Bispectral analysis and genetic algorithm for congestive heart failure recognition based on heart rate variability

This paper proposes a congestive heart failure (CHF) recognition method that includes features calculated from the bispectrum of heart rate variability (HRV) diagrams and a genetic algorithm (GA) for feature selection. The roles of the bispectrum-related features and the GA feature selector are investigated. Features calculated from the subband regions of the HRV bispectrum are added into a feature set containing only regular time-domain and frequency-domain features. A support vector machine (SVM) is employed as the classifier. A feature selector based on genetic algorithm proceeds to select the most effective features for the classifier. The results confirm the effectiveness of including bispectrum-related features for promoting the discrimination power of the classifier. When compared with the other two methods in the literature, the proposed method (without GA) outperforms both of them with a high accuracy of 96.38%. More than 3.14% surpluses in accuracies are observed. The application of GA as a feature selector further elevates the recognition accuracy from 96.38% to 98.79%. When compared to the Isler and Kuntalp's impressive results recently published in the literature that also uses GA for feature selection, the proposed method (with GA) outperforms them with more than 2.4% surpass in the recognition accuracy. These results confirm the significance of recruiting bispectrum-related features in a CHF classification system. Moreover, the application of GA as feature selector can further improve the performance of the classifier.

[1]  B. Boashash,et al.  Pattern recognition using invariants defined from higher order spectra: 2-D image inputs , 1997, IEEE Trans. Image Process..

[2]  Yalcin Isler,et al.  Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure , 2007, Comput. Biol. Medicine.

[3]  Francisco Sepulveda,et al.  Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface , 2008, Inf. Sci..

[4]  N. Thakor,et al.  Higher-order spectral analysis of burst patterns in EEG , 1999, IEEE Transactions on Biomedical Engineering.

[5]  Paul Terry,et al.  Application of the GA/KNN method to SELDI proteomics data , 2004, Bioinform..

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  Metin Akay,et al.  Biomedical Signal Processing , 2020, Series in BioEngineering.

[8]  L. Fauchier,et al.  Heart rate variability in idiopathic dilated cardiomyopathy: characteristics and prognostic value. , 1997, Journal of the American College of Cardiology.

[9]  Sokol Saliu,et al.  Bisprectral Analysis of Heart Rate Variability , 2002 .

[10]  Vinod Chandran,et al.  Detection of mines in acoustic images using higher order spectral features , 2002 .

[11]  R. A. Thuraisingham,et al.  Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals , 2006, Comput. Methods Programs Biomed..

[12]  Marc A Pfeffer,et al.  Heart failure , 2005, The Lancet.

[13]  Ji-Wu Zhang,et al.  Bispectrum analysis of focal ischemic cerebral EEG signal using third-order recursion method , 2000, IEEE Transactions on Biomedical Engineering.

[14]  Musa H. Asyali,et al.  Discrimination power of long-term heart rate variability measures , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[15]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.

[16]  T. Inouye,et al.  Quantification of EEG irregularity by use of the entropy of the power spectrum. , 1991, Electroencephalography and clinical neurophysiology.

[17]  J. Fleiss,et al.  RR variability in healthy, middle-aged persons compared with patients with chronic coronary heart disease or recent acute myocardial infarction. , 1995, Circulation.

[18]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.

[19]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[20]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[21]  Alain Rakotomamonjy,et al.  Variable Selection Using SVM-based Criteria , 2003, J. Mach. Learn. Res..

[22]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[23]  Tao Li,et al.  A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression , 2004, Bioinform..

[24]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[25]  C. M. Lim,et al.  Cardiac state diagnosis using higher order spectra of heart rate variability , 2008, Journal of medical engineering & technology.

[26]  Sung-Nien Yu,et al.  Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm , 2010, Comput. Biol. Medicine.

[27]  L Fauchier,et al.  [Temporal and spectral analysis of heart rate variability in primary dilate cardiomyopathy: evaluation by case control study]. , 1998, Archives des maladies du coeur et des vaisseaux.

[28]  Yuriy V. Chesnokov,et al.  Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods , 2008, Artif. Intell. Medicine.

[29]  G. Furgi,et al.  Quantification of Poincare' maps for the evaluation of heart rate variability , 1994, Computers in Cardiology 1994.

[30]  Lionel Tarassenko,et al.  Quantifying errors in spectral estimates of HRV due to beat replacement and resampling , 2005, IEEE Transactions on Biomedical Engineering.

[31]  Abdulnasir Hossen,et al.  Identification of Patients with Congestive Heart Failure by Recognition of Sub-Bands Spectral Patterns , 2008 .

[32]  Chrysostomos L. Nikias,et al.  Higher-order spectral analysis , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[33]  Seyed Kamaledin Setarehdan,et al.  Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal , 2008, Artif. Intell. Medicine.

[34]  G. Moody,et al.  Power spectral density of unevenly sampled data by least-square analysis: performance and application to heart rate signals , 1998, IEEE Transactions on Biomedical Engineering.

[35]  Changyong Shin,et al.  Machine fault detection using bicoherence spectra , 2004, Proceedings of the 21st IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.04CH37510).

[36]  Amjed S. Al-Fahoum,et al.  A quantitative analysis approach for cardiac arrhythmia classification using higher order spectral techniques , 2005, IEEE Transactions on Biomedical Engineering.

[37]  H. Nagaraja,et al.  Heart rate variability: origins, methods, and interpretive caveats. , 1997, Psychophysiology.