large training corpus
2021
Zenodo
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The algorithm uses morphological analysis of the averaged ECG shape, properties of the R/R interval distribution and spectral analysis of the ECG to create a feature vector used for classification. ...
Building on an earlier proof of concept project work by the author, this thesis presents a fully integrated, custom device, using an advanced classification algorithm trained on thousands of short, annotated ...
While the pattern recognition course proceeds to discuss more advanced methods such as Support Vector Machines (SVM), the ecghelper2 algorithm reaches good performance with LDA already, which is fast and ...
doi:10.5281/zenodo.4560150
fatcat:4cvxfe4jgreuxkbrdqiai2usiq
Energy-Efficient FPGA Accelerator with Fidelity-Controllable Sliding-Region Signal Processing Unit for Abnormal ECG Diagnosis on IoT Edge Devices
2021
IEEE Access
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For more information, see https://creativecommons.org/licenses/by/Composition of the cost matrix for dynamic programming. ...
The existing template-based classification, which selects only one normal beat template, has a problem in that a normal beat with slight shape deformation is overdetected as an abnormal beat: thus, the ...
doi:10.1109/access.2021.3109875
fatcat:v7w5rgmtkvhapiw6wylyam6t2i
Interval Feature Transformation for Time Series Classification Using Perceptually Important Points
2020
Applied Sciences
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The IFT uses perceptually important points to segment the series dynamically into subsequences of unequal length, and then extract interval features from each time series subsequence as a feature vector ...
The IFT distinguishes the best top-k discriminative feature vectors from a data set by information gain. ...
By quantifying the classification ability of different dimensions, k dimensions with the strongest classification ability are finally selected, and the interval features of these k dimensions can be used ...
doi:10.3390/app10165428
fatcat:2gykerhrynea5j4ghnopg7323y
A Method for Automatic Identification of Reliable Heart Rates Calculated from ECG and PPG Waveforms
2006
JAMIA Journal of the American Medical Informatics Association
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The assessment of the waveforms is performed by a Support Vector Machine classifier and the independent computation of heart rate from the waveforms is performed by an adaptive peak identification technique ...
Results: The authors evaluated the method against 158 randomly selected data samples of trauma patients collected during helicopter transport, each sample consisting of 7-second ECG and PPG waveform segments ...
Support Vector Machine Classifier In this study, we employ our previously developed version of an SVM algorithm 16 to classify ECG and PPG waveforms. ...
doi:10.1197/jamia.m1925
pmid:16501184
pmcid:PMC1513657
fatcat:42igwym5oredximxv6xqbj6mze
Multiclass ECG Signal Analysis Using Global Average-Based 2-D Convolutional Neural Network Modeling
2021
Electronics
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Real-time ECG monitoring systems with advanced machine learning methods provide information about the health status in real-time and have improved user's experience. ...
The European ST-T database recorded with single lead L3 is used to validate the CNN classifier and achieved an accuracy of 99.23%, outperforming most existing solutions. ...
There are variations of SVM such as a Multiclass Support Vector Machine (MSVM) and Complex Support Vector Machine (CSVM) that can be used to classify ECG arrhythmia types into multiple classes, as presented ...
doi:10.3390/electronics10020170
fatcat:act6lvg2r5f2ji3xkvninetmli
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