• 대한한의학원전학회지
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• 제22권3호
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• pp.347-352
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• 2009

In the "Somun(素問) Youngranbijeonron(靈蘭秘典論)", that describes the lung as "the office of assisting Heart, the administration come out Lung(相傅之官, 治節出焉)". The means of "the office of assisting Heart" is that Lung assist Heart and execute the Heart's order. The administration come out Lung has two means. The first, Lung administrates and controls the body. The second, Lung controls the Gi and blood(氣血). In the "Somun(素問) Gyeongmaekboulron(經脈別論)", that describes the creation of pectrol Gi(宗氣). The Essence derived from food(穀氣) digested in Stomach comes to Heart, and mixed Lung's Gi of respiration(呼吸之氣), than becomes a pectrol Gi(宗氣). The pectrol Gi(宗氣) controls the Gi and blood(氣血), and we can say that function is the administration come out Lung.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 추계학술대회
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• pp.326-330
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• 2012

2011년 심장질환으로 사망하는 인구수가 약 25,000명에 이른다. 본 논문은 심장마비환자의 빠른 응급구제를 위해 심장마비의 발생을 감지하고 응급상황을 전파하는 시스템을 개발한다. 심장마비를 감지하기 위해 맥박센서가 부착된 wearable computer를 제작한다. 측정된 맥박은 블루투스 무선통신으로 스마트폰으로 전송된다. 스마트폰에서 입력받은 맥박을 분석하여 상황판단을 하고 비상알람, SNS(Social Network Service), SMS(Short Message Service)를 활용하여 상황전파를 한다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.533-536
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• 1997

In this paper, we visualized 3-dimensional volume of heart using volume method by thresholding in EBT slices data. Volume rendering is the method that acquire the color by casting a pixel ray to volume data. The gray level of heart region is so high that we decide heart region by thresholding method. When a pixel ray is cast to volume data, the region that is higher than threshold value becomes heart region. We effectively rendered the heart volume and showed the 3-dimensional heart volume.

• Journal of Chest Surgery
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• 제12권3호
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• pp.263-268
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• 1979

In the course of treating approximately 740 patients with open heart surgery, we experienced 38 patients who underwent open heart surgery as the second operation after initial operation on heart at Seoul National University Hospital. Twenty four cases of congenital 14 acquired heart disease were found. There was 14 operative death of 38 patients, resulting in overall mortality 36.8 %; 8 death [33.3 %] in congenital group, 6 [42.9 %] in acquired group. Principal causes of death were lower cardiac output syndrome and congestive heart failure. Compared with foreign report, as this data shows still high overall mortality, this review suggests that second open heart surgery can be performed safely with reasonable operative mortality and satisfactory prognostic outlook in the near future.

• 한국한의학연구원논문집
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• 제10권2호
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• pp.109-119
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• 2004

The aim of this study is to confirm clinical usefulness of MCG data by analyzing korea medical results of heart disease patients. We used the Heart Disease Questionnaire which asks for Qi deficiency-pattern, Blood deficiency-pattern, Yin deficiency-pattern, Yang deficiency-pattern, Qi stasis-pattern, Blood stasis-pattern, Heart heat-pattern, Phlegm-pattern. Magnetocardiography(MCG) is the measurement of magnetic fields emitted by the human heart from small currents by electrically active cells of the heart muscle. Comparing the MCG results and korea medical diagnosis, we showed clinical usefulness of MCG results and korea medical diagnosis.

• 한국정보전자통신기술학회논문지
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• 제7권1호
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• pp.50-54
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• 2014

본 연구에서는 혈압측정으로부터 얻어지는 오실레이션(Oscillation) 신호를 이용하여 심박주기를 검출하고, 심박주기의 정확성을 개선하는 알고리듬을 개발하였다. 이를 위한 혈압측정의 방법으로 오실로메트릭(Oscillometric) 측정법을 사용하였으며, 오실레이션 신호의 피크들로부터 전 후 기울기들의 평균이 교차하는 지점을 실제 피크로 인정하고 심박주기를 계산하였다. 제안된 방법은 그래프 상에서 심박주기의 극심한 편차와 오류를 줄이는 성능을 나타냈다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.1045-1048
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• 2000

This paper presents the application of the wavelet transform analysis and the neural network method to the phonocardiogram (PCG) signal. Heart sound is a acoustic signal generated by cardiac valves, myocardium and blood flow and is a very complex and nonstationary signal composed of many source. Heart sound can be discriminated normal heart sound and heart murmur. Murmurs have broader frequency bandwidth than the normal ones and can occur at random position of cardiac cycle. In this paper, we classified the group of heart sound as normal heart sound(NO), pre-systolic murmur(PS), early systolic murmur(ES), late systolic murmur(LS), early diastolic murmur(ED). And we used the wavelet transform to shorten artifacts and strengthen the low level signal. The ANN system was trained and tested with the back- propagation algorithm from a large data set of examples-normal and abnormal signals classified by expert. The best ANN configuration occurred with 15 hidden layer neurons. We can get the accuracy of 85.6% by using the proposed algorithm.

• 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.

• Journal of Chest Surgery
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• 제52권1호
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• pp.55-57
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• 2019

Robotically assisted mitral valve repair has proven its efficacy during the last decade. The most suitable approach for patients with difficult anatomies, such as morbid obesity, sternal deformities, cardiac rotation, or vascular anomalies, represents a current challenge in cardiac surgery. Herein, we present the case of a 71-year-old patient affected by severe degenerative mitral valve regurgitation with pectus excavatum and a right aortic arch with an anomalous course of the left subclavian artery who was successfully treated using a Da Vinci-assisted approach.

• 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.