An efficient abnormal beat detection scheme from ECG signals using neural network and ensemble classifiers

This paper presents an investigation into the development of an efficient scheme to detect abnormal beat from lead II Electro Cardio Gram (ECG) signals. Firstly, a fast ECG feature extraction algorithm was proposed which could extract the locations, amplitudes waves and interval from lead II ECG signal. We then created 11 customized features based on the outputs of the feature extraction algorithm. Then, we used these 11 features to train an artificial neural network and an ensemble classifier respectively for detecting the abnormal ECG beats. Three manually annotated databases were used for training and testing our system: MIT-BIH Arrhythmia, QT and European ST-T database availed from Physionet databank. The results showed that for an abnormal beat detection, the neural network classifier had an overall accuracy of 98.73% and the ensemble classifier with AdaBoost had 99.40%. Using time domain processing approach, the proposed scheme reduced overall computational complexity as compared to the existing methods with an aim to deploy on the mobile devices in the future to promote early and instant abnormal ECG beat detection.

[1]  R. Kumar,et al.  Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network , 2011 .

[2]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[3]  Elif Derya Ubeyli,et al.  Feature extraction for analysis of ECG signals , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Wei Qin,et al.  An Adaptive Morphologic Filter Applied to ECG De-noising and Extraction of R Peak at Real-time , 2012 .

[5]  M.B. Tayel,et al.  ECG images classification using artificial neural network based on several feature extraction methods , 2008, 2008 International Conference on Computer Engineering & Systems.

[6]  G. Moody,et al.  The European ST-T database: standard for evaluating systems for the analysis of ST-T changes in ambulatory electrocardiography. , 1992, European heart journal.

[7]  Cédric Join,et al.  An algebraic derivative-based method for R wave detection , 2011, 2011 19th European Signal Processing Conference.

[8]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[9]  Yean-Ren Hwang,et al.  ECG Feature Extraction and Classification Using Cepstrum and Neural Networks , 2008 .

[10]  Pawel Tadejko,et al.  Mathematical Morphology Based ECG Feature Extraction for the Purpose of Heartbeat Classification , 2007, 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07).

[11]  Li Zhang,et al.  An intelligent mobile based decision support system for retinal disease diagnosis , 2014, Decis. Support Syst..

[12]  Gaurav Kumar Jaiswal,et al.  ECG Classification with the Help of Neural Network , 2014 .

[13]  K. O. Gupta,et al.  ECG Signal Analysis and Classification using Data Mining and Artificial Neural Networks , 2012 .

[14]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[15]  S. S. Mehta,et al.  Detection of QRS Complexes in 12-lead ECG using Adaptive Quantized Threshold , 2008 .

[16]  Celia Shahnaz,et al.  Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains , 2012, Biomed. Signal Process. Control..

[17]  Elif Derya Übeyli.  Implementing wavelet transform/mixture of experts network for analysis of electrocardiogram beats , 2008, Expert Syst. J. Knowl. Eng..

[18]  G. Boudreaux-Bartels,et al.  Wavelet transform-based QRS complex detector , 1999, IEEE Transactions on Biomedical Engineering.

[19]  Mohammad Mikaili,et al.  Fetal ECG Extraction Using Multi-Layer Perceptron Neural Networks with Bayesian Approach , 2009 .

[20]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

[21]  Li Zhang.  An intelligent mobile based decision support system for retinal disease diagnosis , 2014 .

[22]  Pablo Laguna,et al.  A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG , 1997, Computers in Cardiology 1997.

[23]  Wen-June Wang,et al.  QRS complexes detection for ECG signal: The Difference Operation Method , 2008, Comput. Methods Programs Biomed..

[24]  Shoushui Wei,et al.  ECG quality assessment based on a kernel support vector machine and genetic algorithm with a feature matrix , 2014, Journal of Zhejiang University SCIENCE C.

[25]  Fei Zhang,et al.  Electrocardiogram QRS Detection Using Multiscale Filtering Based on Mathematical Morphology , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  WangWen-June,et al.  QRS complexes detection for ECG signal , 2008 .

[27]  Fei Wang,et al.  Shape-based Matching of ECG Recordings , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Ola Pettersson,et al.  ECG analysis: a new approach in human identification , 2001, IEEE Trans. Instrum. Meas..

[29]  Yüksel Özbay,et al.  A novel approach for classification of ECG arrhythmias: Type-2 fuzzy clustering neural network , 2009, Expert Syst. Appl..

[30]  P. C. Cortez,et al.  An innovative approach of QRS segmentation based on first-derivative, Hilbert and Wavelet Transforms. , 2012, Medical engineering & physics.

[31]  Ali Ghaffari,et al.  A new mathematical based QRS detector using continuous wavelet transform , 2008, Comput. Electr. Eng..

[32]  Ibrahim Khalil,et al.  ECG biometric using multilayer perceptron and radial basis function neural networks , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[34]  Haiying Zhou,et al.  Real-Time Automatic ECG Diagnosis Method Dedicated to Pervasive Cardiac Care , 2009, Wirel. Sens. Netw..

[35]  Yüksel Özbay,et al.  A new method for classification of ECG arrhythmias using neural network with adaptive activation function , 2010, Digit. Signal Process..

[36]  Majid Moavenian,et al.  A qualitative comparison of Artificial Neural Networks and Support Vector Machines in ECG arrhythmias classification , 2010, Expert Syst. Appl..

[37]  Priyanka Mehta,et al.  Qrs Complex Detection of Ecg Signal Using Wavelet Transform , 2012 .

[38]  P Jafari Moghadam Fard,et al.  A novel approach in R peak detection using Hybrid Complex Wavelet (HCW). , 2008, International journal of cardiology.

[39]  Lyudmila Mihaylova,et al.  QRS complex detection using centered Cumulative Sums of Squares , 2013, 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).