Abstract: Electrocardiographic (ECG) machine is an important equipment to diagnose heart problems. Besides, the ECG signals are used to detect many other features of human body and behavior. But it is not so cheap and simple in operation to be used in the countries like Bangladesh, where most of the people are very low income earners. Therefore, in this paper, we have tried to implement a simple and portable ECG machine. Since Arduino Uno microcontroller is very cheap, we have used it in our system to minimize the cost. Our designed system is powered by a 2-voltage level DC power supply. It provides wireless connectivity to have ECG data either in smartphone having android operating system or a PC/laptop having Windows operating system. To get the data, a graphic user interface has been designed. Android application has also been made using IDE for Android 2.3 and API 10. Since it requires no USB host API, almost 98% Android smartphones, available in the country, will be able to use it. We have calculated the heart rate from the measured ECG by our designed machine and by an ECG machine of a reputed diagnostic center in Dhaka city for the same people at the same time on same day. Then we calculated the percentage of errors between the readings of two machines and computed its average. From this computation, we have found out that the average percentage of error is within an acceptable limit.
Abstract: This paper presents wavelet based classification of various heart diseases. Electrocardiogram signals of different heart patients have been studied. Statistical natures of electrocardiogram signals for different heart diseases have been compared with the statistical nature of electrocardiograms for normal persons. Under this study four different heart diseases have been considered as follows: Myocardial Ischemia (MI), Congestive Heart Failure (CHF), Arrhythmia and Sleep Apnea. Statistical nature of electrocardiograms for each case has been considered in terms of kurtosis values of two types of wavelet coefficients: approximate and detail. Nine wavelet decomposition levels have been considered in each case. Kurtosis corresponding to both approximate and detail coefficients has been considered for decomposition level one to decomposition level nine. Based on significant difference, few decomposition levels have been chosen and then used for classification.
Abstract: Cardiologists perform cardiac auscultation to detect
abnormalities in heart sounds. Since accurate auscultation is
a crucial first step in screening patients with heart diseases,
there is a need to develop computer-aided detection/diagnosis
(CAD) systems to assist cardiologists in interpreting heart sounds
and provide second opinions. In this paper different algorithms
are implemented for automated heart sound classification using
unsegmented phonocardiogram (PCG) signals. Support vector
machine (SVM), artificial neural network (ANN) and cartesian
genetic programming evolved artificial neural network (CGPANN)
without the application of any segmentation algorithm has been
explored in this study. The signals are first pre-processed to remove
any unwanted frequencies. Both time and frequency domain features
are then extracted for training the different models. The different
algorithms are tested in multiple scenarios and their strengths and
weaknesses are discussed. Results indicate that SVM outperforms
the rest with an accuracy of 73.64%.
Abstract: Childhood obesity, which may lead to increased risk for heart diseases in children as well as adults, is one of the most important health problems throughout the world. Prevalences of morbid obesity and metabolic syndrome (MetS) are being increased during childhood age group. MetS is a cluster of metabolic and vascular abnormalities including hypercoagulability and an increased risk of cardiovascular diseases (CVDs). There are also some relations between some components of MetS and leukocytes. The aim of this study is to investigate complete blood cell count parameters that differ between morbidly obese boys and girls with MetS diagnosis. A total of 117 morbid obese children with MetS consulted to Department of Pediatrics in Faculty of Medicine Hospital at Namik Kemal University were included into the scope of the study. The study population was classified based upon their genders (60 girls and 57 boys). Their heights and weights were measured and body mass index (BMI) values were calculated. WHO BMI-for age and sex percentiles were used. The values above 99 percentile were defined as morbid obesity. Anthropometric measurements were performed. Waist-to-hip and head-to-neck ratios as well as homeostatic model assessment of insulin resistance (HOMA-IR) were calculated. Components of MetS (central obesity, glucose intolerance, high blood pressure, high triacylglycerol levels, low levels of high density lipoprotein cholesterol) were determined. Hematological variables were measured. Statistical analyses were performed using SPSS. The degree for statistical significance was p ≤ 0.05. There was no statistically significant difference between the ages (11.2±2.6 years vs 11.2±3.0 years) and BMIs (28.6±5.2 kg/m2 vs 29.3±5.2 kg/m2) of boys and girls (p ≥ 0.05), respectively. Significantly increased waist-to-hip ratios were obtained for boys (0.94±0.08 vs 0.91±0.06; p=0.023). Significantly elevated values of hemoglobin (13.55±0.98 vs 13.06±0.82; p=0.004), mean corpuscular hemoglobin concentration (33.79±0.91 vs 33.21±1.14; p=0.003), eosinophils (0.300±0.253 vs 0.196±0.197; p=0.014), and platelet (347.1±81.7 vs 319.0±65.9; p=0.042) were detected for boys. There was no statistically significant difference between the groups in terms of neutrophil/lymphocyte ratios as well as HOMA-IR values (p ≥ 0.05). Statistically significant gender-based differences were found for hemoglobin as well as mean corpuscular hemoglobin concentration and hence, separate reference intervals for two genders should be considered for these parameters. Eosinophils may contribute to the development of thrombus in acute coronary syndrome. Eosinophils are also known to make an important contribution to mechanisms related to thrombosis pathogenesis in acute myocardial infarction. Increased platelet activity is observed in patients with MetS and these individuals are more susceptible to CVDs. In our study, elevated platelets described as dominant contributors to hypercoagulability and elevated eosinophil counts suggested to be related to the development of CVDs observed in boys may be the early indicators of the future cardiometabolic complications in this gender.
Abstract: The number of natural or organic product consumers is increased in recent years and this healthy demand pushes to increase usage of healthy meat. At the same time, consumers pay more attention on the healthy fat, especially on unsaturated fatty acids. These long chain carbohydrates reduce heart diseases, improve memory and eye sight and activate the immune system. One of the important issues to be solved for our Mongolia’s food security is to provide healthy, fresh, widely available and cheap meat for the population. Thus, an importance of the Selenge breed meat production is increasing in order to supply the quality meat food security since the Selenge breed cattle are rapidly multiplied, beneficial in term of income, the same quality as Mongolian breed, and well digested for human body. We researched the lipid, unsaturated and saturated fatty acid contents of meat of Selenge breed younger cattle by their muscle types. Result of our research reveals that 11 saturated fatty acids are detected. For the content of palmitic acid among saturated fatty acids, 23.61% was in the sirloin meat, 24.01% was in the round and chuck meat, and 24.83% was in the short loin meat.
Abstract: Measuring the Electrocardiogram (ECG) signal is an
essential process for the diagnosis of the heart diseases. The ECG
signal has the information of the degree of how much the heart
performs its functions. In medical diagnosis and treatment systems,
Decision Support Systems processing the ECG signal are being
developed for the use of clinicians while medical examination. In this
study, a modular wireless ECG (WECG) measuring and recording
system using a single board computer and e-Health sensor platform
is developed. In this designed modular system, after the ECG signal
is taken from the body surface by the electrodes first, it is filtered and
converted to digital form. Then, it is recorded to the health database
using Wi-Fi communication technology. The real time access of the
ECG data is provided through the internet utilizing the developed
web interface.
Abstract: The paper presents a novel screening method to
indicate congenital heart diseases (CHD), which otherwise could
remain undetected because of their low level. Therefore, not
belonging to the high-risk population, the pregnancies are not subject
to the regular fetal monitoring with ultrasound echocardiography.
Based on the fact that CHD is a morphological defect of the heart
causing turbulent blood flow, the turbulence appears as a murmur,
which can be detected by fetal phonocardiography (fPCG). The
proposed method applies measurements on the maternal abdomen
and from the recorded sound signal a sophisticated processing
determines the fetal heart murmur. The paper describes the problems
and the additional advantages of the fPCG method including the
possibility of measurements at home and its combination with the
prescribed regular cardiotocographic (CTG) monitoring. The
proposed screening process implemented on a telemedicine system
provides an enhanced safety against hidden cardiac diseases.
Abstract: Heterogeneous repolarization causes dispersion of the T-wave and has been linked to arrhythmogenesis. Such heterogeneities appear due to differential expression of ionic currents in different regions of the heart, both in healthy and diseased animals and humans. Mice are important animals for the study of heart diseases because of the ability to create transgenic animals. We used our previously reported model of mouse ventricular myocytes to develop 2D mouse ventricular tissue model consisting of 14,000 cells (apical or septal ventricular myocytes) and to study the stability of action potential propagation and Ca2+ dynamics. The 2D tissue model was implemented as a FORTRAN program code for highperformance multiprocessor computers that runs on 36 processors. Our tissue model is able to simulate heterogeneities not only in action potential repolarization, but also heterogeneities in intracellular Ca2+ transients. The multicellular model reproduced experimentally observed velocities of action potential propagation and demonstrated the importance of incorporation of realistic Ca2+ dynamics for action potential propagation. The simulations show that relatively sharp gradients of repolarization are predicted to exist in 2D mouse tissue models, and they are primarily determined by the cellular properties of ventricular myocytes. Abrupt local gradients of channel expression can cause alternans at longer pacing basic cycle lengths than gradual changes, and development of alternans depends on the site of stimulation.
Abstract: Case based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computeraided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g. the problems of knowledge acquisition, remembering, robust and maintenance. This paper discusses the CBR methodology, the research issues and technical aspects of implementing intelligent medical diagnoses systems. Successful applications in cancer and heart diseases developed by Medical Informatics Research Group at Ain Shams University are also discussed.
Abstract: The processing of the electrocardiogram (ECG) signal consists essentially in the detection of the characteristic points of
signal which are an important tool in the diagnosis of heart diseases. The most suitable are the detection of R waves. In this paper, we
present various mathematical tools used for filtering ECG using digital filtering and Discreet Wavelet Transform (DWT) filtering. In
addition, this paper will include two main R peak detection methods
by applying a windowing process: The first method is based on calculations derived, the second is a time-frequency method based on
Dyadic Wavelet Transform DyWT.
Abstract: In this study, fuzzy rule-based classifier is used for the
diagnosis of congenital heart disease. Congenital heart diseases are
defined as structural or functional heart disease. Medical data sets
were obtained from Pediatric Cardiology Department at Selcuk
University, from years 2000 to 2003. Firstly, fuzzy rules were
generated by using medical data. Then the weights of fuzzy rules
were calculated. Two different reasoning methods as “weighted vote
method" and “singles winner method" were used in this study. The
results of fuzzy classifiers were compared.
Abstract: Chronic diseases prevailed along with economic
growth as well as life style changed in recent years in Taiwan.
According to the governmental statistics, hypertension related disease
is the tenth of death causes with 1,816 died directly from hypertension
in 2010. There were more death causes amongst the top ten had been
proofed that having strong association with the hypertension, such as
heart diseases, cardiovascular diseases, and diabetes. Hypertension or
High blood pressure is one of the major indicators for chronic diseases,
and was generally perceived as the major causes of mortality. The
literature generally suggested that regular physical exercise was
helpful to prevent the occurrence or to ease the progress of a
hypertension. This paper reported the process and outcomes in
detailed of an improvement project of physical exercise intervention
specific for hypertension patients. Physical information were
measured before and after the project to obtain information such as
weight, waistline, cholesterol (HD & LD), blood examination, as well
as self-perceived health status. The intervention project involved a
six-week exercise program, of which contained three times a week, 30
minutes of tutored physical exercise intervention. The project had
achieved several gains in changing the subjects- behavior in terms of
many important biophysical indexes. Around 20% of the participants
had significantly improved their cholesterols, BMI, and changed
unhealthy behaviors. Results from the project were encouraging, and
would be good reference for other samples.
Abstract: Heart failure is the most common reason of death
nowadays, but if the medical help is given directly, the patient-s life
may be saved in many cases. Numerous heart diseases can be
detected by means of analyzing electrocardiograms (ECG). Artificial
Neural Networks (ANN) are computer-based expert systems that
have proved to be useful in pattern recognition tasks. ANN can be
used in different phases of the decision-making process, from
classification to diagnostic procedures. This work concentrates on a
review followed by a novel method.
The purpose of the review is to assess the evidence of healthcare
benefits involving the application of artificial neural networks to the
clinical functions of diagnosis, prognosis and survival analysis, in
ECG signals. The developed method is based on a compound neural
network (CNN), to classify ECGs as normal or carrying an
AtrioVentricular heart Block (AVB). This method uses three
different feed forward multilayer neural networks. A single output
unit encodes the probability of AVB occurrences. A value between 0
and 0.1 is the desired output for a normal ECG; a value between 0.1
and 1 would infer an occurrence of an AVB. The results show that
this compound network has a good performance in detecting AVBs,
with a sensitivity of 90.7% and a specificity of 86.05%. The accuracy
value is 87.9%.
Abstract: Artificial Neural Network (ANN) has been
extensively used for classification of heart sounds for its
discriminative training ability and easy implementation. However, it
suffers from overparameterization if the number of nodes is not
chosen properly. In such cases, when the dataset has redundancy
within it, ANN is trained along with this redundant information that
results in poor validation. Also a larger network means more
computational expense resulting more hardware and time related
cost. Therefore, an optimum design of neural network is needed
towards real-time detection of pathological patterns, if any from heart
sound signal. The aims of this work are to (i) select a set of input
features that are effective for identification of heart sound signals and
(ii) make certain optimum selection of nodes in the hidden layer for a
more effective ANN structure. Here, we present an optimization
technique that involves Singular Value Decomposition (SVD) and
QR factorization with column pivoting (QRcp) methodology to
optimize empirically chosen over-parameterized ANN structure.
Input nodes present in ANN structure is optimized by SVD followed
by QRcp while only SVD is required to prune undesirable hidden
nodes. The result is presented for classifying 12 common
pathological cases and normal heart sound.