Diagnosis of congestive heart failure using poincare map plot

In this study, in order to diagnose congestive heart failure patients (CHF), Poincare map obtained from raw ECG data is used. CHF and normal ECG data, which are distributed freely via internet, are analyzed. Poincare map is divided into equal rectangle cells and points in all of the cells are determined. These values are used for knn (k-nearest neighbour) classification. At the result of this study, it is considered that CHF patients and normal people can be separated each other using features obtained from Poincare map of Raw ECG record.

[1]  N. Kannathal,et al.  Cardiac State Diagnosis using Adaptive Neuro-Fuzzy Technique , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[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]  C. M. Lim,et al.  Cardiac state diagnosis using adaptive neuro-fuzzy technique. , 2006, Medical engineering & physics.

[4]  Yakup Kutlu,et al.  Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes , 2009, Expert Syst. Appl..

[5]  Chandrakar Kamath,et al.  A new approach to detect congestive heart failure using Teager energy nonlinear scatter plot of R-R interval series. , 2012, Medical engineering & physics.

[6]  Mehmet Engin.  Spectral and wavelet based assessment of congestive heart failure patients , 2007, Comput. Biol. Medicine.

[7]  S. Parvaneh,et al.  Application of novel mapping for heart rate phase space and its role in cardiac arrhythmia diagnosis , 2010, 2010 Computing in Cardiology.

[8]  V. C. Veera Reddy,et al.  CARDIAC ARRHYTHMIA CLASSIFICATION USING FUZZY CLASSIFIERS , 2008 .

[9]  Elif Derya Übeyli,et al.  Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents , 2009, Comput. Methods Programs Biomed..

[10]  Elif Derya Übeyli.  Adaptive neuro-fuzzy inference system for classification of ECG signals using Lyapunov exponents , 2009, Comput. Methods Programs Biomed..

[11]  A M Nasrabadi,et al.  Utilizing occurrence sequence of Heart Rate's phase space points in order to discriminate heart Arrhythmia , 2010, 2010 17th Iranian Conference of Biomedical Engineering (ICBME).

[12]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[13]  KutluYakup,et al.  Optimizing the performance of an MLP classifier for the automatic detection of epileptic spikes , 2009 .

[14]  Elif Derya Übeyli,et al.  A modified mixture of experts network structure for ECG beats classification with diverse features , 2005, Eng. Appl. Artif. Intell..