• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.2
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• pp.80-87
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• 1994

Fetal monitoring is a routine procedure to obtain a record of physiologic functions during pregnancy and labor. It is required to determine fetal heart frequency accurately. There are various types of fetal heart rate(FHR) determination and the most frequently applied method is transabdominal Doppler ultrasound. However, in the case of weak or noise corrupted Doppler ultrasound signals, conventional peak detections and the autocorrelation function method have many difficulties to determine FHR precisely. Also the autocorrelation function is effected by threshold level and window size. To solve these problems, the high resolution pitch determination algorinthm is introduced to detect FHR from Doppler ultrasound signals. This scheme digitally processes Doppler ultrasound signal for digital rectification, envelope detection, decimation and correlation calculation of two interconnected segments and then FHR is determined by its maximal value. Even in the case of a greatly smeared noise signal, this algorithm is able to search FHR more accurately than autocorrelation function by means of compensating FHR with a constant correlation threshold. This algorithm is simulated by 386-MATLAB on PC 486/DX and verified that it is superior to the autocorrelation function method.

• Journal of the korean veterinary medical association
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• v.24 no.1
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• pp.19-24
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• 1988

A lactating Holstein cow which did not have had responded to drug therapy during 2 months was clinically examined. Right atrial vegetative endocarditis was determined dy ultrasonographic diagnosis, and later it was confirmed at necropsy. An early systolic

• Journal of Biomedical Engineering Research
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• v.12 no.3
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• pp.209-214
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• 1991

Digital fetal monitoring system based on the personal computer combined with the digital signal processing (DSP) board was implemented. The DSP board acquires and digitally processes ultra- sound fetal Doppler signal for digital signal conditioning, rectification, low -pass filtering, autocorrealtion function calculation and its peak detection. The personal computer interfaced with the DSP board is in charge of graphic display, hardcopy, data transmission and on -line analysis of fetal heart rate change including on - line warning system, base -line estmation, acceleration, deceleration and variability. It is one of the most suitable situation to apply the DSP chip for siganl conditioning, digital filtering of ultrasound fetal Dopier signal and fetal heart rate estimation using autocorrelation technique .

• Proceedings of the KOSOMBE Conference
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• v.1993 no.05
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• pp.81-85
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• 1993

Despite the simplicity of processing, conventional autocorrelation function (ACF) method for the precise determination of fetal heart rate (FHR) has many problems. In the case of weak or noise corrupted Doppler ultrasound singnals, the ACF method is very sensitive to the threshold level and data window length. It is real troublesome to extract FHR when there is a data loss. To overcome these problems, the high resolution pitch detection algorithm is adapted to estimate the FHR. The FHR is determined from the correlation of two interconnected segments by its maximum correlation value. FHR is compensated with a constant correlation threshold in a greatly smeared noise signal. This method yields more accurate, robust and reliable than the ACF method.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3253-3255
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• 1999

출생시 국내에서 영아 사망률은 약 1%에 이르고 태아의 질병발생과 사망은 계속적으로 일어나고 있으므로 저가의 태아감시기술의 개발이 절실하다. 이를 위하여 본 연구는 임산부의 복부로부터 태아의 움직임과 심음을 검출하는 증폭기의 개발을 목표로 한다. 검출된 신호는 듣거나 녹음할 수 있으며. A/D 변환할 경우 PC에서 태아의 심음을 분석할 수 있게 한다. 개발된 증폭기를 이용하여 잡음에 노출된 일반 대학병원 환경에서 30명의 임산부를 대상으로 임상실험을 수행한 결과, 저잡음 특성을 나타내고. 빠른 경우 22주에서도 태아의 심음을 검출할 수 있었고. 심음의 주기검출이 가능하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.4
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• pp.134-138
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• 2015

Heart sound is used for a basic clinical examination to check for abnormalities in the lungs and heart that can be heard with a stethoscope or phonocardiography. In this paper, we try to find an easier and non-invasive method to diagnose heart diseases using neural network classifier. The classifier has been developed for one normal heart sound and five murmurs by using Shannon entropy and conjugate scaled back propagation algorithm. The experimental results showed that the classification is possible with 1.63185e-6 of classification error.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.237-240
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• 2009

We suggested the portable measurement system that estimate heart-condition for heart disease patients and healthy. We used informations of ECG and PCG in this system. The informations of ECG and PCG obtained by using electrodes and microphone, respectively.

• The Journal of Dong Guk Oriental Medicine
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• v.6 no.1
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• pp.67-89
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• 1997

The heart takes the top position as the monarch of the physiological activity in five viscera and six bowels. Activity to think and ponder, or harmony of the function of viscera and bowels and passing smoothly of qi and blood and so on, these depend on the function of heart. So it is called the center of life activity. This thesis studied bibliographically the process of formation of the system of differention of syndromes. First, in the classify of deficiency syndrome, insufficiency of the Heart is classified deficiency of the Heart-yin and insufficiency of the Heart-yang. After it classified insufficiency of the Heart-qi, insufficiency of the Heart-yang, dificiency of the 'Heart-blood and deficiency of the Heart-yin. At lately it classified more subdivide into insufficiency of the Heart-qi, insufficiency of the Heart-yang, dificiency of the Heart-blood, deficiency of the Heart-yin. Deficiency of the Heart-qi yin, deficiency of the Heart-qi blood, deficiency of the Heart-yin yang and sudden exhaustion of the Heart-yang. Second, It were the most important that the phlegm, fire and heat in the classify of excess syndrome. It classified various differentiation of syndrome. In the beginning of a period, it only classified phlegm syndrome and heat syndrome, but recently it classified not only phlegm syndrome and heat syndrome but also phlegm-fire. Also, It classified importantly gradually Heart-blood stasis caused by deficiency of the Heart-qi and the Heart-yang. Variety and subdivision of classify of differentiation of syndrome seemed resault of study to prepare various disease. And that after demanded more and more positive study.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.5
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• pp.241-246
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• 2001

The conventional stethoscope can not store its stethoscopic sounds. Therefor a doctor diagnoses a patient with instantaneous stethoscopic sounds at that time, and he can not remember the state of the patient's stethoscopic sounds on the next. This prevent accurate and objective diagnosis. If the electronic stethoscope, which can store the stethoscopic sound, is developed, the auscultation will be greatly improved. This study describes an amplifier for electronic stethoscope system that can extract heart sounds of fetus as well as adult and alow us hear and record the sounds. Using the developed stethoscopic amplifier, clean heart sounds of fetus and adult can be heard in noisy environment, such as a consultation room of a university hospital, a laboratory of a university. Surprisingly, the heart sound of a 22-week fetus was heard through the developed electronic stethoscope. Pitch detection experiments using the detected heart sounds showed that the signal represents distinct periodicity. It can be expected that the developed electronic stethoscope can substitute for conventional stethoscopes and if proper analysis method for the stethoscopic signal is developed, a good electronic stethoscope system can be produced.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2173-2174
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• 2006

심음은 심장이 수축, 확장 시에 심장의 움직임과 혈류의 흐름에 의해 발생하는 음향이다. 심음은 여러 신호원으로 이루어져 있고, 매우 복잡하고 비고정적인 신호이다. 심장의 질환에 따라 심음의 소리는 다르게 나타난다. 심음을 구분하여 심장 질환의 유무를 판단하는 가장 기초적인 기준이 될 수 있다. 본 연구에서는 Support Vector Machine 기법을 이용하여 심음을 통한 심장 질환 판별 검출 알고리즘을 제안하였다. Support Vector Machine은 신경망의 한 종류이며 이진분류에서 좋은 성능을 보인다. 또한 Polynomial Radial Basis Function, Multi-Layer Perceptron Classifiers를 위한 대안적인 학습방법으로 사용된다. 이러한 특성을 사용하여 심음의 데이터들을 일정한 기준에 의하여 (+)데이터와 (-)데이터로 분리한 후, 각 데이터들을 학습시켜 최적의 데이터를 만든다. 이후 각 데이터들은 점층적인 추가 학습을 시킴으로써 적은 양의 학습 데이터만으로도 높은 분류 성능을 표현할 수 있다. 이 연구에서 제안된 SVM을 실제 심음 데이터에 적용한 실험에서 심장 질환의 유무 판별에 우수한 성능을 보임을 확인할 수 있을 것으로 판단된다.