Feature Extraction of ECG Signal Using HHT Algorithm

This paper describe the features extraction algorithm for electrocardiogram (ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal for an individual human being is different due to unique heart structure. The purpose of feature extraction of ECG signal would allow successful abnormality detection and efficient prognosis due to heart disorder. Some major important features will be extracted from ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. Therefore, we need a strong mathematical model to extract such useful parameter. Here an adaptive mathematical analysis model is Hilbert-Huang transform (HHT). This new approach, the Hilbert-Huang transform, is implemented to analyze the non-linear and nonstationary data. It is unique and different from the existing methods of data analysis and does not require an a priori functional basis. The effectiveness of the proposed scheme is verified through the simulation.

[1]  Binwei Weng,et al.  ECG Denoising Based on the Empirical Mode Decomposition , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Sanjay L. Nalbalwar,et al.  MODULAR NEURAL NETWORK BASED ARRHYTHMIA CLASSIFICATION SYSTEM USING ECG SIGNAL DATA , 2011 .

[3]  P. Langley,et al.  Body surface potential mapping for detection of myocardial infarct sites , 2007, 2007 Computers in Cardiology.

[4]  A. B. Ramli,et al.  Correlation analysis for abnormal ECG signal features extraction , 2003, 4th National Conference of Telecommunication Technology, 2003. NCTT 2003 Proceedings..

[5]  M. Arthanari,et al.  Classification of ECG Signals Using Extreme Learning Machine , 2011, Comput. Inf. Sci..

[6]  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).

[7]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[8]  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.

[9]  S. C. Saxena,et al.  Data Compression and Feature Extraction of Ecg Signals , 1997, Int. J. Syst. Sci..

[10]  A. Geva,et al.  ECG feature extraction using optimal mother wavelet , 2000, 21st IEEE Convention of the Electrical and Electronic Engineers in Israel. Proceedings (Cat. No.00EX377).

[11]  Stanislaw Osowski,et al.  ECG beat recognition using fuzzy hybrid neural network , 2001, IEEE Trans. Biomed. Eng..

[12]  A. Jovic,et al.  Feature Extraction for ECG Time-Series Mining Based on Chaos Theory , 2007, International Conference on Information Technology Interfaces.

[13]  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.

[14]  M. Fereniec,et al.  Wavelet Denoising for Multi-lead High Resolution ECG Signals , 2007 .

[15]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[16]  S. Yaacob,et al.  Emotion recognition from electrocardiogram signals using Hilbert Huang Transform , 2012, 2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT).

[17]  R. Saatchi,et al.  Feature extraction and classification of electrocardiogram (ECG) signals related to hypoglycaemia , 2003, Computers in Cardiology, 2003.

[18]  Dimitrios Hatzinakos,et al.  A new ECG feature extractor for biometric recognition , 2009, 2009 16th International Conference on Digital Signal Processing.

[19]  Pedro R. Gomes,et al.  ECG Data-Acquisition and classification system by using wavelet-domain Hidden Markov Models , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.