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Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines

Felipe Alonso-Atienza, Eduardo Morgado, Lorena Fernandez-Martinez, Arcadi Garcia-Alberola, Jose Luis Rojo-Alvarez
2014 IEEE Transactions on Biomedical Engineering  
In this study, we present a novel life-threatening arrhythmias detection algorithm that combines a number of previously proposed ECG parameters by using support vector machines classifiers.  ...  Index Terms-Feature selection (FS), support vector machines (SVM), ventricular fibrillation (VF) detection. 0018-9294  ...  ACKNOWLEDGMENT The authors would like to thank toÓscar Barquero-Pérez and Rebeca Goya-Esteban for their kindly help in programming the complexity measurement and the sample entropy parameters.  ... 
doi:10.1109/tbme.2013.2290800 pmid:24239968 fatcat:s55cocz4gfdghoj4qyb3hthmvi

A Generic and Robust System for Automated Detection of Different Classes of Arrhythmia

Vandana Singh, U. Srinivasulu Reddy, G. Manju Bhargavia
2020 Procedia Computer Science  
Cardiovascular Arrhythmias (irregular beat) are related to the sudden death, can be characterized into two kinds, life-threatening (dangerous) and non-life-threatening.  ...  The objective of this paper is the classification of arrhythmias on the ECG as per AAMI standards. First beat detection and then on a given window size beat segmentation is performed.  ...  Abstract Cardiovascular Arrhythmias (irregular beat) are related to the sudden death, can be characterized into two kinds, life-threatening (dangerous) and non-life-threatening.  ... 
doi:10.1016/j.procs.2020.03.199 fatcat:tsd5ya3xsjb7xhnxmj6mc647im

Premature ventricular beat detection by using spectral clustering methods

B.R. Ribeiro, A.M. Marques, J.H. Henriques, M.A. Antunes
2007 2007 Computers in Cardiology  
In a third stage, Support Vector Machines (SVM) which are benchmarked against several techniques have been chosen for PVC detection.  ...  By applying SVM Recursive Feature Elimination (SVM RFE) where the weight magnitude is used as ranking criterion we reduced the feature dimension to smaller sets.  ...  Acknowledgements MyHeart European Project IST-2002-507816 is gratefully acknowledged for partial financing support.  ... 
doi:10.1109/cic.2007.4745443 fatcat:z2gifhmsgvhj7noo6jxfa5h2ie

A TRANSFER LEARNING APPROACH BY USING 2-D CONVOLUTIONAL NEURAL NETWORK FEATURES TO DETECT UNSEEN ARRHYTHMIA CLASSES

Emre CİMEN
2020 Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering  
Then, we train a ν-Support Vector Machines model with only normal heartbeats and predict if a test sample is normal or arrhythmic.  ...  In this study, the arrhythmia classification problem is defined as an anomaly detection problem. We use ECG signals as inputs of the model and represent them with 2-D images.  ...  ACKNOWLEDGEMENT This study is supported by the Scientific Research Projects commission of Eskisehir Technical University under the grant number 20ADP131.  ... 
doi:10.18038/estubtda.755500 fatcat:oq6uqclicvc65mp5bjg7xi2xfe

CHOOSING REAL-TIME PREDICTORS FOR VENTRICULAR ARRHYTHMIA DETECTION

BERNARDETE RIBEIRO, AMÂNDIO MARQUES, JORGE HENRIQUES, MANUEL ANTUNES
2007 International journal of pattern recognition and artificial intelligence  
The risk of developing life-threatening ventricular arrhythmias in patients with structural heart disease is higher with increased occurrence of premature ventricular complex (PVC).  ...  In particular, recently developed sparse Bayesian methods, such as, Relevance Vector Machines (RVM), present a parsimonious solution when compared with Support Vector Machines (SVM), yet revealing competitive  ...  Acknowledgments We thank the anonymous reviewers for the useful comments which helped to improve the paper.  ... 
doi:10.1142/s0218001407005934 fatcat:vb3nntgucje55cznwulhlmw7zm

Identification of Premature Ventricular Contraction in ECG Signals – A Review

V. Sharmila
2018 International Journal for Research in Applied Science and Engineering Technology  
Several Promising algorithms based on autoregressive (AR) models, artificial neural networks (ANN), support vector machines (SVM), discrete cosine transform (DCT), Hilbert transform (HT), teager energy  ...  Simulations are carried out using Matlab software. Various records of MIT BIH arrhythmia database are used for testing.  ...  Detection of PVCS with Support Vector Machine 13 Support vector machines (SVMs) are supervised learning models for classification or pattern recognition.  ... 
doi:10.22214/ijraset.2018.2033 fatcat:gk6dvkn76zfmlprp7k46eznpha

Emerging Concepts and Applied Machine Learning Research in Patients with Drug-Induced Repolarization Disorders

Mina Bjelogrlic, Arnaud Robert, Arnaud Miribel, Mehdi Namdar, Baris Gencer, Christian Lovis, François Girardin
2020 Studies in Health Technology and Informatics  
The paper presents a review of current research to develop predictive models for automated detection of drug-induced repolarization disorders and shows a feasibility study for developing machine learning  ...  electronic application for the detection of adverse drug events with data mining and natural language processing; and to compute individual-based predictive scores in order to further identify clinical  ...  The classification, according to the resulting feature vectors is done using classifiers such as linear discriminant (LD), support vector machines (SVM) [19] , Random Forests [20] , and ensemble classifiers  ... 
doi:10.3233/shti200150 pmid:32570374 fatcat:rodrhjh2urfclkpsiygwbdbrgy

Electrocardiogram Pattern Recognition and Analysis Based on Artificial Neural Networks and Support Vector Machines: A Review

Mario Sansone, Roberta Fusco, Alessandro Pepino, Carlo Sansone
2013 Journal of Healthcare Engineering  
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)  ...  In particular, we focus on features commonly used for heartbeat classification.  ...  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

Efficient and robust ventricular tachycardia and fibrillation detection method for wearable cardiac health monitoring devices

Eedara Prabhakararao, M. Sabarimalai Manikandan
2016 Healthcare technology letters  
The method achieves an average sensitivity (Se) of 99.61%, specificity (Sp) of 99.96%, and overall accuracy (OA) of 99.92% in detecting VTVF and non-VTVF episodes by using a ZCR feature.  ...  VTVF detection; and (iv) peak-to-peak interval (PPI) feature based VT/VF discrimination.  ...  A life threatening arrhythmia detection algorithm using the 14 features extracted from the detail coefficients at levels of 3 and 4 of the Haar transform [7] .  ... 
doi:10.1049/htl.2016.0010 pmid:27733933 pmcid:PMC5047284 fatcat:xyihteyxgfalnapfy22wdomm6a

Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

Artzai Picon, Unai Irusta, Aitor Álvarez-Gila, Elisabete Aramendi, Felipe Alonso-Atienza, Carlos Figuera, Unai Ayala, Estibaliz Garrote, Lars Wik, Jo Kramer-Johansen, Trygve Eftestøl, Steve Lin
2019 PLoS ONE  
The proposed architecture was compared to 1D-CNN only deep learners, and to a classical approach based on VF-detection features and a support vector machine (SVM) classifier.  ...  ECG feature extraction and machine learning have been successfully used to detect ventricular fibrillation (VF) in AED shock decision algorithms.  ...  Detection of life-threatening arrhythmias using feature selection and support vector machines.  ... 
doi:10.1371/journal.pone.0216756 pmid:31107876 pmcid:PMC6527215 fatcat:bui673jh35bjxlxcgv3tolgmym

Multiclass Classification of Cardiac Arrhythmia Using Improved Feature Selection and SVM Invariants

Anam Mustaqeem, Syed Muhammad Anwar, Muahammad Majid
2018 Computational and Mathematical Methods in Medicine  
For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence  ...  The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique.  ...  Conflicts of Interest The authors declare that there are no conflicts of interest regarding the publication of this paper.  ... 
doi:10.1155/2018/7310496 pmid:29692863 pmcid:PMC5859855 fatcat:h2rmvkmfh5eglplozpa7z7tjzi

Detection of Life Threatening Ventricular Arrhythmia Using Digital Taylor Fourier Transform

Rajesh K. Tripathy, Alejandro Zamora-Mendez, José A. de la O Serna, Mario R. Arrieta Paternina, Juan G. Arrieta, Ganesh R. Naik
2018 Frontiers in Physiology  
The least square support vector machine (LS-SVM) classifier with linear and radial basis function (RBF) kernels is employed for detection and classification of VT vs.  ...  This paper introduces a novel method for detection and classification of life-threatening ventricular arrhythmia episodes.  ...  The combination of Taylor-Fourier magnitude and phase features and LSSVM classifier has been used for detection of various life threatening arrhythmia.  ... 
doi:10.3389/fphys.2018.00722 pmid:29951004 pmcid:PMC6008495 fatcat:qrqmnfsiojgqzoxsp43synzkcq

Data Mining Algorithms in Healthcare

Anjali Dwivedi, Kulsoom Rehman, Mayuri Ghosh, R. Raman
2018 International Journal of Computer Applications  
Data mining is the process of examining large pre-existing databases in order to generate new information.  ...  It discovers patterns in large datasets using various data mining algorithms to extract information. These data mining algorithms are extensively used in healthcare industry.  ...  [4] .TOPIC:-Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal''.  ... 
doi:10.5120/ijca2018916901 fatcat:7yl56n4nvnef3caakgtentiz3m

A Review Study for Electrocardiogram Signal Classification

Lana Abdulrazaq Abdulla, Muzhir Shaban Al-Ani
2020 UHD Journal of Science and Technology  
wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor.  ...  Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects.  ...  Arrhythmias can be divided into two parts, which are life-threatening and non-life-threatening arrhythmias, a long-term ECG classification is required for the diagnosis of non-life-threatening arrhythmias  ... 
doi:10.21928/uhdjst.v4n1y2020.pp103-117 fatcat:7gpxdwtbonczxm6ojdofhzarma

Enhancing accuracy of arrhythmia classification by combining logical and machine learning techniques

Vignesh Kalidas, Lakshman S Tamil
2015 2015 Computing in Cardiology Conference (CinC)  
Robust classification of each arrhythmia is achieved using a combination of logical and SVM-based machine learning techniques.  ...  Information from electrocardiogram and photoplethysmogram signals, sampled at 250Hz, is used for logical analysis and to form the feature set.  ...  Robust classification of each arrhythmia was achieved with a combination of logical and Support Vector Machine (SVM) based machine learning techniques.  ... 
doi:10.1109/cic.2015.7411015 dblp:conf/cinc/KalidasT15 fatcat:cngvp7iwhveedhxlmwy53tfjvi
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