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L. Sörnmo, P. Laguna (2005)
Bioelectrical Signal Processing in Cardiac and Neurological Applications
E. Imah, F. Afif, M. Fanany, W. Jatmiko, T. Basaruddin (2011)
A comparative study on Daubechies Wavelet Transformation, Kernel PCA and PCA as feature extractors for arrhythmia detection using SVMTENCON 2011 - 2011 IEEE Region 10 Conference
P. Melillo, R. Castaldo, Giovanna Sannino, Ada Orrico, G. Pietro, L. Pecchia (2015)
Wearable technology and ECG processing for fall risk assessment, prevention and detection2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Taiyong Li, M. Zhou (2016)
ECG Classification Using Wavelet Packet Entropy and Random ForestsEntropy, 18
Mohammad Kachuee, Shayan Fazeli, M. Sarrafzadeh (2018)
ECG Heartbeat Classification: A Deep Transferable Representation2018 IEEE International Conference on Healthcare Informatics (ICHI)
R. Martis, U. Acharya, K. Mandana, A. Ray, C. Chakraborty (2013)
Cardiac decision making using higher order spectraBiomed. Signal Process. Control., 8
Peiguang Jing, Yuting Su, Liqiang Nie, Huimin Gu, J. Liu, M. Wang (2019)
A Framework of Joint Low-Rank and Sparse Regression for Image Memorability PredictionIEEE Transactions on Circuits and Systems for Video Technology, 29
S. Raj, K. Ray (2017)
ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVMIEEE Transactions on Instrumentation and Measurement, 66
F. Atienza, E. Morgado, L. Fernandez-Martinez, A. García-Alberola, J. Rojo-álvarez (2014)
Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector MachinesIEEE Transactions on Biomedical Engineering, 61
R. Mehra (2007)
Global public health problem of sudden cardiac death.Journal of electrocardiology, 40 6 Suppl
D. Samuel, G. Toussaint (1990)
Computing the external geodesic diameter of a simple polygonComputing, 44
A. Goldberger, L. Amaral, L. Glass, Jeffrey Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. Moody, Chung-Kang Peng, H. Stanley (2000)
Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol
R. Sameni, M. Shamsollahi, C. Jutten, G. Clifford (2007)
A Nonlinear Bayesian Filtering Framework for ECG DenoisingIEEE Transactions on Biomedical Engineering, 54
E. Roonizi, R. Sassi (2016)
A Signal Decomposition Model-Based Bayesian Framework for ECG Components SeparationIEEE Transactions on Signal Processing, 64
R. Prati, Gustavo Batista, M. Monard (2008)
A Study with Class Imbalance and Random Sampling for a Decision Tree Learning System
Eduardo Luz, W. Schwartz, Guillermo Chávez, D. Menotti (2016)
ECG-based heartbeat classification for arrhythmia detection: A surveyComputer methods and programs in biomedicine, 127
R. Ceylan, Y. Özbay (2007)
Comparison of FCM, PCA and WT techniques for classification ECG arrhythmias using artificial neural networkExpert Syst. Appl., 33
M. Chawla (2009)
A comparative analysis of principal component and independent component techniques for electrocardiogramsNeural Computing and Applications, 18
(2013)
Analyzing electrocardiogram signals with multiscale short-time fourier transforms
K Muthuvel (2015)
Classification of ECG signal using hybrid feature extraction and neural network classifier. New Delhi, power electronics and renewable energy systems
E. Alickovic, A. Subasi (2016)
Medical Decision Support System for Diagnosis of Heart Arrhythmia using DWT and Random Forests ClassifierJournal of Medical Systems, 40
Can Ye, B. Kumar, M. Coimbra (2012)
Heartbeat Classification Using Morphological and Dynamic Features of ECG SignalsIEEE Transactions on Biomedical Engineering, 59
Chun-Cheng Lin, Chunmin Yang (2014)
Heartbeat Classification Using Normalized RR Intervals and Morphological FeaturesMathematical Problems in Engineering, 2014
M. Kropf, D. Hayn, G. Schreier (2017)
ECG classification based on time and frequency domain features using random forests2017 Computing in Cardiology (CinC)
N. Chawla, K. Bowyer, L. Hall, W. Kegelmeyer (2002)
SMOTE: Synthetic Minority Over-sampling TechniqueArXiv, abs/1106.1813
RC Prati, GEAPA Batista, MC Monard (2008)
A study with class imbalance and random sampling for a decision tree learning system. IFIP International Conference on Artificial Intelligence in Theory and Practice
Jeen-Shing Wang, Wei-Chun Chiang, Yu-Liang Hsu, Y. Yang (2013)
ECG arrhythmia classification using a probabilistic neural network with a feature reduction methodNeurocomputing, 116
Can Ye, M. Coimbra, B. Kumar (2010)
Arrhythmia detection and classification using morphological and dynamic features of ECG signals2010 Annual International Conference of the IEEE Engineering in Medicine and Biology
E. Uslu, G. Bilgin (2012)
Exploiting locality based Fourier transform for ECG signal diagnosis2012 International Conference on Applied Electronics
B. Pourbabaee, M. Roshtkhari, K. Khorasani (2018)
Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation PatientsIEEE Transactions on Systems, Man, and Cybernetics: Systems, 48
P. Chazal (2013)
A Switching Feature Extraction System for ECG Heartbeat Classification
Y. Kutlu, D. Kuntalp (2011)
A multi-stage automatic arrhythmia recognition and classification systemComputers in biology and medicine, 41 1
R. Varatharajan, Gunasekaran Manogaran, M. Priyan (2017)
A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computingMultimedia Tools and Applications, 77
U. Acharya, U. Acharya, U. Acharya, H. Fujita, Shu Oh, Yuki Hagiwara, J. Tan, Muhammad Adam (2017)
Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signalsInf. Sci., 415
Ary Goldberger, L. Amaral, Leon Glass, S. Havlin, J. Hausdorg, P. Ivanov, R. Mark, J. Mietus, George Moody, Chung-Kang Peng, H. Stanley, Physiotool-kit Physiobank (2000)
Physionet: components of a new research resource for complex physiologic signals
Y. Kutlu, D. Kuntalp (2012)
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficientsComputer methods and programs in biomedicine, 105 3
Brij Singh, A. Tiwari (2006)
Optimal selection of wavelet basis function applied to ECG signal denoisingDigit. Signal Process., 16
AL Goldberger (2000)
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signalsCirculation, 101
S. Nurmaini, Radiyati Umi, M. Naufal, Ali Gani (2018)
Cardiac Arrhythmias Classification Using Deep Neural Networks and Principle Component Analysis Algorithm
Shirin Shadmand, B. Mashoufi (2016)
A new personalized ECG signal classification algorithm using Block-based Neural Network and Particle Swarm OptimizationBiomed. Signal Process. Control., 25
Shraddha Singh, S. Pandey, Urja Pawar, R. Janghel (2018)
Classification of ECG Arrhythmia using Recurrent Neural NetworksProcedia Computer Science, 132
Edoardo Pasolli, F. Melgani (2015)
Genetic algorithm-based method for mitigating label noise issue in ECG signal classificationBiomed. Signal Process. Control., 19
Shweta Jain, M. Ahirwal, Anil Kumar, V. Bajaj, G. Singh (2017)
QRS detection using adaptive filters: A comparative study.ISA transactions, 66
S. Raj, Kshitij Maurya, K. Ray (2015)
A knowledge-based real time embedded platform for arrhythmia beat classificationBiomedical Engineering Letters, 5
Jianning Li (2018)
Detection of Premature Ventricular Contractions Using Densely Connected Deep Convolutional Neural Network with Spatial Pyramid Pooling LayerArXiv, abs/1806.04564
Muhammad Zubair, Jinsul Kim, Changwoo Yoon (2016)
An Automated ECG Beat Classification System Using Convolutional Neural Networks2016 6th International Conference on IT Convergence and Security (ICITCS)
U. Acharya, H. Fujita, O. Lih, Yuki Hagiwara, J. Tan, Muhammad Adam (2017)
Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural networkInf. Sci., 405
A. Shinde, P. Kanjalkar (2011)
The comparison of different transform based methods for ECG data compression2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies
E. Pinheiro, O. Postolache, P. Girão (2011)
Method for segmentation of cardiac signals based on four parameter sine fitting2011 IEEE EUROCON - International Conference on Computer as a Tool
Professor Khadra, A. Al-Fahoum, H. Al-Nashash (1997)
Detection of life-threatening cardiac arrhythmias using the wavelet transformationMedical and Biological Engineering and Computing, 35
Shanshan Chen, Wei Hua, Zhi Li, Jian Li, Xingjiao Gao (2017)
Heartbeat classification using projected and dynamic features of ECG signalBiomed. Signal Process. Control., 31
S. Kiranyaz, T. Ince, R. Hamila, M. Gabbouj (2015)
Convolutional Neural Networks for patient-specific ECG classification2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Zhiyong Wu, Xiangqian Ding, Guangrui Zhang (2016)
A Novel Method for Classification of ECG Arrhythmias Using Deep Belief NetworksInt. J. Comput. Intell. Appl., 15
Mohamad Rahhal, Y. Bazi, H. Alhichri, N. Alajlan, F. Melgani, R. Yager (2016)
Deep learning approach for active classification of electrocardiogram signalsInf. Sci., 345
Weiyi Yang, Yujuan Si, Di Wang, B. Guo (2018)
Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machineComputers in biology and medicine, 101
Yi Zheng, Qi Liu, Enhong Chen, Yong Ge, J. Zhao (2014)
Time Series Classification Using Multi-Channels Deep Convolutional Neural Networks
Marwa Hadhoud, M. Eladawy, A. Farag (2006)
Computer Aided Diagnosis of Cardiac Arrhythmias2006 International Conference on Computer Engineering and Systems
P. Chazal, M. O’Dwyer, R. Reilly (2004)
Automatic classification of heartbeats using ECG morphology and heartbeat interval featuresIEEE Transactions on Biomedical Engineering, 51
Zhaohan Xiong, M. Nash, Elizabeth Cheng, V. Fedorov, M. Stiles, Jichao Zhao (2018)
ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural networkPhysiological Measurement, 39
Z Yi (2014)
Time series classification using multi-channels deep convolutional neural networks. International Conference on Web-Age Information Management
U. Acharya, Shu Oh, Yuki Hagiwara, J. Tan, Muhammad Adam, Arkadiusz Gertych, Ruyan Tan (2017)
A deep convolutional neural network model to classify heartbeatsComputers in biology and medicine, 89
Aya Khalaf, M. Owis, I. Yassine (2015)
A novel technique for cardiac arrhythmia classification using spectral correlation and support vector machinesExpert Syst. Appl., 42
A. Gacek, W. Pedrycz (2011)
ECG Signal Processing, Classification and Interpretation: A Comprehensive Framework of Computational Intelligence
Wei Lu, Fugui Fan, Jinghui Chu, Peiguang Jing, Yuting Su (2019)
Wearable Computing for Internet of Things: A Discriminant Approach for Human Activity RecognitionIEEE Internet of Things Journal, 6
A. Krizhevsky, Ilya Sutskever, Geoffrey Hinton (2012)
ImageNet classification with deep convolutional neural networksCommunications of the ACM, 60
K. Muthuvel, L. Suresh, T. Alexander, S. Veni (2015)
Classification of ECG Signal Using Hybrid Feature Extraction and Neural Network Classifier
Timely prediction of cardiovascular diseases with the help of a computer-aided diagnosis system minimizes the mortality rate of cardiac disease patients. Cardiac arrhythmia detection is one of the most challenging tasks, because the variations of electrocardiogram(ECG) signal are very small, which cannot be detected by human eyes. In this study, an 11-layer deep convolutional neural network model is proposed for classification of the MIT-BIH arrhythmia database into five classes according to the ANSI–AAMI standards. In this CNN model, we designed a complete end-to-end structure of the classification method and applied without the denoising process of the database. The major advantage of the new methodology proposed is that the number of classifications will reduce and also the need to detect, and segment the QRS complexes, obviated. This MIT-BIH database has been artificially oversampled to handle the minority classes, class imbalance problem using SMOTE technique. This new CNN model was trained on the augmented ECG database and tested on the real dataset. The experimental results portray that the developed CNN model has better performance in terms of precision, recall, F-score, and overall accuracy as compared to the work mentioned in the literatures. These results also indicate that the best performance accuracy of 98.30% is obtained in the 70:30 train-test data set.
Australasian Physical & Engineering Sciences in Medicine – Springer Journals
Published: Nov 14, 2019
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