• Journal of Yeungnam Medical Science
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• 제38권2호
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• pp.95-106
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• 2021

Infections involving the heart are becoming increasingly common, and a timely diagnosis of utmost importance, despite its challenges. F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is a recently introduced diagnostic tool in cardiology. This review focuses on the current evidence for the use of FDG PET/CT in the diagnosis of infective endocarditis, cardiac implantable device infection, left ventricular assist device infection, and secondary complications. The author discusses considerations when using FDG PET/CT in routine clinical practice, patient preparation for reducing physiologic myocardial uptake, acquisition of images, and interpretation of PET/CT findings. This review also functions to highlight the need for a standardized acquisition protocol.

• Journal of information and communication convergence engineering
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• 제21권2호
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• pp.130-138
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• 2023

The selection of the correct examination variables for diagnosing heart disease provides many benefits, including faster diagnosis and lower cost of examination. The selection of inspection variables can be performed by referring to the data of previous examination results so that future investigations can be carried out by referring to these selected variables. This paper proposes a model for selecting examination variables using an Artificial Bee Swarm Optimization method by considering the variables of accuracy and cost of inspection. The proposed feature selection model was evaluated using the performance parameters of accuracy, area under curve (AUC), number of variables, and inspection cost. The test results show that the proposed model can produce 24 examination variables and provide 95.16% accuracy and 97.61% AUC. These results indicate a significant decrease in the number of inspection variables and inspection costs while maintaining performance in the excellent category.

• Healthcare Informatics Research
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• 제24권4호
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• pp.283-291
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• 2018

Objectives: To identify the current status of smartphone usage and to describe the needs for smartphone-based cardiac telerehabilitation of cardiac patients. Methods: In 2016, a questionnaire survey was conducted in a supervised ambulatory cardiac rehabilitation (CR) program in a university affiliated hospital with the participation of heart failure or heart transplantation patients who were smartphone users. The questionnaire included questions regarding smartphone usage, demands for smartphone-based disease education, and home health monitoring systems. Results were described and analyzed according to principal diagnosis. Results: Ninety-six patients (66% male; mean age, $5{\pm}11$ years), including 56 heart failure and 40 heart transplantation patients, completed the survey (completion rate, 95%). The median daily smartphone usage time was 120 minutes (interquartile range, 60-300), and the most frequently used smartphone function was text messaging (61.5%). Of the patients, 26% stated that they searched for health-related information using their smartphones more than 1 time per week. The major source of health-related information was Internet browsing (50.0%), and the least sought source was the hospital's website (3.1%). Patients with heart failure expressed significantly higher needs for disease education on treatment plan, home health monitoring of blood pressure, and body weight (${\chi}^2=5.79$, 6.27, 4.50, p < 0.05). Heart transplantation patients expressed a significant need for home health monitoring of body temperature (${\chi}^2=5.25$, p < 0.05). Conclusions: Heart failure and heart transplantation patients show high usage of and interest in mobile health technology. A smartphone-based cardiac telerehabilitation program should be developed based on high demand areas and modified to suit to each principal diagnosis.

• Parasites, Hosts and Diseases
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• 제55권4호
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• pp.429-431
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• 2017

Hydatid cyst is usually located in the liver and lungs, rare cases showing localization in other organs or tissues. In the unusual location, echinococcosis is an excluding diagnosis that is established only after microscopic evaluation. Our first case occurred in a 67-year-old female previously diagnosed with pulmonary tuberculosis and hospitalized with persistent pain in the hip joint. The clinical diagnosis was tuberculosis of the joint, but the presence of the specific acellular membrane indicated a hydatid cyst of the synovial membrane, without bone involvement. Fewer than 25 cases of joint hydatidosis have been reported in literature to date. In the second case, the intramural hydatid cyst was incidentally discovered at autopsy, in the left heart ventricle of a 52-year-old male hospitalized for a fatal brain hemorrhage, as a result of rupture of an anterior communicating artery aneurysm. The conclusion of our paper is that echinococcosis should be taken into account for the differential diagnosis of cystic lesions, independently from their location.

• Journal of Multimedia Information System
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• 제3권2호
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• pp.35-42
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• 2016

Electrocardiogram (ECG) signal gives a clear indication whether the heart is at a healthy status or not as the early notification of a cardiac problem in the heart could save the patient's life. Several methods were launched to clarify how to diagnose the abnormality over the ECG signal waves. However, some of them face the problem of lack of accuracy at diagnosis phase of their work. In this research, we present an accurate and successive method for the diagnosis of abnormality through Discrete Wavelet Transform (DWT), QRS complex detection and Support Vector Machines (SVM) classification with overall accuracy rate 95.26%. DWT Refers to sampling any kind of discrete wavelet transform, while SVM is known as a model with related learning algorithm, which is based on supervised learning that perform regression analysis and classification over the data sample. We have tested the ECG signals for 10 patients from different file formats collected from PhysioNet database to observe accuracy level for each patient who needs ECG data to be processed. The results will be presented, in terms of accuracy that ranged from 92.1% to 97.6% and diagnosis status that is classified as either normal or abnormal factors.

• Korean Journal of Radiology
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• 제23권6호
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• pp.581-597
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• 2022

Left ventricular (LV) wall thickening, or LV hypertrophy (LVH), is common and occurs in diverse conditions including hypertrophic cardiomyopathy (HCM), hypertensive heart disease, aortic valve stenosis, lysosomal storage disorders, cardiac amyloidosis, mitochondrial cardiomyopathy, sarcoidosis and athlete's heart. Cardiac magnetic resonance (CMR) imaging provides various tissue contrasts and characteristics that reflect histological changes in the myocardium, such as cellular hypertrophy, cardiomyocyte disarray, interstitial fibrosis, extracellular accumulation of insoluble proteins, intracellular accumulation of fat, and intracellular vacuolar changes. Therefore, CMR imaging may be beneficial in establishing a differential diagnosis of LVH. Although various diseases share LV wall thickening as a common feature, the histologic changes that underscore each disease are distinct. This review focuses on CMR multiparametric myocardial analysis, which may provide clues for the differentiation of thickened myocardium based on the histologic features of HCM and its phenocopies.

• Clinical and Experimental Pediatrics
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• 제58권7호
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• pp.256-262
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• 2015

Purpose: Kabuki syndrome is a multiple congenital malformation syndrome, with characteristic facial features, mental retardation, and skeletal and congenital heart anomalies. However, the cardiac anomalies are not well described in the Korean population. We analyzed the cardiac anomalies and clinical features of Kabuki syndrome in a single tertiary center. Methods: A retrospective analysis was conducted for a total of 13 patients with Kabuki syndrome. Results: The median age at diagnosis of was 5.9 years (range, 9 days to 11 years and 8 months). All patients showed the characteristic facial dysmorphisms and congenital anomalies in multiple organs, and the diagnosis was delayed by 5.9 years (range, 9 days to 11 years and 5 months) after the first visit. Noncardiac anomalies were found in 84% of patients, and congenital heart diseases were found in 9 patients (69%). All 9 patients exhibited left-side heart anomalies, including hypoplastic left heart syndrome in 3, coarctation of the aorta in 4, aortic valve stenosis in 1, and mitral valve stenosis in 1. None had right-side heart disease or isolated septal defects. Genetic testing in 10 patients revealed 9 novel MLL2 mutations. All 11 patients who were available for follow-up exhibited developmental delays during the median 4 years (range, 9 days to 11 years 11 months) of follow-up. The leading cause of death was hypoplastic left heart syndrome. Conclusion: Pediatric cardiologist should recognize Kabuki syndrome and the high prevalence of left heart anomalies with Kabuki syndrome. Genetic testing can be helpful for early diagnosis and counseling.

• 한국멀티미디어학회논문지
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• 제24권11호
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• pp.1500-1507
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• 2021

Identification and analysis of the first and second heart sounds(S1, S2) is the easiest way for cardiovascular disease prevention and early diagnosis. However, accurate identification is difficult because the heart sound includes organ movement, blood vortex, user experience, and noise influenced by subjective judgment. Therefore, an algorithm to automatically identify the S1 and S2 heart sounds based on blood vessel transit time(VTT) is presented in this paper. According to the experimental results of comparing the algorithm developed for S1 and S2 heart sound analysis with the conventional Shannon energy algorithm in 10 volunteers, it has been proven that the proposed algorithm can automatically identify S1 and S2 heart sounds with higher accuracy than existing algorithms.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.77-88
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• 2021

Heart Failure represents a critical pathological case that is challenging to predict and discover at an early age, with a notable increase in morbidity and mortality. Machine Learning and Neural Network techniques play a crucial role in predicting heart attacks, diseases and more. These techniques give valuable perspectives for clinicians who may then adjust their diagnosis for each individual patient. This paper evaluated neural network models for heart attacks predictions. Several online learning methods were investigated to automatically and accurately predict heart attacks. The UCI dataset was used in this work to train and evaluate First Order and Second Order Online Learning methods; namely Backpropagation, Delta bar Delta, Levenberg Marquardt and QuickProp learning methods. An optimizer technique was also used to minimize the random noise in the database. A regularization concept was employed to further improve the generalization of the model. Results show that a three layers' NN model with a Backpropagation algorithm and Nadam optimizer achieved a promising accuracy for the heart attach prediction tasks.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.328-330
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• 2004

In this paper, ECG based cardiac disease diagnosis models are developed. Conventionally, ECG monitoring equipments can only measure and store ECG signals and they always require medical doctor's diagnosis actions which are not desirable for continuous ambulatory monitoring and diagnosis healthcare systems. In this paper, two kinds of neural based self cardiac disease diagnosis engines are developed and tested for four kinds of diseases, sinus bradycardia, sinus tachycardia, left bundle branch block and right bundle branch block. For diagnosis engines, error backpropagation neural network (BP) and probabilistic neural network (PNN) were applied. Five signal features including heart rate, QRS interval, PR interval, QT interval, and T wave types were selected for diagnosis characteristics. To show the validity of proposed diagnosis engine, MIT-BIH database were used to test. Test results showed that BP based diagnosis engine has 71% of diagnosis accuracy which is superior to accuracy of PNN based diagnosis engine. However, PNN based diagnosis engine showed superior diagnosis accuracy for complex-disease diagnoses than BP based diagnosis engine.