Arrhythmia classification using higher order statistics

In this work, the features are extracted for the arrhythmia classification from the electrocardiograph (ECG) signals by using Higher order statistics. K-nearest neighborhood algorithm is used as classifier. Cumulants are calculated from the raw signals obtained from consecutive sample values of each R peak in ECG signals and used as features. In addition to these features, different features obtained from the relations of cumulants are also used. Simulation results shows that features obtained from the relations among cumulants are more discriminative than the cumulants.

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

[2]  H. Nakajima,et al.  Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network , 1999, IEEE Transactions on Biomedical Engineering.

[3]  W.J. Tompkins,et al.  A patient-adaptable ECG beat classifier using a mixture of experts approach , 1997, IEEE Transactions on Biomedical Engineering.

[4]  Russell C. Eberhart,et al.  Neural network PC tools , 1990 .

[5]  S. Yoo,et al.  Support Vector Machine Based Arrhythmia Classification Using Reduced Features , 2005 .

[6]  K. Minami,et al.  Arrhythmia diagnosis with discrimination of rhythm origin and measurement of heart-rate variation , 1997, Computers in Cardiology 1997.

[7]  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.

[8]  S. Osowski,et al.  Support Vector Machine based expert system for reliable heart beat recognition , 2022 .

[9]  C. L. Nikias,et al.  Higher-order spectra analysis : a nonlinear signal processing framework , 1993 .

[10]  J. Nadal,et al.  Classification of cardiac arrhythmias based on principal component analysis and feedforward neural networks , 1993, Proceedings of Computers in Cardiology Conference.

[11]  Nurettin Acir.  Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm , 2005, Neural Computing & Applications.

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

[13]  L. Sornmo,et al.  Self-organizing maps and Hermite functions for classification of ECG complexes , 1997, Computers in Cardiology 1997.

[14]  Stanislaw Osowski,et al.  Support vector machine-based expert system for reliable heartbeat recognition , 2004, IEEE Transactions on Biomedical Engineering.

[15]  H. Gholam Hosseini,et al.  A multi-stage neural network classifier for ECG events , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.