Feature Extraction of ECG Signal by using Deep Feature

The analysis and classification of Electrocardiogram (ECG) signals have become very important tool to diagnose of heart disorders. Computer-aided techniques are generally used to classify biomedical application areas. In this paper, we aim to feature extraction and classification of ECG signals. Accordingly, an open access ECG database in Physionet was employed in order to separate normal and abnormal of ECG records. Deep feature approach which is based on Convolutional Neural Network (CNN) was applied to taking out important features of heart recordings. Afterward, Extreme Learning Machine (ELM) was applied to the ECG records. The average precision value metric was used to the performance of the classification performed. In this content, it was noticed classification success values were achieved to accuracy % 88.33, sensitivity %89.47 and specificity % 87.80 with ELM.

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

[2]  Selcan Kaplan Berkaya,et al.  A survey on ECG analysis , 2018, Biomed. Signal Process. Control..

[3]  Sarajane Marques Peres,et al.  Feature selection for biometrie recognition based on electrocardiogram signals , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[4]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[5]  Daeyoung Kim,et al.  ECG arrhythmia classification using a 2-D convolutional neural network , 2018, ArXiv.

[6]  S. P. Kulkarni DWT and ANN Based Heart Arrhythmia Disease Diagnosis from MIT-BIH ECG Signal Data , 2015 .

[7]  Feng Liu,et al.  Deep Learning and Its Applications in Biomedicine , 2018, Genom. Proteom. Bioinform..

[8]  M. Mitra,et al.  Detection of ECG characteristic features using slope thresholding and relative magnitude comparison , 2012, 2012 Third International Conference on Emerging Applications of Information Technology.

[9]  Ping Zhang,et al.  Ant Colony Optimization Based Memetic Algorithm to Solve Bi-Objective Multiple Traveling Salesmen Problem for Multi-Robot Systems , 2018, IEEE Access.

[10]  Da Liu,et al.  Emotional image color transfer via deep learning , 2018, Pattern Recognit. Lett..

[11]  Miguel C. Soriano,et al.  Electrocardiogram Classification Using Reservoir Computing With Logistic Regression , 2015, IEEE Journal of Biomedical and Health Informatics.

[12]  Eric Reiher,et al.  Deep Convolutional Neural Network for ECG-based Human Identification , 2018 .

[13]  Stephen Marshall,et al.  Activation Functions: Comparison of trends in Practice and Research for Deep Learning , 2018, ArXiv.

[14]  David Atienza,et al.  A multi-lead ECG classification based on random projection features , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  Monica Subashini,et al.  Analysis of Electrocardiograph (ECG) Signal for the Detection of Abnormalities Using MATLAB , 2014 .

[16]  Ram Pal Singh,et al.  Application of Extreme Learning Machine Method for Time Series Analysis , 2007 .

[17]  Farid Melgani,et al.  Genetic algorithm-based method for mitigating label noise issue in ECG signal classification , 2015, Biomed. Signal Process. Control..

[18]  Temel Kayikcioglu,et al.  R-peaks detection with convolutional neural network in electrocardiogram signal , 2018, 2018 26th Signal Processing and Communications Applications Conference (SIU).

[19]  Engin Avci,et al.  Determination of R-peaks in ECG signal using Hilbert Transform and Pan-Tompkins Algorithms , 2017, 2017 25th Signal Processing and Communications Applications Conference (SIU).

[20]  Md. Shahjahan,et al.  A new approach to image classification by convolutional neural network , 2017, 2017 3rd International Conference on Electrical Information and Communication Technology (EICT).

[21]  Farid Melgani,et al.  Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization , 2008, IEEE Transactions on Information Technology in Biomedicine.

[22]  Zafer Cömert,et al.  Fetal Hypoxia Detection Based on Deep Convolutional Neural Network with Transfer Learning Approach , 2018, CSOS.

[23]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[24]  Engin Avci,et al.  A new automatic target recognition system based on wavelet extreme learning machine , 2012, Expert Syst. Appl..

[25]  Engin Avci,et al.  A Diagnostic Model for Identification of Myocardial Infarction from Electrocardiography Signals , 2017 .

[26]  Guang-Bin Huang,et al.  Extreme Learning Machine for Multilayer Perceptron , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Chuan Liu,et al.  Improved K-means algorithm based on hybrid fruit fly optimization and differential evolution , 2017, 2017 12th International Conference on Computer Science and Education (ICCSE).

[28]  Mehmet Korürek,et al.  ECG beat classification using particle swarm optimization and radial basis function neural network , 2010, Expert Syst. Appl..