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The Cardio Kinesiograph System (CKinG) is a novel computerized diagnostic system incorporating a computer model of cardiac kinesis. Cardiac Kinesis (CK) is the interpretation of heart movement or electrical activity of specialized cardiac muscle cells in response to biochemical reactions. The mechanical events occurring during the cardiac cycle consist of changes in pressure in the ventricular chamber which cause(s) blood to move in and out of the ventricle . The events of the cardiac cycle start with an electrical signal and proceed through excitation-contraction coupling (which involves chemical and mechanical events) to contraction of the ventricle (pressure generation) and ejection of blood (flow) into the pulmonary and systemic circulations . Thus, we can characterize the cardiac cycle by tracking changes in ventricular volume (LVV), ventricular pressure (LVP), left atrial pressure (LAP) and aortic pressure (AoP) . In the other hand, because electrical events always precede mechanical events in the cardiac cycle, distortions of a part or parts of the electrical signal have been used as diagnostic indicators of both electrical and mechanical dysfunctions of the heart muscle . There are numerous methods and technical systems available for diagnosis or predict heart disease. However, medical errors and undesirable results  in these systems are reasons for a need for conventional computer-based diagnosis of heart diseases systems. The purpose of this study is to design and develop a system that can observe and analyze Kinesiographical Cardiac basted on statistical models and identifies some fundamental characteristic of heart motions.
Keywords: Cardiovascular biomechanics; Statistical analysis methods; Medical heart imaging modalities; DNA modelization in biomedical image
Since 1950 cardiologists have studied the functions of heart moments to employ them in the diagnostics of ischemic heart disease (IHD). Indeed, changes of the movements have found their diagnostic application in this field. If blood supply to a certain area of ventricular myocardium is insufficient the con-tractions in this area diminish and even ceases. After systolic increase in ventricular pressure this area dilates and forces intercostal tissues out, causing a “bulge” wave on the record.
Cardiokymography was one of several noninvasive techniques able to detect coronary artery disease. It can qualitatively determine abnormal left ventricular motion, and, based on animal models, this can be directly related to abnormalities in the left coronary artery . Nevertheless sensitivity of cardiokymography in detecting patients with ischaemic left ventricular wall motion abnormalities depended on the extent of left ventricular ischaemia . Cardiokymography is no longer valid . Today Computer Aided Diagnosis (CAD) is one of the trusted methods in the field of medicine . Advances in medical imaging and image processing techniques have greatly enhanced interpretation of medical images. Computer aided diagnosis (CAD) systems based on these techniques play a vital role  in the early detection of cardiovascular diseases and hence reduce death rate. CAD is the most preferable method for the initial diagnosis of heart disease. The combination of Digital and Medical Image Pro-cessing, Cardiac Electrophysiology, Ventricular Pressure-Volume Technique and Phonocardiogram etc makes the CAD system more reliable and efficient. One
example of the medical applications in computer aided diagnosis is the detection system for heart disease based on Cardiovascular Magnetic Resonance (CMR). Cardiovascular magnetic resonance of the heart provides a potentially useful way to assess cardiac mechanical function. Besides CMR, positron emission tomography (PET), and cardiac CT are able to illustrate kinesis of cardiac muscles. Perfusion imaging with cardiac PET is used clinically to produce images of myocardial blood flow, aiding the diagnosis of coronary artery disease and the monitoring of condition of coronary circulation in response to treatment .
Clinical imaging in positron emission tomography (PET) is often performed using single-time-point estimates of tracer uptake or static imaging that provides a spatial map of regional tracer concentration . However, dynamic cardiac techniques (e.g. PET, Myocardial perfusion imaging and Dynamic cardiac SPECT ) are used to estimate rate parameters activity of myocardial blood flow, and there are limited studies evaluating the role of Cardiovascular Magnetic Resonance and cardiac PET and cardiac CT for the assessment of cardiac kinesis. Therefore, scientific communities in computational cardiovascular science  have contributed to developing mathematical models and algorithms to improve efficiency of cardiac safety data management in clinical trials. In this way in 2014 proposed a new method based on a mathematical model, “Fourier Transform” which calculates an amplitude parametric image for the assessment of cardiac kinetics. This image, calculated from the Cine MR images, allows the localization and quantification of abnormalities related to difference in contraction and their
extent . In 2015 Zakynthinaki  has also presented
effective mathematical model of heart rate kinetics in response
to movement. She made conclusion that the new model is
able not only to provide important information regarding an
individual’s cardiovascular condition but to also simulate and
predict heart rate kinetics for any given exercise intensities. The
existing models of cardiac kinetics focus mainly on amplitude
images or are limited to simulation of biological transformation.
The present study provides a novel mathematical model of
cardiac kinesis based on visual presentation of numerical data
(obtained through the cardiac kinesis) in the form of graph,
with a particular focus on Human Cardio Kinesiograph Analysis.
The purpose of this study was to de-sign, develop and evaluate
a novel method for Kinesiograph of Cardiac for the clinical
assessment of cardiac and vascular function.
Data collection: In order to obtain an accurate data,
the Cardiovascular Magnetic Resonance imaging (CMR),
Cardiovascular Ultrasound or Cardiac Computed Tomography
are produced better performances for detection of cardiac
mobility. However, CMR is provided the most comprehensive
anatomic picture for patient selection . The system therefore
is obtained relevant information from the CMR.
Video quality assessment: Different medical imaging
methods may introduce common artifacts include image
distortion, signal pileup (bright regions), and image dropout
(area without signal) , therefore the quality assessment is
an important factor at the operational level. The assessment
of quality of video depends upon the type of distortion .
Numerous video quality assessment methods and metrics have
been proposed over the past years with varying computational
complexity and accuracy . We utilize different forms of
quality assessment methods, however the merit of these methods
is often judged by assessing the quality of a set of results through
lengthy user studies .
Motion estimation and inertial measurements: Motion
estimation is the process of determining the movement of
blocks between adjacent video frames (MathWorks). Efficient
and accurate motion estimation is an essential component in
the domains of image sequence analysis  and medical video
processing. The estimation of motion is also important from the
viewpoint of matching metric technique, which is computed
the context similarity between two images. There exist several
methods for motion estimation image, and video processing (e.g.
pixel-based motion estimation, block-based motion estimation,
optical flow method). In this study in order to increase the
computational accuracy and improve efficiency in solving
problem, DNA Modeling (Dawoudi, 2017) method in biomedical
image matching has been proposed. The method is based on the
linear mapping and the one-to-one correspondences between
point features extracted from the frames and on calculating
similarities in pixel values. This correspondence is determined
by comparing two strings constructed from pixel values of the
frames. The method uses a table called the Quarter Code table,
which is the set of characters and numbers. In this table every
number between 0 and 255 is translated into a unique string of
four letter alphabet. Letters A,C,G,T are chosen, since they are
the same as used in DNA sequences. In this way it possible to
utilize tools originally programmed to DNA sequences analysis.
When all pixel values of the frames (images) are converted to
virtual DNA sequences, one can show the differences between
two virtual DNA sequences.
Visual representation of numerical data in the form
of graphs: The E-value gives a measure of the similarity of
sequences. From this function we can obtain the correlation
coefficient which will give us a single value of similarity. The rate
of similarity between sequences (frames) is plotted as a graph
and it’s appearing in the Monitor.
The information is obtained by extracting frames from CMR
imaging video. There are different tools in order to extract
frames from cardiac MRI Video (e.g. Free Video to JPG Converter,
VLC or Virtual Dud) (Figure 1).
The difference between two adjacent frames is used to
estimate motion direction and magnitude. This process has been
implemented within a tool called Image Diff. The Perforce image
diff tool enables researchers to compare two adjacent frames.
The following represents pixels difference value (Percent
Changed; Pixels) and color difference value (Percent Change;
Color) between adjacent Frames (Table 1).
In conclusion, the proposed Cardio Kinesiograph System
(CKinG) may become a robust and efficient tool for the
clinical assessment of Cardiac and Vascular function. CKinGbased
Computer-Aided Diagnosis (CAD) holds the promise of
improving the diagnosis accuracy and reducing the cost.
The developed system can be used as a prototype in the
clinical sectors for the evaluation of cardiovascular diseases.
However, a novel method for real time MRI of cardiac kinesis
and simultaneously Kinesiographical Cardiac Analysis Based on
Statistical Methods are proposed. The proposed methods may
become efficient Medical Diagnostic Support tools (DSTs) for