Sensitivity and specificity of magnetocardiography, using computerized classification of current density vectors maps, in ischemic patients with normal ECG and echocardiogram

Fainzilberg, Leonid S.; Chaikovsky, Illya; Auth‐Eisernitz, Sabine; Awolin, B.; Ivaschenko, D.; Hailer, Birgit

doi:10.1016/j.ics.2007.01.042

Cited by 9 publications

(7 citation statements)

References 2 publications

(3 reference statements)

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“…Our results seem to be very promising also in comparison with other MCG-based methods, which reached a sensitivity of 86% and a specificity of 85% in the best cases [14,[16][17][18][19]24,25]. The classification method that we propose has also a better performance than our previous classification system based on the MCG signal features extracted by ICA, for which we obtained a sensitivity of 94% and a specificity of 96% when using MCG data recorded at rest [23].…”

Section: Discussionmentioning

confidence: 52%

“…Another study on 123 patients assessed the discriminative power of CDV maps obtained from MCG recordings in ischemic patients with normal ECG at rest and normal left ventricular function. The MCG based computerized classification achieved a sensitivity of 76% and a specificity of 81% for CDV maps calculated during the ST-T interval [17]. Other MCG based methods were developed to differentiate CAD patients using the current distribution parameters during repolarization (sensitivity of 85.7% and specificity of 74.3%) [18] or the subtracted ST-T waveform (sensitivity of 74.6% and specificity of 84.1%) [19].…”

Section: Introductionmentioning

confidence: 99%

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Early detection of coronary artery disease in patients studied with magnetocardiography: An automatic classification system based on signal entropy

Steinisch

Torke

Haueisen

et al. 2013

Computers in Biology and Medicine

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Section: Discussionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Early detection of coronary artery disease in patients studied with magnetocardiography: An automatic classification system based on signal entropy

Steinisch

Torke

Haueisen

et al. 2013

Computers in Biology and Medicine

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“…Attempts of 3-D modeling and threedimensional imaging of cardiac electrophysiological activity based on current density images were made [9]. Fairly good results have been achieved in classification of current density vector maps, including classification, based on clusters analysis for coronary artery disease and ischemic heart disease detection [10], [11]. Nevertheless, classification of MCG data, especially CDDMs is on its early stage.…”

Section: One Of the Approaches Of Studying Magnetic Field In Humanmentioning

confidence: 99%

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

Udovychenko¹,

Попов²,

Chaikovsky³

2015

International Journal of Electronics and Telecommunications

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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 usingCityblock 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.

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“…3-D modeling and three-dimensional imaging of cardiac electrophysiological activity based on current density images were performed [6]. Rather good results have been achieved in classification of current density vector maps, including classification, based on cluster analysis for coronary artery disease and ischemic heart disease detection [7][8]. Nevertheless, classification of MCG data, especially CDDMs is on its starting point.…”

Section: Introductionmentioning

confidence: 99%

Ischemic heart disease recognition by k-NN classification of current density distribution maps

Udovychenko

Попов

Chaikovsky

2015

2015 IEEE 35th International Conference on Electronics and Nanotechnology (ELNANO)

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Magnetocardiography is non-invasive and riskfree technique allowing body-surface recording of the magnetic fields generated by the electrical activity of the heart. In this paper, k-Nearest Neighbor algorithm is applied for binary classification of myocardium current density distribution maps (CDDM). Different types of CDDMs from patients with ischemic heart disease are compared with normal subjects. Selection of number of neighbors for k-NN classifier was performed to optimize classification characteristics. Specificity, accuracy, precision and sensitivity of classification as functions of number of neighbors in k-NN are obtained. Depending on CDDMs type, accuracy in a range of 60-90%, 32-88% sensitivity, 78-95% specificity and 77-93% precision were achieved. Considering all studied groups of patients with ischemic heart disease, optimal number of neighbors for obtaining highest accuracy using for k-NN classification lies in a range of 20-25 neighbors.

show abstract

Sensitivity and specificity of magnetocardiography, using computerized classification of current density vectors maps, in ischemic patients with normal ECG and echocardiogram

Cited by 9 publications

References 2 publications

Early detection of coronary artery disease in patients studied with magnetocardiography: An automatic classification system based on signal entropy

Early detection of coronary artery disease in patients studied with magnetocardiography: An automatic classification system based on signal entropy

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

Ischemic heart disease recognition by k-NN classification of current density distribution maps

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