An Analysis of Various Electrical Activity in Heart Cavities for

Ischemia-Related Issue

JAVALKAR VINAY KUMAR1,2,a,*, SHYLASHREE NAGARAJA1,b,

YATISH DEVANAND VAHVALE3, SRIDHAR VENUGOPALACHAR4,c

1Department of Electronics and Communication Engineering,

R. V. College of Engineering, Bengaluru,

Affiliated to Visvesvaraya Technological University,

Belagavi-590018, Karnataka,

INDIA

2Department of Electronics and Communication Engineering,

Ballari Institute of Technology and Management, Ballari,

Affiliated to Visvesvaraya Technological University,

Belagavi-590018, Karnataka,

INDIA

3Design Verification Engineer,

Intel Technology India Private Limited,

Bengaluru, Karnataka,

INDIA

4Department of Electronics and Communication Engineering,

Nitte Meenakshi Institute of Technology,

Bengaluru, Affiliated to Visvesvaraya Technological University, Belagavi-590018, Karnataka,

INDIA

aORCiD: https://orcid.org/0000-0002-5943-6947

bORCiD: https://orcid.org/0000-0003-4185-6190

cORCiD: https://orcid.org/0000-0003-4715-6996

*Corresponding Author

Abstract: - The heart is the hub of the circulatory system, a system of blood veins that distributes blood

throughout the body. When arterial blood flow to a tissue, organ, or extremity is interrupted, it is known as

ischemia. If left untreated, ischemia can cause tissue death. Since the heart's structure may be represented and

simulated for cardiac contraction and relaxation, it is significant in COMSOL Multiphysics. The Fitzhugh-

Nagumo (FN) and Ginzburg-Landau (GL) equations are used to implement the electrical activity in presumably

different cardiac cavities with the ultimate goal of addressing ischemia-related problems. The heart model is

divided into four distinct models to illustrate blood flow. Both the observed plots and the dependent variables'

waves have a spiral shape.

Key-Words: - COMSOL Multiphysics, Ischemia, Landau Ginzberg equation, Fitzhugh Nagumo equation, Heart

Model, Heart cavity variations, Simulation.

Received: March 13, 2023. Revised: November 11, 2023. Accepted: December 14, 2023. Published: January 11, 2024.

1 Introduction

Modeling electrical activity in cardiac tissue is

required to understand the pattern of heart dilatation

and contraction. The sinus node is a cardiac area

responsible for the generation of rhythmic electrical

pulses. In turn, the electrical pulses cause the muscle

to physically contract. In a healthy heart, electrical

pulses are muted, but several cardiac disorders

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E-ISSN: 2224-2902

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increase the chance of signals re-entering. This

indicates that the normally steady pulse has been

disrupted, a dangerous and urgent condition known

as arrhythmia. This work employs complex

Ginzburg-Landau equations and Fitzhugh-Nagumo

equations, both solved on the same geometry, to

investigate various propagation aspects of electrical

impulses in heart tissue. Several clinical heart and

circulatory illness conditions, such as stroke,

inherent coronary illness, musicality issues,

subclinical atherosclerosis, coronary illness,

cardiovascular breakdown, valvular infection,

venous illness, and fringe vein illness, are updated

in the study that was published in the Statistical

Update. It also includes information on the

associated outcomes, such as the type of care,

procedures, and financial costs, [1]. In the course of

their investigation, the authors. developed a model

an electrical activity for ischemia, [2]. The

mathematical reconstruction of electrocardiograms

(ECGs) was discussed in their study, [3]. The

objective on the research done, is to create a

numerical model for differential conditions that may

generate acceptable 12-lead ECGs, [4]. The study

that the cardiovascular framework deals with several

miracles related to different temporal, constitutive,

and mathematical scales, [5]. The backward

problem of electrocardiography (IPE), which has

been described in several methods to get significant

data about the heart condition in an inconspicuous

manner, [6]. Advanced arithmetic was used to assess

the model conditions, taking into account significant

topological characteristics of the two atria. A closed-

circle model of cardiovascular incitement using a

constrained component model of the whole heart,

which is thought to be a useful tool for pacemaker

planning and testing, [7].

The systems with hidden normal and atypical

atrial rhythms could be the result of re-contestant

enactment by an ectopic concentration, [8]. A

personal computer was utilized in their investigation

to illustrate the elements in figure-of-eight re-

emergence during the severe stage of local

myocardial ischemia, [9]. A well-chosen projection

from the 4-dimensional HH stage space onto a plane

yields a graph that is comparable to that which

illustrates the fundamental connection between the

two models, [10]. The forward problem in

electrocardiography is related to estimating the

potential effects of heat sources on the body's

surface using fictitious electromagnetic conditions,

[11] and [12].

The intricate structure of cardiac muscle, which

consists of interconnected cells surrounded by an

interstitial made of fluids, connective tissue, and

veins, presents some obvious difficulties for

anybody trying to comprehend the tissue as an

electrical medium, [13]. The spatial distribution of

electrical potential is processed using the proposed

bidomain model, [14]. To discretize incomplete

differential circumstances, one uses the limited

component approach; to compare direct situations,

one uses the multigrid methodology, [15]. Applying

research and innovation to naturally occurring cells

and tissues that are electrically guided and edgy in

their duty, [16]. The concept of wide variety

applications in both electric and attractive fields is

discussed in their study, [17].

An extraordinary computerized bandpass

channel decreases incorrect identifications caused

by the many forms of impedance present in ECG

signals, [18]. The lead field theory provides a

comprehensive illustration of the distinctions and

similarities between bioelectricity and

biomagnetism, [19]. A review of the literature

shows that more studies have been conducted

utilizing a few methods to simulate electrical

activity in cardiac tissue. Nonetheless, there is still a

knowledge gap regarding the circulation of blood in

the heart, [20]. To tackle ischemia-related problems,

the authors thus provide an analysis of electrical

activity in different cardiac cavities using the

Fitzhugh-Nagumo (FN) and Ginzburg-Landau (GL)

equations, [21]. The authors solve for the FN/GL

mathematical models using the commercial software

program COMSOL (R), then compare their

findings.

2 Methodology

The COMSOL tool is used to track the heart's

electrical activity. The behavior of the heart in a

variety of situations, including stress, happiness, and

elevated blood pressure, was examined using

COMSOL Multiphysics, [22]. The heart's

contraction and relaxation brought on by blood flow

are analyzed by the movement of the heart cavity on

the device using predefined models. By adjusting

the cavity and mesh dimensions in COMSOL, the

values are examined, [23].

Anomalies in the cardiac arteries' blood flow

result in ischemia. It is possible to use the COMSOL

tool to reproduce this behavior. This method is used

on an apparatus that measures changes in the heart's

internal chamber that serve as an indirect indicator

of anomalies in the heart caused by ischemia, [24].

The method for completing the task required to meet

the given objective is shown in Figure 1.

Understanding cardiac function and how it relates to

mathematical models for use in COMSOL

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Multiphysics is the main objective, [25]. The

limitations that must be put in place and the

equation for data processing are shown in Figure 1.

Examining how the heart behaves in response to

different stresses and emotions is the technique's

main objective [26]. After finishing the heart study,

one must be able to understand the equations and

how to apply them to COMSOL. The resulting heart

diagrams were modeled and simulated, and their

behavior in response to applied inputs was observed,

[27].

Fig. 1: Outlines the project's flow

The next section will look at the fundamental

modeling of the heart using cavities and various

mesh designs to observe how ischemia affects the

heart for various time changes, [28].

3 Implementation of Comsol for

Unique Cardio Models

The strategy employed to achieve the desired

outcome is covered in this section. Complex

Landau-Ginzburg equations and Fitzhugh-Nagumo

equations COMSOL are used to implement the heart

movement through the use of equations, [29].

3.1 Equations of Fitzhugh-Nagumo

The foundational Fitzhugh-Nagumo equations may

be used to simulate the heart in COMSOL in order

to track and evaluate the impact of standard

equations and how they respond to various stimuli.

∂u1/δt = Δu +(α-u1) (u1-1) u1+(-u2) (1)

δu2/δt = ε(β.u1-γu2-δ) (2)

The activator variable u1 represents an action

potential, while the gate variable u2 represents a

gate, [30]. Assuming that there is no current flowing

into or out of the heart, the insulating boundary

conditions for u1 are established. (1) and (2) are

extreme examples.

u1 (0,x,y,z) = v0 ((x +d) > 0) ((z +d) > 0),

u2 (0,x,y,z) = v2 ((-x +d) > 0) ((z +d) > 0).

The initial circumstance describes an u1 initial

potential, where the v0 is increased potential when

at rest, and one of the cardiac quadrants is constant.

The values 'x' and 'd' for the modifications for the

dependent variable v0 should be less than zero.

The value (-x+d)'0' must also remain in v2. For

the equation to be satisfied, the value (z+d) must be

larger than zero. The following logical formulae,

where TRUE = 1 and FALSE = 0, can be used to

create this first distribution. d = 10-5 in this

instance, which is used in the computations to raise

the raised potential along the principal axes.

3.2 The Complex Landau-Ginzburg

Equations

δv1

δt − Δ(v1 − c1 v2)=

v1 − (v1 − c3 v2) (v12 − v22) (3)

The activator (3) variable is denoted by v1, and

the inhibitor (3) variable by v2. The characteristics

of the material are represented by the constants c3

and c1. The presence and nature of stable solutions

are likewise governed by these constants. The

following initial circumstances lead to a smooth

transition step close to z = 0:

 v1(0,x,y,z) = tanh(z)

 v2(0,x,y,z) = –tanh(z)

3.3 Parameters

The parameters needed to define equations 1, 2, and

3 are shown in Table 1. A wide range of variables

are accepted by the cardiac simulation shown in

Table 1. The values of the constants

alpha, epsilon, beta, delta, gamma, nu0, V0, d, c1,

and c3 are listed in Table 1.

Table 1. Defining Parameters for the Equations

Name

Expression

Description

epsilon

0.01

excitability

gamma

system’s parameter

alpha

0.1

excitation threshold

beta

0.5

system’s parameter

nu0

0.3

inhibitor value elevated

delta

system’s parameter

potential value elevated

1E−5

shift distance off-axis

−0.2

PDE parameter

Inserting mathematical modeling

during the simulation

Landau-Ginzburg equations

Fitzhugh - Nagumo equations

Observing the variations in terms

of the heart cavity and

mathematical values

Design of heart using COMSOL

Preview of heart

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Equations (1) and (2) are used to represent the

heart's structure on the simulation tool using the

values for alpha, beta, delta, and gamma. According

to Table 1, the PDE parameter's values for the

constants c1 and c3 are 2 and -0.2, respectively.

Table 2 displays the parameters for the element

edge, element vertex, triangle, tetrahedron, less

element quality, and average element quality that

were utilized to create the various models that were

used to study hearts using the COMSOL tool.

3.4 Models

The different models are meant to yield better

results than the cardiac simulations that the equation

represents. The fact that Models 1 and 4 have

distinct cavities but the same mesh value suggests

that ischemia has caused the heart tubes to enlarge.

In a similar vein, models 2 and 3 have different

cavities but the same mesh value. The section will

go into depth about the many qualities for which the

examination of these models is essential.

Since it analyses values in percentage terms, the

minimal element quality falls between 0 and 1, with

a value of almost 2.5 for all models. Moreover, all

four models have an average element quality of 0.7.

Models 1 through 4's edge values are presented in

decreasing order. Figure 2 displays the mesh

analysis for four different models: models 1, 2, 3,

and 4. This is represented by Table 1 and Table 2.

Table 2. Parameters Defined for the Equations

(a) (b)

Fig. 2: (a) Model 1 mesh view; (b) Model 2 mesh

view; (c) Model 3 mesh view; (d) Model 4 mesh

view.

4 Results and Discussion

To see the variations repeated on heart models, the

heart is implemented in COMSOL. This section

presents the final findings from comparing the

various models and modeling the heart on the tool as

shown in Figure 3.

Fig. 3: Images of the two-dimensional (right) and

three-dimensional (left) heart subdomains.

Atrioventricular node (AVN), Sinoatrial node

(SAN), Bundle branches (BNL), HIS: His bundle,

and Purkinje fibers (PKJ), [2]

4.1 Models – Spiral Waves

4.1.1 Model 1

Figure 4 depicts the simulated depiction of the heart

at various dependent variables utilized in equations

1 and 2. Figure 4(a) depicts a fluctuation in the

surface image of the heart for model 1 for dependent

variable u1 at time t = 500s. Figure 4(b) depicts the

Description

Value

(Model 1)

Value

(Model 2)

Value

(Model 3)

Value

(Model 4)

Minimum

element

quality

0.2592

0.2346

0.2518

0.2715

Average

element

quality

0.709

0.7065

0.6863

0.7186

Tetrahedron

28498

28621

17127

4572

Triangle

3802

4074

2762

1188

Edge

element

306

336

276

164

Vertex

element

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cavity view at t = 120s for the same model. After

applying the LG equation to two more plots, Figure

4(c) depicts the view of heart internal changes at

time t = 75s. Figure 4(d) depicts the spiral

perspective of model 1 at time t = 45s derived from

the LG equation.

(a) (b)

Fig. 4: (a) FN equation variation of heart internal at

time = 500s for u1, (b) FN equation variation of

heart surface at time = 120s, (c) Spiral view of heart

inside for variable v1 at time = 75s using LG

equation, and (d) spiral view of heart surface for

variable v1 at time = 45s using LG equation.

4.1.2 Model 2

The heart's modifications for Model 2 are predicated

on the mesh and cavity observations for blood flow.

A COMSOL simulation of the heart for model 2 is

shown in Figure 5. The surface-dependent variable

u1 from the FN equation is shown in Figure 5(a),

whereas the cavity view is shown in Figure 5(b). As

the simulation became increasingly significant,

Figure 5(c) shows the shift to the LG equation for

dependent variable v1 at tim1 t=75s. The final

surface picture for model 2 at time t=45s is shown in

Figure 5(d).

(a) (b)

Fig. 5: (a) heart interior variation for FN equation at

time = 0s for u1, (b) heart surface variation for FN

equation at time = 120s, (c) spiral view of heart

interior using LG equation for variable v1 at time =

75s, and (d) spiral view of heart surface using LG

equation for variable v1 at time = 45s.

4.1.3 Model 3

The mesh analysis pertains to model 2, even though

model 3 in this section shows a simulated image of

the heart that is defined for a different cavity than

the other models. By modeling the provided model,

the COMSOL tool produced the spiral wave shown

in Figure 6 for the FN and LG equations. The heart

variant for the cavity and mesh modifications

performed in model 3 is shown in Figure 6. For the

dependent variable u1 at time t = 500 s, the interior

cavity of the heart is shown in Figure 6(a). The

surface view for the same variable at t = 120s is

shown in Figure 6(b). The outcome of using the LG

equations and providing the boundary conditions in

COMSOL for variable v1 at t = 75s is displayed in

Figure 6(c). Additionally, the whole spiral

viewpoint for model 3 is displayed in Figure 6(d).

(a) (b)

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Fig. 6: (a) Heart interior variation for FN equation at

time = 500s for u1, (b) Heart surface variation for

FN equation at time = 120s, (c) Heart interior spiral

view using LG equation for variable v1 at time =

75s, and (d) Heart surface spiral view using LG

equation for variable v1 at time = 45s.

4.1.4 Model 4

Compared to model 1, model 4 has a different

cavity, but the mesh alterations are the same. This

section shows the simulated heart picture on

COMSOL for analysis, along with the heart's

behavior in response to the applied stimulus and its

reaction to the differential equation. Figure 7(b)

shows the same model for the cavity change at time

t = 120s, while Figure 7(a) shows the dependent

variable u1 for model 4 at time t = 500s. The heart is

shown in Figure 7(c) following the application of

the LG equation to the dependent variable v1. The

surface spiral wave for the same variable is shown

in Figure 7(d).

(a) (b)

Fig. 7: (a) heart interior variation for FN equation at

time = 500s for u1, (b) heart surface variation for

FN equation at time = 120s, (c) spiral view of heart

interior using LG equation for variable v1 at time =

75s, and (d) spiral view of heart surface using LG

equation for variable v1 at time = 45s.

4.2 Analysis of Values

This section discusses the thorough investigation of

the dependent variables on the simulated heart. The

waveforms that follow in this section illustrate the

many waveforms related to time and how they rely

on the heart's arc length.

Fig. 8: Variable u2 v/s arc length along with the time

change

Fig. 9: Variable u2, x component v/s arc length

along with the time change

The behavior of the heart for the dependent

variable u2 is seen in Figure 8 as the arc length from

10 grows. The value of u2 varies from -0.04 to a

maximum of 0.32 in the short range of arc length for

10+/- 5. At time t = 75s, the whole dimension

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change takes place. Figure 8 shows a sharp increase

in variable u2 with a constant arc length of 10.

Similarly, during a time of t = 75 s, the dependent

variable u2 in Figure 9 is altered along the x-

direction. The variation around the arc length of 10

is seen in Figure 13. The value quickly rises to 0.06

as the u2 gradient shifts. Thus, its observations are

the changes at an arc length of 10.

Fig. 10: Variable v1 v/s arc length along with the

time change

Fig. 11: Variable v1, first-time derivative v/s arc

length along with time change

The values for the dependent variable v1 that

match the LG equation are shown in Figure 9 over

several timelines that span from 0 to 75 seconds. At

t = 70s, the value may be observed. As seen in

Figure 10, the arc length of the 30s reaches the high

state value at various time frames, and the

fluctuation of v1 is from -1 to max value. The waves

look chaotic, but for all necessary timescales, waves

with an arc length of thirty offer the best view. The

variable v1, first-time derivative v/s arc length with

time change, is shown in Figure 11. where an arc

length of 10 and a period of 70s are used to modify

the value. Moreover, v1 for some time is essentially

zero. The figure shows the value of the derivative

since it is used in the LG equation.

Fig. 12: Variable v1, x component v/s arc length

along with the time change

Fig. 13: Variable V2 v/s arc length along with the

time change

Since it affects the design simulation as a whole,

the variable used in the LG equation to alter the

heart's cavity using COMSOL is crucial. Figure 12

shows the variable v1, the x component v/s arc

length, and the time change as the value varies

throughout a range of 10 arc lengths over the period

t = 35s, from -0.6 to +2. The dependent variable v2's

fluctuations across several time intervals are seen in

Figure 13. There is a change from -1 to +1 in the

variable v2. At arc length forty, the value change is

the largest. The arc length of most of the periods is

40.

Four models are used to assess and demonstrate

the overall contraction and relaxation of blood flow

in the heart utilizing waves in ischemia. This chapter

reviews the needed waves related to the dependent

variables v1 and v2, which are used in equations (1)

and (3), along with the simulated cardiac results.

5 Conclusion and Future Scope

The ischemia-related problem is resolved by

analyzing electrical activity in many cardiac cavities

using the Fitzhugh-Nagumo (FN) and Ginzburg-

Landau (GL) equations. The heart was simulated

using the COMSOL program to look for changes in

the cavity. Additionally, the values are measured

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and included in the observation tool. For a change in

arc length of 10, the maximum value of the model

for the variable u2 is +2. In a similar vein, the arc

length changes most at 30 with varied v1. The way

that contemporary medicine approaches human

problems like ischemia going forward will include

studying the heart as a tool. Additionally, by

modeling the pertinent equations and generating the

constraints or boundary values for the equation, this

approach helps the doctor identify the problem early

on and devise a speedy solution.

References:

[1] E J Benjamin, M S Bittencourt, P Muntner.

Heart Disease and Stroke Statistics -2019

update: A report from the American heart

association. Circ. 2019; 139(10): e56–e528.

https://doi.org/10.1161/CIR.00000000000006

[2] Biasi, N., Tognetti, A. Modelling whole heart

electrical activity for ischemia and cardiac

pacing simulation. Health Technol. 10, 837–

850 (2020). https://doi.org/10.1007/s12553-

020-00417-6

[3] Upasham, S., Bhide, A., Lin, K., and Prasad,

S Point-of-use sweat biosensor to track the

endocrine–inflammation relationship for

chronic disease monitoring. Future Science

OA, 7(1). https://doi.org/10.2144/fsoa-2020-

0097 , 2021.

[4] M Boulakia, Zemzemi N, Gerbeau J F,

Fernandez M A. Numerical Simulation of

Electro cardiograms. Modeling of

physiological flows, Springer, pp 77–106.

2012. https://doi.org/10.1007/978-88-470-

1935-5_4

[5] M Boulakia, S Cazeau, Zemzemi N,

Gerbeau J F, Fernandez M A. Mathematical

Modeling of Electro cardiograms: A

numerical study. Ann Biomed Eng.,

2010;38(3):1071–1097.

https://dx.doi.org/10.1007/s10439-009-9873-0

[6] Chávez CE, Coudie`re Y, Zemzemi N, A´

lvarez D, F Alonso Atienza. The inverse

problem of Electro cardiography: Estimating

the location of Cardiac ischemia in a 3D

realistic geometry. International Conference

on Functional Imaging and Modeling of the

Heart, Springer, pp 393–401. 2015.

https://doi.org/10.1007/978-3-319-20309-

6_45

[7] Niccoló Biasi, Tognetti Alessandro. Heart

Closedloop model for the assessment of

cardiac pacing. Mediterranean conference on

medical and biological engineering and

computing, Springer, pp 488–499. 2019.

http://dx.doi.org/10.1007/978-3-030-31635-

8_59

[8] Dokos S, Lovell N H, Cloherty S L.

Computational Model of Atrialelectrical

activation and propagation. IEEE Trans on

Medicine and Biology Society, 2007, pp 908-

911.

https://doi.org/10.1109/iembs.2007.4352438

[9] Ferrero J, J Saiz, B Trenor, Montilla F,

Hernandez V. Electrical Activity and reentry

in acute regional ischemia: Insights from

simulations. Proceedings of the 25th Annual

International Conference of the IEEE

Engineering in Medicine and Biology Society

(IEEE Cat. No.03CH37439), pp.17–20.

https://doi.org/10.1109/IEMBS.2003.1279483

[10] J.T. Strauss(2019), 25 - Metal injection

molding (MIM) of precious metals. Handbook

of Metal Injection Molding (Second Edition),

Woodhead Publishing 609-622.

https://doi.org/10.1016/B978-0-08-102152-

1.00030-1

[11] Joshi, Preeti & Sutrave, D.S. (2018). A Brief

Study of Cyclic Voltammetry and

Electrochemical Analysis. International

Journal of ChemTech Research, 11. 77-88.

https://doi.org/10.20902/IJCTR.2018.110911

[12] Cifrić, S., Nuhić, J., Osmanović, D., Kišija, E.

(2020). Review of Electrochemical

Biosensors for Hormone Detection. In:

Badnjevic, A., Škrbić, R., Gurbeta Pokvić, L.

(eds) CMBEBIH 2019. CMBEBIH 2019.

IFMBE Proceedings, vol 73. Springer, Cham.

https://doi.org/10.1007/978-3-030-17971-

7_27

[13] A M Janssen, T F Oostendorp, O Dossel,

Potyagaylo D. Assessment of the Equivalent

Dipole layer source model in the

Reconstruction of Cardiac activation times

based on BSPMS produced by an anisotropic

model of the heart. Medical & Biological

Engineering & Computing, 2018; 56: pp

1013–1025. https://doi.org/10.1007/s11517-

017-1715-x

[14] Zhihao Jiang, Sriram Radhakrishnan, R

Mangharam S Sarode, V Sampath,. Heart-on-

a-chip: A closed-loop testing platform for

implantable pacemakers, Scholarly Commons,

2014 [Online].

https://repository.upenn.edu/handle/20.500.14

332/40666 (Accessed Date: January 8, 2024).

[15] Joseph Lau, Balk EM, Ioannidis JP, Chew

PW, Milch C, Salem D Terrin N. Diagnosing

WSEAS TRANSACTIONS on BIOLOGY and BIOMEDICINE

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Javalkar Vinay Kumar, Shylashree Nagaraja,

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Acute Cardiac ischemia in Emergency

Department: A Systematic Review of the

Accuracy and Clinical Effect of Current

Technologies. Annals of Emergency Medicine,

2001; 37: pp 453– 460.

https://doi.org/10.1067/mem.2001.114903

[16] G T Lines, P Grottum, A Tveito. Modeling

the Electrical Activity of the heart: a

Bidomain Model of the Ventricles Embedded

in a Torso. Computing and Vision Science,

2003;5(4):195–213.

https://doi.org/10.1007/S00791-003-0100-5

[17] Potse. Scalable and accurate ecg simulation

for reaction-diffusion models of the human

heart. FrontPhysiol, 2018;9: pp 370.

https://doi.org/10.3389/fphys.2018.00370

[18] Q. Wang, “Mathematical methods for

biosensor models”, Technological University

Dublin, 2011, [Online].

https://arrow.tudublin.ie/cgi/viewcontent.cgi?

article=1124&context=sciendoc (Accessed

Date: January 8, 2024).

[19] M Lysaker, B F Nielsen. Towards a level set

framework for infarction modeling: An

inverse problem. International Journal of

Numerical Analysis and Modelling,

2006;3(4):377–394.

[20] MacLachlan, J Sundnes, G Lines. Simulation

of st segment changes during subendo Cardial

ischemia using a realistic 3-D Cardiac

geometry. IEEE Trans Biomed Eng.,

2005;52(5):799–807.

https://doi.org/10.1109/tbme.2005.844270

[21] B F Nielsen, P Grøttum, M Lysaker.

Computing ischemic regions in the heart with

the bidomain model—first steps towards

validation. IEEE Transaction on Medical

Imaging, 2013;32: pp 1085–1096.

https://dx.doi.org/10.1109/tmi.2013.2254123

[22] Yemane Tadesse, Abraha Tadese, R. C. Saini,

Rishi Pal, ”Cyclic Voltammetric Investigation

of Caffeine at Anthraquinone Modified

Carbon Paste Electrode”, International

Journal of Electrochemistry, vol. 2013,

Article ID 849327, 7 pages, 2013.

https://doi.org/10.1155/2013/849327

[23] Yang Su, Modeling and Characteristic Study

of Thin Film Based Biosensor Based on

COMSOL. Theory and Applications of

Complex Networks, 2004

https://doi.org/10.1155/2014/581063

[24] Amay J. Bandodkar, Itthipon Jeerapan, and

Joseph Wang Wearable Chemical Sensors:

Present Challenges and Future Prospects,

ACS Sens., 2016, 1, 5, 464–482,

https://doi.org/10.1021/acssensors.6b00250

[25] Bandodkar, A.J. and Wang, J. Non-invasive

wearable electrochemical sensors: a review.

Trends in Biotechnology, Vol. 32, Issue 7,

pp.363-371, July 2014,

https://doi.org/10.1016/j.tibtech.2014.04.005

[26] Alessandro Lavacchi, U. Bardi, C. Borri, S.

Caporali A. Fossati and I. Perissi Cyclic

voltammetry simulation at microelectrode

arrays with COMSOL, Journal of Applied

Electrochemistry, Vol. 39, pp.2159–2163,

2009, https://doi.org/10.1007/s10800-009-

9797-2

[27] Chernecky Cynthia C. and Barbara J. Berger.

2013. Laboratory Tests & Diagnostic

Procedures. Sixth ed. St. Louis Mo:

ClinicaKey, Elsevier, [Online].

http://www.clinicalkey.com/dura/browse/book

Chapter/3-s2.0-C20100683313 (Accessed

Date: January 8, 2024).

[28] G.D. Uttrachi, Productivity Factors in

Submerged Arc Line Pipe Welding

Applications. Pipeline and Energy Plant

Piping, Design and Technology, 1980, pp.

135-139. https://doi.org/10.1016/B978-0-08-

025368-8.50017-8

[29] Faraj (M.G.Faraj), Mohammad & Ibrahim,

Kamarulazizi & Ali, Mohammed. (2011).

PET as a plastic substrate for flexible

optoelectronic applications. Journal of

Optoelectronics and Advanced Materials-

Rapid Communications, Vol. 5, Issue 8, pp.

879–882, [Online].

http://www.scopus.com/inward/record.url?scp

=80052060657&partnerID=8YFLogxK

(Accessed Date: January 8, 2024).

[30] Highly Sensitive and selective detection of

steroid hormones using terahertz molecule-

specific sensors, Anal. Chem., 2019, 91, 10,

6844–6849

https://doi.org/10.1021/acs.analchem.9b01066

WSEAS TRANSACTIONS on BIOLOGY and BIOMEDICINE

DOI: 10.37394/23208.2024.21.2

Javalkar Vinay Kumar, Shylashree Nagaraja,

Yatish Devanand Vahvale, Sridhar Venugopalachar

E-ISSN: 2224-2902

Volume 21, 2024

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WSEAS TRANSACTIONS on BIOLOGY and BIOMEDICINE

DOI: 10.37394/23208.2024.21.2

Javalkar Vinay Kumar, Shylashree Nagaraja,

Yatish Devanand Vahvale, Sridhar Venugopalachar

E-ISSN: 2224-2902

Volume 21, 2024