2014 IEEE 34th International Scientific Conference on ELECTRONICS AND NANOTECHNOLOGY

(ELNANO)

National Technical University of Ukraine "Kyiv Polytechnic Institute"

CONFERENCE PROCEEDINGS

April 15-18, 2014 Kyiv, Ukraine

2014 IEEE 34th International Scientific Conference on Electronics and Nanotechnology (ELNANO)

National Technical University of Ukraine "Kyiv Polytechnic Institute"

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IEEE Catalog Number: CFP14O5U-CDR ISBN: 978-1-4799-4582-5

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Faculty of Electronics, National Technical University of Ukraine “KPI” Polytekhnichna Str., 16, Block 12, off. 405, 03056, Kyiv, Ukraine

2014 IEEE XXXIV International Scientific Conference Electronics and Nanotechnology (ELNANO)

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Current Density Distribution Maps Threshold Processing

Yevhenii Udovychenko, Anton Popov Physical and Biomedical Electronics Department

National Technical University of Ukraine “Kyiv Polytechnic Institute”

Kyiv, Ukraine

Illya Chaikovsky Glushkov Institute of Cybernetics of NAS of Ukraine

Kyiv, Ukraine illya.chaikovsky@gmail.com

Abstract — Magnetocardiography is one of the most

progressive tools for diagnosis and investigation of heart diseases, such as coronary heart disease. Noninvasiveness, contactlessness, high sensitivity makes magnetocardiography suitable for this purpose and comfortable in use for physician. In this paper analysis of myocardium current density distribution maps is proposed. Current density distribution map surface area dependence on time and intensity threshold is obtained.

Keywords — magnetocardiography; current density imaging; threshold processing; current density distribution map

I. INTRODUCTION

Magnetocardiography (MCG) is a technique of measuring the weak magnetic fields generated from the heart during it’s functioning. MCG can be measured using a superconducting quantum interference device (SQUID) sensor which converts magnetic flux to voltage, and is the most sensitive sensor to detect magnetism [1]. Magnetic measurements are not only non-invasive, but also contactless. Thereby the occurrence of artifacts due to insufficient reliability of contacts between electrodes and skin is prevented. MCG is more sensitive to intra- and extracellular activation currents then electrocardiography (ECG) [2]. Effects of magnetic susceptibility changes in the torso associated with blood movement may contribute to variations in the external field. Since 1963 when recording of the human magnetocardiogram (MCG) was first reported, the number of clinical studies has been limited, but nowadays the high-quality tracings can now be easily obtained in special shielded chambers or in regular clinic rooms due to availability of elaborated sensitive SQUID devices [3].

MCG analysis is used for diagnosis of ischemic heart disease by comparing magnetic field maps for both normal subjects and patients with ischemic heart disease [4]. Cardiac-current images that include morphological information on the heart may be obtained by projecting a two-dimensional (2-D) current-arrow map (CAM), which is calculated from magnetocardiogram signals, onto a three-dimensional (3-D) standard heart model, which is intended to be applicable to all adult subjects [5]. The noninvasive magnetocardiographic mapping data is used for diagnostics and analysis of Wolf-Parkinson-White syndrome [6] and other diseases accompanied by lesions of current flow in the heart muscles.

One of the approaches of studying magnetic field in human heart is analysis of current density distribution maps (CDDM). Current density imaging is a noninvasively measurement of the electrical current density inside a conductive sample. Current density imaging measures the local magnetic field vector generated by the current flowing inside the tissue and uses a vector curl operation to calculate the current density. Since current density imaging can be performed contactless using SQUID, it is inherently noninvasive (when compared with multiple-electrode measurements) and does not suffer from the limitations of aforementioned techniques. Therefore, it is possible to accurately measure the detailed current density information inside the body using current density imaging followed by the solution of inverse problem. Furthermore, current flow information can be calculated from the current density data using streamline analysis [7].

Aim of the study is analysis of CDDM surface area dependence on time and current density threshold for further determination of diagnostically useful characteristics.

II. ANALYSIS OF CDDM SURFACE AREA DEPENDENCE ON

TIME AND CURRENT DENSITY THRESHOLD

For spatial data capture at MCG studying, the observation points - the intersection nodes of a square grid, which has a binding to anatomical landmarks on the thorax, are used. As the number of nodes is limited in order to localize areas of pathological activity of the myocardium and to build maps of instant distribution of magnetic field induction in heart, a smooth filling and interpolation of the function of two variables in the points beyond the bounds of standard grid are usually performed. In other words, based on synchronous averaged MCG curves using two-dimensional interpolation algorithms instant the magnetic field distribution maps are built. Then, using an algorithm for solving the "inverse problem", the distribution map of the magnetic field can be converted into instant CDDMs. Thus each CDDM in this study is formed by using and transformation of current density vectors, obtained using MCG. Each CDDM is a grayscale discrete image with size of M×N pixels, where white color corresponds the highest brightness and black responds the least brightness (Figure 1). Thus brightness of an image corresponds to the current density value in corresponding point. CDDMs are built for definite moments of time with some step (up to 10 ms) during T wave of electrocardiogram QT interval (Figure 2). It gives opportunity to associate ECG data with corresponding dependences, calculated from CDDMs.

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2014 IEEE XXXIV International Scientific Conference Electronics and Nanotechnology (ELNANO)

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Fig. 1. Current density distribution map

Fig. 2. Electrocardiogram with denoted T wave of QT interval

In this study analysis of CDDM surface area dependence on time and current density threshold was made. The area of CDDM may be found as

S∑xy

where Nx and Ny are numbers of rows and columns of matrix, that forms CDDM. This matrix is obtained from transforming values of current density vector, which comes from MCG.

After that the threshold λ (%) for current density, which is represented by brightness on CDDM, is specified. Then the threshold current density is calculated

iλtimaxtλ

where t – time instant, imax(t) – the highest value of current density at defined moment. It should be noted, that threshold current density isn’t the same for all CDDMs, but is calculated separately for each map.

Then threshold processing of a card I (t, nx, ny) is carried out

(3)

where I (t, nx, ny) – current density in defined point of CDDM at defined moment.

After that CDDM discrete values which have current density higher than threshold current density are counted. Number of such pixels is Nλ(t). Then effective surface area can be found by the formula

SλtλtS∑

Total current density is calculated for each map for each time instant

(5)

This value is especially significant for associating with ECG data.

III. EXPERIMENTAL RESULTS

In this study 24 CDDMs were analyzed. Each map corresponds to defined moment of time of T wave of QT interval. Thresholded CDDMs , ,x yI t n n

11were built for

several threshold values. Examples of the obtained maps are given Figures 3-5.

Fig. 3. Threshold processed current density distribution map, threshold = 0.1

Fig. 4. Threshold processed current density distribution map, threshold = 0.5

Fig. 5. Threshold processed current density distribution map, threshold = 0.9

2014 IEEE XXXIV International Scientific Conference Electronics and Nanotechnology (ELNANO)

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For threshold values λ = 0.1…0.9 with step 0.1, using value of Sλ(t), the effective surface area dependence on time was built (Figures 6-8).

Fig. 6. Effective surface area dependence on time, threshold = 0.1

Fig. 7. Effective surface area dependence on time, threshold = 0.5

Fig. 8. Effective surface area dependence on time, threshold = 0.9

Total current density dependence on time I t was also

calculated (Figure 9).

Fig. 9. Total current density dependence on time

From the preliminary results obtained during this work it can be seen that there is substantial difference in the amount of current flow from parts of total map surface.

For larger threshold, there is more or less invariable ratio of surface area submitting large current during the T-peak of ECG, see e.g. Fig. 5, in which during whole time period the ratio of surface with current density over 10% of maximum lies between 95 and 99%. With the threshold decrease, there are different ratios supplying current in myocardium during different time periods of T-peak. In Fig. 6 during the first half of T-peak approximately 40-45% of total area has current density over 50% of maximum, while at the end of T-peak there is a decrease of this ratio to 22%.

IV. CONCLUSION

The technique of studying of CDDM surface area dependence on time and current density threshold is proposed and tested for 24 current density distribution maps, showing the change in the area having certain current density values with respect to the current density threshold. The proposed approach can be used for studying the current density distribution as a function of time, and can give potentially useful information about the spatial distribution of current in myocardium in different time periods of heart cycle.

REFERENCES [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] Chaikovsky I. et al., “Magnetocardiography in clinical practice: algorithms and technologies for data analysis” in Medical Science 3-4, June 2011, pp. 21-38.

[3] Geselowitz, David B., “Magnetocardiography: an overview” in IEEE Transaction on Biomedical Engineering, Vol. BME-26, Sept. 979, pp. 497-504.

[4] 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.

[5] 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.

[6] 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.

[7] Richard S. et al., “Measurement of thoracic current flow in pigs for the study of defibrillation and cardioversion,” IEEE Transactions on Biomedical Engineering, Vol. 50, Oct. 2003 pp. 1167-1173.