• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.131-135
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• 2018

The heart disease taking the second place of the cause of the death of modern people is a terrible disease that makes sudden death without noticing. To judge the aortic valve disease of heart diseases a name of disease was diagnosed using psychological data provided from physioNet. Aortic valve is a valve of the area that blood is spilled from left ventricle to aorta. Aortic stenosis of heart troubles is a disease when the valve does not open appropriately in contracting the left ventricle to aorta due to narrowed aortic valve. In this paper, 3126 samples of cardiac sound data were used as an experiment data composed of 180 characteristics including normal people and aortic valve stenosis patients. To diagnose normal and aortic valve stenosis patients, NEWFM was utilized. By using an average method of weight as an feature selection method of NEWFM, the result shows 91.0871% accuracy.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.175-182
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• 1995

Despite the simplicity of processing, a conventional autocorrelation function (ACF) method for the precise determination of fetal heart rate (FHR) has many problems. In case of weak or noise corrupted Doppler ultrasound signal, the ACF method is very sensitive to the threshold level and data window length. It is very troublesome to extract FHR when there is a data loss. To overcome these problems, the high resolution pitch detection algorithm was adopted to estimate the FHR. This method is more accurate, robust and reliable than the ACF method. With a lot of calculation, however, it is impossible to process real time FHR estimation. This paper is presented a new FHR estimation algorithm for real time processing and studied the real time FHR monitoring system by high resolution pitch detection algorithm.

• 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.

• Journal of Advanced Navigation Technology
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• v.27 no.1
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• pp.139-145
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• 2023

When a doctor examines a patient in a hospital, the doctor directly checks the patient's condition and conducts a face-to-face diagnosis through dialogue with the patient. However, it is often difficult for doctors to directly treat patients. Recently, several types of telemedicine systems have been developed. However, the systems have lack of capabilities to observe heart disease, neck condition, skin condition, inside ear condition, etc. To solve this problem, in this paper, an interactive telemedicine robot system with autonomous driving in a room capable of visual examination and auscultation of patients is developed. The developed robot can be controlled remotely through the WebRTC platform to move toward the patient and check a patient's condition under the doctor's observation using the multi-joint robot arm. The video information, audio information, patient's heart sound, and other data obtained remotely from patients can be transmitted to a doctor through the web RTC platform. The developed system can be applied to the various places where doctors are not possible to attend.