• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1139-1143
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• 2007

For the possible application of monitoring or diagnosing heart sounds in an ubiquitous healthcare environment. a small and light electro-stethoscope that can be attached in human body should be exploited. With this aim, this study proposes a new style of electro-stethoscope device that is composed of four hardware modules in wearable style. For this ambulatory heart sound collecting device, the several tests must be performed to check portability and material capability for collecting heart sounds. It turned out to be that the multi-channel electro-stethoscope can detect heart sound signals well even if it is not pinpointed in the accurate stethoscope point on a heart. Consequently, our ambulatory electro-stethoscope hardware system can be applied to monitor or diagnose abnormal heart sounds in the ubiquitous healthcare system.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.101-102
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• 2007

Heart diseases are critical and should be detected as soon as possible. A stethoscope is a simple device to find cardiac disorder but requires keen experiences in heart sounds. We evaluate a cardiac disorder classifier by using heart sounds recorded by a digital wireless stethoscope developed in this work. The classifier uses hidden Markov models with circular state transition to model the heart sounds. We train the classifier using two kinds of data: One recorded by using our stethoscope and the other sampled from a clean heart sound database. In classification experiments using 165 sound clips, the classifier shows the classification accuracy of 82% in classifying 6 cardiac disorder categories.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.108-112
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• 2001

Heart sound contains rich information regarding the dynamics of the heart and the auscultation has been a first choice of routine procedures for diagnosis of the heart. However, heart sounds captured using a conventional stethoscope are not often loud or clear enough for doctors to precisely classify their characteristics, especially, under the noisy environments of the hospital. A simple auscultation device that removed shortcomings of the conventional stethoscope was constructed in the study. The device employed a polymer based adherent differential output sensor which was on contact with skin through a coupling medium and appropriated electronic circuits for signal amplification and conditioning An ordinary headphone is taken to hear the captured heart sounds and the volume can be adjusted to hear well. It is also possible that the device sends the captured heart sound signals to a PC where the signals are further processed and viualized.

• 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 Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.185-188
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• 2000

Heart sound contains rich information regarding the dynamics of the heart and the auscultation has been a first choice of routine procedures for diagnosis of the heart. However, heart sounds captured using a conventional stethoscope are not often loud or clear enough for doctors to precisely classify their characteristics, especially, under the noisy environments of the hospital. A simple auscultation device that removed shortcomings of the conventional stethoscope was constructed in the study. The device employed a polymer based adherent differential output sensor which was on contact with skin through a coupling medium and appropriated electronic circuits for signal amplification and conditioning. An ordinary headphone is taken to hear the captured heart sounds and the volume can be adjusted to hear well. It is also possible that the device sends the captured heart sound signals to a PC where the signals are further processed and viualized.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3001-3008
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• 2013

To diagnose the cardiac valve abnormalities using analysis of phonocardiogram, first of all, accurate detection of S1, S2 components is needed for heart sound segmentation. In this paper, a new method that uses the third moment characteristics of an envelope of the PCG is proposed for accurate detection of S1 and S2 components of the heart sound with cardiac murmurs. The envelope of the PCG is obtained from the short-time energy profile, and its third moment profile with slope information is used for accurate time gating of the S1, S2 components. Experimental results have shown that the proposed method is superior to the conventional second moment method for detection of S1 and S2 regions from the heart sound signals with cardiac murmurs.

• 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.

• Journal of Chest Surgery
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• v.22 no.4
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• pp.589-593
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• 1989

The frequency spectrum of the metallic closing sound and its loudness were measured by non invasive techniques in 66 patients. They had examined a total of 7 Carbomedics valve, 10 Duromedics valve, 11 St. Jude heart valve in mitral position and 8 Carbomedics, 10 Duromedics, 20 St. Jude heart valve prostheses functioning normally in aortic position. Statistical comparison of the loudness from sound produced by the three valves in each position, the following; The Carbomedics valve has the lowest average loudness, followed by the St. Jude medical valve, and finally the Edward Duromedics valve. And we analysis the changing factor of the loudness of valve sound, only the velocity of the flow through the valve influenced to the valve sound.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2486-2488
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• 1998

In this paper, Prameters esimated t using AR model in order to approach linearly the heart sound which include the nonlinear characteristic from the characteristics based on a statistical theory. The parameters which is figured out using AR model is a very important information which show the characteristic heart sound In this paper parameters estimated using autocorrelation method and order selected by proposed Akaike[6] method. Compared the similirities of the spectrums between estimated by using AR model and estimated by using FFT method.