• ETRI Journal
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• v.37 no.4
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• pp.824-832
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• 2015

Heart sounds are the main obstacle in lung sound analysis. To tackle this obstacle, we propose a diagnosis algorithm that uses singular spectrum analysis (SSA) and frequency features of heart and lung sounds. In particular, we introduce a frequency coefficient that shows the frequency difference between heart and lung sounds. The proposed algorithm is applied to a synthetic mixture of heart and lung sounds. The results show that heart sounds can be extracted successfully and localizations for the first and second heart sounds are remarkably performed. An error analysis of the localization results shows that the proposed algorithm has fewer errors compared to the SSA method, which is one of the most powerful methods in the localization of heart sounds. The presented algorithm is also applied in the cases of recorded respiratory sounds from the chest walls of five healthy subjects. The efficiency of the algorithm in extracting heart sounds from the recorded breathing sounds is verified with power spectral density evaluations and listening. Most studies have used only normal respiratory sounds, whereas we additionally use abnormal breathing sounds to validate the strength of our achievements.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2051-2052
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• 2011

The structural defects of a heart often reflects the sounds that the heart produces. This paper describes heart sound analysis method using Wavelet transform and average Shannon energy. This can extract the features of heart sounds in various disease identify the heart sounds. Experimental results show that the presented method has potential application in detecting various heart diseases.

• 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 Korea Multimedia Society
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• v.24 no.11
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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.

• 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.

• 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 KOSOMBE Conference
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• v.1997 no.05
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• pp.402-405
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• 1997

The analysis of heart sounds is a noninvasive diagnostic method useful to diagnose heart valve function. In this paper we compared the ability of spectral analysis method for prosthetic heart valve sounds. Phonocardiograms of prosthetic heart valve were analyzed in order to derive frequency domain feature suitable for the classification of the valve state. The FFT-based methods did not provide sufficient frequency resolution to completely characterize the spectrum of prosthetic heart valve sounds. A high resolution parametric methods were shown to give superior frequency resolution. In parametric methods, all methods provide a 1st & 2nd & 3rd frequency component. But Shank method provided a most dominant frequency peak.

• Journal of Korea Multimedia Society
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• v.25 no.9
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• pp.1251-1256
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• 2022

Auscultation of heart sounds using a stethoscope is the basic method to diagnose the cardiovascular disease and observation of abnormalities. However, the heart sound transmitted to the ear through the stethoscope is greatly affected by internal sounds such as organ movement or breathing. In addition, the user's experience significantly influences the accuracy of the auscultation result. Therefore, in this paper, we developed a wearable device that simultaneously measures heart sound and PPG signals for cardiac condition monitoring. The structure of the proposed device is designed to simultaneously measure heart sound and PPG signals when worn on a finger and placed on the chest. A prototype was implemented according to the design structure, and it was confirmed that the performance of measurements and collection for physiological signals was excellent through experiments.

• Journal of Biomedical Engineering Research
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• v.20 no.6
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• pp.573-581
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• 1999

Although phonocardiography by auscultation has been used in diagnosis long time ago, recognition of heart sound was tried only restricted fields such as the first heart sound, the second heart sound, and specific valve operation for the purpose of analyzing local function or operation of heart and developments of heart sound recognition in full cycle are quite insignificant. in this paper, we proposed a recognition method which extracts features of heart sound in full cycle and classllies heart sounds This proposed recognition algorithm is based on detecting the first and second heart sounds in thme domain. The algorithm classifics heart sound into several classes by extracting the important time blocks and analyzing the peak position, integration values and statistical variables. Heart sounds are classified into normal, early systolic murmur, late systolic mumur, early diastolic murmur, late diastolie murmur, continuous murmur. We can verify our algorithm is useful from the results which show the average recognition rate of heart sounds is 88 perecnt. Recognition error was occurred mainly in early systolic murmur.

• Journal of Korea Multimedia Society
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• v.15 no.11
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• pp.1330-1340
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• 2012

In this paper, a new algorithm is proposed for the heart valves stenosis region detection using heart sounds. Many researches for detecting primary components or removing heart murmurs have been studied, but their performances are degraded at abnormal heart sounds such as aortic stenosis and mitral stenosis because of large heart murmurs. In this paper, heart murmur detection method is proposed based on noise intensity function. The proposed noise intensity function detect the primary components S1, S2, then set session up using S1, S2. And then noise intensity function was computed using autocorrelation value of each session. The proposed noise intensity function estimated noise intensity of each sessions and detected heart murmurs. According to simulation results, the proposed algorithm has better performance than former study for detecting heart valve stenosis region.