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Feature Extraction and Classification of Segmented ECG Signals Based on Radial Basis Function and Random Forest Methodology

Rexy J, Velmani P, Rajakumar T.C
2021 International Journal on Cybernetics & Informatics  
Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia database and Noise Stress database are used for this implementation and the classes are identified based on the given dataset  ...  Hence this performance evaluation paves way for best classification algorithm selection or extension of the best methodology and it can be further optimized for better classification result.  ...  Classification is a supervised pattern of dividing the signals and variety of algorithms play a vital role in classifying the ECG signals based on the extracted features.  ... 
doi:10.5121/ijci.2021.100205 fatcat:njkqcailbvhenibhoeu645ieha

Patient-Specific ECG Classification with Integrated Long Short-Term Memory and Convolutional Neural Networks

Jiaquan WU, Feiteng LI, Zhijian CHEN, Xiaoyan XIANG, Yu PU
2020 IEICE transactions on information and systems  
This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN).  ...  In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data.  ...  Based on these prior works, our ECG classification method achieves a nice exploration in network topology.  ... 
doi:10.1587/transinf.2019edp7282 fatcat:hwummen5wrhybo7uysrwgx24ni

Computational Diagnostic Techniques for Electrocardiogram Signal Analysis

Liping Xie, Zilong Li, Yihan Zhou, Yiliu He, Jiaxin Zhu
2020 Sensors  
Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here.  ...  Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years.  ...  A novel entropy-based principal component analysis (EPCA) was developed to automatically select the optimal number of principal components for dimensionality reduction of ECG signals.  ... 
doi:10.3390/s20216318 pmid:33167558 pmcid:PMC7664289 fatcat:echda3mznbekrclhwj3e774gc4

Atrial Fibrillation Analysis Based On Blind Source Separation In 12-Lead Ecg

Pei-Chann Chang, Jui-Chien Hsieh, Jyun-Jie Lin, Feng-Ming Yeh
2010 Zenodo  
In this research, we also adopt a second-order blind identification approach to transform the sources extracted by ICA to more precise signal and then we use frequency domain algorithm to do the classification  ...  Because of the invisible waveform of atrial fibrillation in atrial activation for human, it is necessary to develop an automatic diagnosis system. 12-Lead ECG now is available in hospital and is appropriate  ...  Because AA has a narrowband spectrum, SOBI algorithm is appropriate for estimating the AA. E. Power Spectral Density Based Classification SOBI can separate the mixed uncorrelated sources.  ... 
doi:10.5281/zenodo.1330632 fatcat:qly3knhau5abpopv4tr2b5bu3u

New Hybrid (SVMs -CSOA) Architecture for classifying Electrocardiograms Signals

Assist. Prof., Assist. Prof.
2015 International Journal of Advanced Research in Artificial Intelligence (IJARAI)  
In this paper we present the SVM parameter optimization approach using novel metaheuristic for evolutionary optimization algorithms is Cat Swarm Optimization Algorithm (CSOA).  ...  a medical test that provides diagnostic relevant information of the heart activity is obtained by means of an ElectroCardioGram (ECG).  ...  ECG Classification ECG classification system based on Support Vector Machines SVMs One cycle of ECG signal consists of P-QRS-T wave, six types of beats including Normal sinusrhythm (N), a trial premature  ... 
doi:10.14569/ijarai.2015.040505 fatcat:b6unuqoe2zamjdqdelc3suyfpa

A Novel Approach to ECG Classification Based upon Two-Layered HMMs in Body Sensor Networks

Wei Liang, Yinlong Zhang, Jindong Tan, Yang Li
2014 Sensors  
This paper presents a novel approach to ECG signal filtering and classification.  ...  and the corresponding classification outcomes are deduced and shown on the BSN screen.  ...  Jindong Tan provided critical instructions on HMM based ECG classifications and the hardware prototype design.  ... 
doi:10.3390/s140405994 pmid:24681668 pmcid:PMC4029659 fatcat:zbera6ao2ndg5dpsgcjvr4alne

Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search [article]

Jindi Lv and Qing Ye and Yanan Sun and Juan Zhao and Jiancheng Lv
2021 arXiv   pre-print
Specifically, we initially search a cell architecture by Darts and then customize a novel CNN model for ECG classification based on the obtained cells.  ...  However, manually evaluating ECG signals is a complicated and time-consuming task.  ...  Based on the above inspiration, in this paper, we propose a novel method named Heart-Darts for effective classification of ECG signals. A.  ... 
arXiv:2105.00693v1 fatcat:uzikhqed4nenjnbrovmalecfqu

Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection

Yi-Li Tseng, Keng-Sheng Lin, Fu-Shan Jaw
2016 Computational and Mathematical Methods in Medicine  
Myocardial ischemia commonly manifests as ST- and T-wave changes on ECG signals.  ...  A novel classification method, sparse representation-based classification (SRC), is involved to improve the performance of the existing algorithms.  ...  Acknowledgment This work was supported by Grants 103-2218-E-002-026and 104-2221-E-030-007-from the Ministry of Science and Technology, Taiwan.  ... 
doi:10.1155/2016/9460375 pmid:26925158 pmcid:PMC4746342 fatcat:dnn6mzgtkrbphlnisim6xo32ke

A Novel Automatic Detection System for ECG Arrhythmias Using Maximum Margin Clustering with Immune Evolutionary Algorithm

Bohui Zhu, Yongsheng Ding, Kuangrong Hao
2013 Computational and Mathematical Methods in Medicine  
First, raw ECG signal is processed by an adaptive ECG filter based on wavelet transforms, and waveform of the ECG signal is detected; then, features are extracted from ECG signal to cluster different types  ...  This paper presents a novel maximum margin clustering method with immune evolution (IEMMC) for automatic diagnosis of electrocardiogram (ECG) arrhythmias.  ...  Technology (nos. 11XD1400100 and 11JC1400200), and the Fundamental Research Funds for the Central Universities.  ... 
doi:10.1155/2013/453402 pmid:23690875 pmcid:PMC3652208 fatcat:i4uo2fnwyfedzdfpoh3lgqvujy

Atrial Fibrillation Analysis Based on Blind Source Separation in 12-Lead ECG Data [chapter]

Pei-Chann Chang, Jui-Chien Hsieh, Jyun-Jie Lin, Feng-Ming Yeh
2010 Lecture Notes in Computer Science  
In this research, we also adopt a second-order blind identification approach to transform the sources extracted by ICA to more precise signal and then we use frequency domain algorithm to do the classification  ...  To extract the feature, some approaches base on either QRST cancellation through spatiotemporal method [5], average beat subtraction (ABS) [6] or blind source Pei-Chann Chang is with Yuan Ze University  ...  Because AA has a narrowband spectrum, SOBI algorithm is appropriate for estimating the AA. E. Power Spectral Density Based Classification SOBI can separate the mixed uncorrelated sources.  ... 
doi:10.1007/978-3-642-13923-9_31 fatcat:wvg5aavdg5eyhjdi5vl34ssa2a

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

Hongqiang Li, Danyang Yuan, Youxi Wang, Dianyin Cui, Lu Cao
2016 Sensors  
Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the  ...  A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency  ...  Several independent component analysis (ICA) algorithms were tested and analysed to identify various components with high accuracy in a particular algorithm based on biomedical data for classification  ... 
doi:10.3390/s16101744 pmid:27775596 pmcid:PMC5087529 fatcat:j3enr3aaxbdbfp42q3qyoag4qi


Semih ERGIN, Selcan KAPLAN BERKAYA, Alper Kursat UYSAL, Efnan SORA GUNAL, Serkan GUNAL, Mehmet Bilginer GULMEZOGLU
2020 Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering  
Six different sets of features based on QRS, time-domain, wavelet transform, and power spectral density are derived from ECG signals in this database.  ...  Initially, the feature vectors extracted from raw electrocardiogram (ECG) signals are projected into a particular subspace obtained via the Common Vector Approach, which is an effective subspace method  ...  PROPOSED FRAMEWORK BASED ON SUBSPACE PROJECTION AND DECISION TREE CLASSIFICATION In this work, a hybrid algorithm is proposed for the classification of ECG signals.  ... 
doi:10.18038/estubtda.630634 fatcat:5ml7zpgr5neddjxf3esjdfuojq

Obstructive sleep apnea detection using SVM-based classification of ECG signal features

L. Almazaydeh, K. Elleithy, M. Faezipour
2012 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
The classification technique is based on support vector machines (SVM) and has been trained and tested on sleep apnea recordings from subjects with and without OSA.  ...  In this paper, an automated classification algorithm is presented which processes short duration epochs of the electrocardiogram (ECG) data.  ...  We propose a novel methodology in this paper that combines most effective RRinterval based features of the ECG signal based on the ones suggested by Chazal et al., and Yilmaz et al.  ... 
doi:10.1109/embc.2012.6347100 pmid:23367035 dblp:conf/embc/AlmazaydehEF12 fatcat:n5b435gr2zbzhbpqb5urepzz5q

ECG Artefact Detection Using Ensemble Decision Trees

Jonathan Moeyersons, Carolina Varon, Dries Testelmans, Bertien Buyse, Sabine Van Huffel
2017 2017 Computing in Cardiology Conference (CinC)  
This paper describes a novel method for artefact detection in electrocardiogram (ECG) signals. ECG analysis algorithms require a relatively clean dataset.  ...  Due to its effectiveness in skewed datasets, the RUSBoost algorithm is then used for classification. Results show an accuracy of 99.85%, a sensitivity of 100% and a specificity of 95.51%.  ...  Conclusion A novel algorithm to detect artefacts in ECG signals by means of ACF features and the RUSBoost algorithm is presented in this paper. Two advantages can be observed using this methodology.  ... 
doi:10.22489/cinc.2017.240-159 dblp:conf/cinc/MoeyersonsVTBH17 fatcat:dhfa4f3ocfgfxc2jvgtafggnpm

Anticipating Atrial Fibrillation Signal Using Efficient Algorithm

Mohand Lokman Ahmad Al-dabag, Haider Th. Salim ALRikabi, Raid Rafi Omar Al-Nima
2021 International Journal of Online and Biomedical Engineering (iJOE)  
This study proposes a system that distinguishes between the normal and AF ECG signals. First, this work provides a novel algorithm for segmenting the ECG signal for extracting a single heartbeat.  ...  One of the common types of arrhythmia is Atrial Fibrillation (AF), it may cause death to patients.  ...  Segmenting the ECG signal has also effects on classification impacts for SVM and MLP.  ... 
doi:10.3991/ijoe.v17i02.19183 fatcat:ncxtez6kbrchpotrrgrsgy7v4y
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