• Journal of Industrial Technology
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• v.22 no.B
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• pp.163-167
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• 2002

Autonomic nervous system play an important role of keeping our health as balancing homeostasis. But the abnormality of these abilities makes our presence be feeble. To obtain these information of body which helps for us to decide whether one is healthy or not, based on the study of Heart Rate Variability. In this paper, we presented HRV model and its processing steps to extract some information of human body. After that, some experimental results are presented in time-domain and frequency-domain.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.90-96
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• 2004

The objective of this study was to develop a model for safe work load based on a physiological model of metabolic energy of manual material handling tasks. Fifteen male subjects voluntarily participated in this study. Lifting activities with four different weights, 0, 8, 16, 24kg, and four different working frequencies (2, 5, 8, 11 lifts/min) for a lifting range from floor to the knuckle height of 76cm were considered. Oxygen consumption rates and heart rates were measured during the performance of sixteen different lifting activities. Simplified predictive equations for estimating the oxygen consumption rate and the heart rate were developed. The oxygen consumption rate and the heart rate could be expressed as a function of task variables; frequency and the weight of the load, and a personal variable, body weight, and their interactions. The coefficients of determination ($r^2$) of the model were 0.9777 and 0.9784, respectively, for the oxygen consumption rate and the heart rate. The model of oxygen consumption rate was modified to estimate the work load for the given oxygen consumption rate. The overall absolute percent errors of the validation of this equation for work load with the original data set was 39.03%. The overall absolute percent errors were much larger than this for the two models based on the US population. The models for the oxygen consumption rate and for the work load developed in this study work better than the two models based on the US population. However, without considering the biomechanical approach, the developed model for the work load and the two US models are not recommended to estimate the work loads for low frequent lifting activities.

• International Journal of Advanced Culture Technology
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• v.5 no.3
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• pp.40-45
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• 2017

This data mining technique was used to extract useful information from percutaneous coronary intervention data obtained from the US public data homepage. The experiment was performed by extracting data on the area, frequency of operation, and the number of deaths. It led us to finding of meaningful correlations, patterns, and trends using various algorithms, pattern techniques, and statistical techniques. In this paper, information is obtained through efficient decision tree and cluster analysis in predicting the incidence of percutaneous coronary intervention and mortality. In the cluster analysis, EM algorithm was used to evaluate the suitability of the algorithm for each situation based on performance tests and verification of results. In the cluster analysis, the experimental data were classified using the EM algorithm, and we evaluated which models are more effective in comparing functions. Using data mining technique, it was identified which areas had effective treatment techniques and which areas were vulnerable, and we can predict the frequency and mortality of percutaneous coronary intervention for heart disease.

• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.441-448
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• 2007

Heart rate variability (HRV) in electrocardiogram (ECG) is an important index for understanding the health status of heart and the autonomic nervous system. Most HRV analysis approaches are based on the proper heart rate (HR) data. Estimation of heart rate is thus a key process in the HRV study. In this paper, we report an innovative method to estimate the heart rate. This method is mainly based on the concept of periodicity transform (PT) and instantaneous period (IP) estimate. The method presented is accordingly called the "PT-IP method." It does not require ECG R-wave detection and thus possesses robust noise-immune capability. While the noise contamination, ECG time-varying morphology, and subjects' physiological variations make the R-wave detection a difficult task, this method can help us effectively estimate HR for medical research and clinical diagnosis. The results of estimating HR from empirical ECG data verify the efficacy and reliability of the proposed method.

• Annals of Clinical Neurophysiology
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• v.20 no.1
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• pp.1-2
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• 2018

• Journal of Sasang Constitutional Medicine
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• v.14 no.2
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• pp.125-131
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• 2002

An ischemic heart disease(IHD) is a anemic state of heart caused by disproportion between heart's demand and supply of oxygen. A patient who has this IHD feels serious chest pain called angina pectoris. In a keen condition it leads to a necrosis of heart muscles, known as myocardial infarction. In an ischemic heart disease the ECG waves gives us useful information of patients' heart. And CK(creatine kinase) in serum and Troponin T are the principal factors in diagnosis of IHD. In this study, the IHD patient classified by Sasang Constitutional Medicine had a notable medical effects. The symptoms of patient are disappeared and waves of ECG is closed to normal. The result of CK in serum is also recovered. So we report the healing process and results of this patient in this study.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.290-297
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• 2024

We investigate the efficacy of ensemble learning methods, specifically the soft voting technique, for enhancing heart disease prediction accuracy. Our study uniquely combines Logistic Regression, SVM with RBF Kernel, and Random Forest models in a soft voting ensemble to improve predictive performance. We demonstrate that this approach outperforms individual models in diagnosing heart disease. Our research contributes to the field by applying a well-curated dataset with normalization and optimization techniques, conducting a comprehensive comparative analysis of different machine learning models, and showcasing the superior performance of the soft voting ensemble in medical diagnosis. This multifaceted approach allows us to provide a thorough evaluation of the soft voting ensemble's effectiveness in the context of heart disease prediction. We evaluate our models based on accuracy, precision, recall, F1 score, and Area Under the ROC Curve (AUC). Our results indicate that the soft voting ensemble technique achieves higher accuracy and robustness in heart disease prediction compared to individual classifiers. This study advances the application of machine learning in medical diagnostics, offering a novel approach to improve heart disease prediction. Our findings have significant implications for early detection and management of heart disease, potentially contributing to better patient outcomes and more efficient healthcare resource allocation.

• The Korean Journal of Medicine
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• v.93 no.6
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• pp.501-508
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• 2018

Obesity is a chronic disorder that is a significant risk factor for diabetes, cardiovascular diseases, malignancy, and other chronic diseases. Lifestyle modifications form the basis of most treatments for obesity, but it has become clear that such modifications alone are not enough for many obese patients. When a behavioral approach is insufficient, pharmacological treatment may be recommended. In recent years, the US Food and Drug Administration (FDA) has withdrawn several therapeutic options for obesity due to their side effects, but has approved four novel anti-obesity agents. Until recently, orlistat was the only drug approved for the management of long-term obesity, but the US FDA approved the novel anti-obesity drugs lorcaserin and phentermine/topiramate in 2012, and naltrexone/bupropion and liraglutide in 2014. The present review discusses the different pharmacotherapeutic options for the treatment of obesity.

• Clinics in Shoulder and Elbow
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• v.17 no.4
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• pp.197-200
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• 2014

Reports of osteomyelitis in the proximal humerus with pyogenic glenohumeral arthritis of adjacent joints mostly involve pediatric patients. Nowadays, osteomyelitis that is secondary to adjacent pyogenic glenohumeral arthritis is extremely rare, even more so in adults than in pediatrics. We report a rare case of the pyogenic glenohumeral arthritis followed by osteomyelitis of the proximal humerus in an elderly patient. Initially, we diagnosed a case of pyogenic glenohumeral arthritis only, which, despite arthroscopic synovectomy, did not resolve and severe pain continued. Subsequent radiological imaging, performed after our suspicion of a secondary involvement, allowed us to diagnose osteomyelitis combined with the pyogenic glenohumeral arthritis, which we had overlooked because of the extreme rarity of the condition in adults since the antibiotic era began.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.294-294
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• 2000

In this paper, we would like to discuss the signal processing and the algorithm for ECG analysis. The ECG gives us information about the condition of the heart muscle, because myocardial abnormality or infarction is inscribed on the ECG during myocardial depolarization and repolarization. Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. The wavelet transform decomposes the ECG signal into high and low frequency component using wavelet function. Recomposing high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the curve-fitting partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with some kinds of heart disease ECG pattern, we can detect and classify the kind of heart disease.