Mode by Mode Classification of Congestive Heart Failure from Long Term HRV Analysis
In this work a new approach for automatic discrimination of Congestive Heart Failure (CHF) from Normal Sinus Rhythm (NSR) using long-term Heart Rate Variability (HRV) signals is presented. Unlike classical approaches where features are extracted from the input HRV signal, in the present work the hidden information of the signal is retrieved from its different narrow-band components. These components are extracted using the Multivariate Empirical Modes Decomposition (MEMD). Being fully data driven approach, the MEMD is well suited for processing non-stationary signals such as HRV signals. The idea is to identify physically meaningful components that convey pertinent information about the CHF. From these components statistical and geometrical features extracted and combined. We investigate the potential of the higher order statistics (Skewness and Kurtosis) combined with the Hjorth and Poincare plot parameters as features for HRV classification. Different combinations of these features are analyzed, and feature vectors are selected as an input into the binary Support Vector Machines (SVM) classifier. To evaluate the performance of the proposed strategy, experimental tests have been conducted. Results of classification show an accuracy rate around 100%. We expected that the proposed method would be helpful, in clinical diagnosis, to discriminate CHF patients from NSR subjects using long-term HRV recordings.
Copyright © 2016 Praise Worthy Prize - All rights reserved.
M. Nichols, N. Townsend, et al, European Cardiovascular Disease Statistics 2012, (2012) European Heart Network, Brussels, European Society of Cardiology, Sophia Antipolis.
Sumathi, R., Kirubakaran, E., Krishnamoorthi, R., Multi Class Multi Label Based Fuzzy Associative Classifier with Genetic Rule Selection for Coronary Heart Disease Risk Level Prediction, (2014) International Review on Computers and Software (IRECOS), 9 (3), pp. 533-540.
Nalini Muruganantham, D., Periasamy, R., Lloyd and Minkowski Based K-Means Clustering for Effective Diagnosis of Heart Disease and Stroke, (2015) International Review on Computers and Software (IRECOS), 10 (6), pp. 573-579.
Hendradi, R., Arifin, A., Shida, H., Gunawan, S., Purnomo, M., Hasegawa, H., Kanai, H., Signal Processing and Extensive Characterization Method of Heart Sounds Based on Wavelet Analysis, (2016) International Review of Electrical Engineering (IREE), 11 (1), pp. 55-68.
E.D. Übeyli, Combining recurrent neural networks with eigenvector methods for classification of ECG beats, (2009) Digital Sig. Proc., vol. 19, no. 2, pp. 320-329.
J. H. Abawajy, A.V. Kelarev and M. Chowdhury, Multistage approach for clustering and classification of ECG data, (2013) Comput. Methods and Programs Biomed., vol. 112, no. 3, pp. 720-730.
U. Orhan, Real-time CHF detection from ECG signals using a novel discretization method, (2013) Comput. Biol. Med., vol. 43, no. 10, pp. 1556-1562.
M. H. Vafaie, M. Ataei and H. R. Koofigar, Heart diseases prediction based on ECG signals classification using a genetic-fuzzy system and dynamical model of ECG signals, (2014) Biomed. Sig. Proc. Control, vol. 14, pp. 291-296.
K. Y. K. Liao, C. C. Chiu and S. J. Yeh, A novel approach for classification of congestive heart failure using relatively short-term ECG waveforms and SVM classifier, (2015)Proc. Int. MultiConference Eng. Comput. Scientists vol. 1, pp. 1-4.
Z. Masetic and A. Subasi, Congestive heart failure detection using random forest classifier, (2016) Comput. Methods and Programs Biomed., vol. 130, pp. 54-64.
K.S. Phyllis, R.K. Phyllis, W. Michael, J.N. Rottman, et al, Stability of index of heart rate variability in patients with congestive heart failure, (1995) American Heart J., vol. 129, no. 5, pp. 975-981.
P. Ponikowski, S. D. Anker, T. P. Chua, et al, Depressed heart rate variability as an independent predictor of death in chronic congestive heart failure secondary to ischemic or idiopathic dilated cardiomyopathy, (1997) American J. Cardiol., vol. 79, no. 12, pp. 1645-1650.
K.C. Bilchick, B. Fetics, R. Djoukeng, et al, Prognostic value of heart rate variability in chronic congestive heart failure (Veterans Affairs’ Survival Trial of Antiarrhythmic Therapy in Congestive Heart Failure), (2002) American J. Cardiol., vol. 90, no. 1, pp. 24-28.
M.T. La Rovere, G. D. Pinna, R. Maestri, et al, Short-term heart rate variability strongly predicts sudden cardiac death in chronic heart failure patients, (2003) Circulation, vol. 107, no. 4, pp. 565-570.
Y. İşler and M. Kuntalp, Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure,( 2007) Comput. Biol. Med., vol. 37, no. 10, pp. 1502-1510.
G. Liu, L. Wang, Q. Wang, et al, A new approach to detect congestive heart failure using short-term heart rate variability measures, (2014) PloS one, vol. 9, no. 4, pp. 1-8.
K. Abinaya and M. Vijayakumar, Support vector machine technique for risk assessment in patients suffering from congestive heart failure via heart rate variability variant, (2015), Int. J. Eng. Development and Research, vol. 3, no. 2, pp. 925-928.
A. Hossen and B. Al-Ghunaimi, A wavelet-based soft decision technique for screening of patients with congestive heart failure, (2007), Biomed. Sig. Proc. Control, vol. 2, no. 2, pp. 135-143.
A. Hossen and B. Al-Ghunaimi, Identification of patients with congestive heart failure by recognition of sub-bands spectral patterns, (2008), Conf Proc. World Academy Sci., Eng. Technol.. pp. 21-24.
R. A. Thuraisingham, Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals, (2006), Comput. Methods and Programs Biomed., vol. 83, no. 1, pp. 78-82.
S. Kuntamalla and L.G.R. Reddy, Detecting congestive heart failure using heart rate sequential trend analysis plot, (2010) Int. J. Eng. Sci. Technol., vol. 1, no. 2, pp. 7329-7334.
S.N. Yu and M.Y. Lee, Conditional mutual information-based feature selection for congestive heart failure recognition using heart rate variability, (2012) Comput. Methods and Programs Biomed, vol. 108, no. 1, pp. 299-309.
S. Kuntamalla and L.G.R. Reddy, Reduced data dual scale entropy analysis of HRV signals for improved congestive heart failure detection, (2014) Meas. Sci. Rev., vol. 14, no. 5, pp. 294-301.
U. Acharya, R. Kumar, et al, Classification of cardiac abnormalities using heart rate signals, (2004) Med. Biol. Eng. Comput., vol. 42, no. 3, pp. 288-293.
A. L. Goldberger, L.A.N. Amaral, L. Glass, et al. Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals, (2000) Circulation, vol. 101, no. 23, p.p. 215-220.
N.E. Huang, Z. Shen, S.R. Long et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary, (1998), Proc. Royal Soc., A: Mathematical Physical and Engineering Science, vol. 454, no. 1971, pp. 903-995.
A.O. Boudraa and J.C. Cexus, EMD-based signal filtering (2007) IEEE Trans. Instrum. Meas. vol. 56, no. 6, pp. 2196-2202.
C. Santhi, Nonlinear methods and analysis of heart rate variability, PhD Thesis, Dept. Information and Communication Engineering, Anna University, Chennai, 2011.
B. Nassiri, R. Latif, A. Toumanari, S. Elouaham and F. Maoulainine, ECG signal de-noising and compression using discrete wavelet transform and empirical mode decomposition techniques, (2013) Int. J. Numerical and Analytical Methods Eng., vol. 1, no. 5, pp. 245-252.
W. Hu, Z. Zhao, Y. Wang, H. Zhang and F. Lin, Noncontact accurate measurement of cardiopulmonary activity using a compact quadrature doppler radar sensor, (2014) IEEE Trans. Biomed. Eng., vol. 61, no. 3, pp. 725-735.
H. Li, S. Kwong, L. Yang, et al, Hilbert-Huang transform for analysis of heart rate variability in cardiac health, (2011) IEEE Trans. Computational Biol. Bioinformatics, vol. 8, no. 6, pp. 1557-1567.
N. Rehman and D.P. Mandic, Multivariate empirical mode decomposition, (2010) Proc. Royal Soc. London, Mathematical, Physical and Engineering Sciences, vol. 466, no. 2117, pp. 1291-1302.
M. Brennan, M. Palaniswami and P. Kamen, Poincare plot interpretation using a physiological model of HRV based on a network of oscillators, (2002) American J. Physiology-Heart and Circulatory Physiology, vol. 283, no 5, pp.1873-1886.
B. Hjorth, EEG analysis based on time domain properties, (1970) Electroencephalography and Clinical Neurophysiology vol. 29, no. 3, pp. 306-310.
H. Li, X. Feng, L. Cao, et al, A New ECG signal classification based on WPD and ApEn feature extraction, (2016) Circuits, Sys. Sig. Proc., vol. 35, no. 1, pp. 339-352.
S. Dong, B. Boashash, G. Azemi, et al, Automated detection of perinatal hypoxia using time–frequency-based heart rate variability features, (2014) Medical & Biol. Eng. & Computing, vol. 52, no 2, pp. 183-191.
F. A. Elhaj, N. Salim, A. R. Harris, et al, Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals, (2016) Comput. Methods and Programs Biomed., vol. 127, pp. 52-63.
I. Hamed and M.I. Owis, Automatic arrhythmia detection sing support vector machine based on Ddiscrete wavelet transform, (2016) J. Med. Imag. Health Informatics, vol. 6, no 1, pp. 204-209.
Hendel, M., Benyettou, A., Hendel, F., Heartbeats Arrhythmia Classiﬁcation Using Quadratic Loss Multi-Class Support Vector Machines, (2016) International Review on Computers and Software (IRECOS), 11 (1), pp. 49-55.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2019 Praise Worthy Prize