Binary Classification of Heart Failures Using k-NN with Various Distance Metrics

• Autorzy: Udovychenko Y. , Popov A. , Chaikovsky I.

Identyfikatory

DOI

10.2478/eletel-2015-0044

• Konferencja: Signal Processing Symposium (10-12.06. 2015 ; Debe, Poland)
• Języki publikacji: EN

Abstrakty

EN

Magnetocardiography is a sensitive technique of measuring low magnetic fields generated by heart functioning, which is used for diagnostics of large number of cardiovascular diseases. In this paper, k-nearest neighbor (k-NN) technique is used for binary classification of myocardium current density distribution maps (CDDM) from patients with negative T-peak, male and female patients with microvessels (diffuse) abnormalities and sportsmen, which are compared with normal control subjects. Number of neighbors for k-NN classifier was selected to obtain highest classification characteristics. Specificity, accuracy, precision and sensitivity of classification as functions of number of neighbors in k-NN are obtained for classification with several distance measures: Mahalanobis, Cityblock, Eucleadian and Chebyshev. Increase of the accuracy of classification for all groups up to 10% was obtained using Cityblock distance metric in binary k-NN classifier with 19 - 27 neighbors, comparing to other metrics. Obtained results are acceptable for further patient’s state evaluation.

Słowa kluczowe

EN

magnetocardiography; current density imaging; current density distribution map; k-NN classification; negative Tpeak; heart failure diagnostics; Mahalanobis distance; cityblock distance; Eucleadian distance; Chebyshev distance

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2015
• Tom: Vol. 61, No. 4
• Strony: 339--344
• Opis fizyczny: Bibliogr. 14 poz., il., wykr.

Twórcy

autor

Udovychenko Y.

• Physical and Biomedical Electronics Departmentof National Technical University of Ukraine “Kyiv Polytechnic Institute”, Kyiv, Ukraine

autor

Popov A.

• Physical and Biomedical Electronics Departmentof National Technical University of Ukraine “Kyiv Polytechnic Institute”, Kyiv, Ukraine

autor

Chaikovsky I.

• Glushkov Institute of Cybernetics of NAS of Ukraine, Kyiv, Ukraine

Bibliografia

• [1] Hyun Kyoon Lim et al., “Magnetocardiogram difference detween healthy subjects and ischemic heart disease patients” in IEEE Transactions on Magnetics, Vol. 45, June 2009, pp. 2890-2893.
• [2] Tsukada K. et al., “Magnetocardiographic mapping characteristic for diagnosis of ischemic heart disease” in Computers in Cardiology 2000, Cambridge, MA, Sept. 2000, pp. 505-508.
• [3] Chaikovsky I. et al., “Magnetocardiography in clinical practice: algorithms and technologies for data analysis” in Medical Science 3-4, June 2011, pp. 21-38.
• [4] Jazbinsek V. et al., “Magnetocardiographic localization of accessory conduction pathway in patients suffering from WPW syndrome”, Computers in Cardiology 1995, Vienna, Austria, Sept. 1995, pp. 417-420.
• [5] Romanovych S. et al., “Imaging of heart biomagnetic sources by current lines in a plane using the magnetic moments method” in Proceedings of the EMBEC99, Part 1, 1991, pp. 410-411.
• [6] Ogata K. et al., “Projecting cardiac-current images onto a 3-D standard heart model” in Engineering in Medicine and Biology Society, 2003, Vol. 1, Sept. 2003, pp. 517-520.
• [7] Kobayashi K et al., “Visualization of the Current-Density Distributionfor MCG With WPW Syndrome Patients Using Independent Component Analysis in IEEE Transactions on Magnetics, Vol. 40, July 2004, pp. 2970-2972.
• [8] Brockmeier K. et al., “Magnetocardiography and 32-Lead Potential Mapping: Repolarization in Normal Subjesct During Pharmacologically Induced Stress”, Journal of Cardiovascular Electrophysiology, Vol. 8' No. 6, June. 1999, pp. 615-626.
• [9] Leder U. et al., “Noninvasive Biomagnetic Imaging in Coronary Artery Disease Based on Individual Current Density Maps of the Heart”, International Journal of Cardiology 64 (1998), pp. 417-420.
• [10] Fainzilberg L. et al., “Sensitivity and specificity of magnetocardiography, using computerized classification of current density vectors maps, in ischemic patients with normal ECG and echocardiogram”, International Congress Series 1300 (2007), pp. 468-471.
• [11] Chaikovsky I. et al., “Detection of coronary artery disease in patients with normal or unspecifically changed ECG on the basis of magnetocardiography”, Proceedings of the 12-th International Conference on Biomagnetism (2000), pp. 565-568.
• [12] Udovychenko Y., et al., “Current Density Distribution Maps Threshold Processing” in 2014 IEEE 34th International Scientific Conference on Electronics and Nanotechnology (ELNANO), Apr. 2014, pp. 313-315.
• [13] Kilian Q. et al., “Distance Metric Learning for Large Margin Nearest Neighbor Classification”, Journal of Machine Learning Research 10 (2009), Sept. 2009, pp. 207-244.
• [14] Udovychenko Y., et al., “k-NN Binary Classification of Heart Failures Using Myocardial Current Density Distribution Maps”, Signal Processing Symposium (SPSympo), June 2015, pp. 98-102.

Uwagi

EN

This paper is an extended version of paper “k-NN Binary Classification of Heart Failures Using Myocardial Current Density Distribution Maps” [14], presented in Signal Processing Symposium, Debe, Poland, June 10-12, 2015.