• Proceedings of the IEEK Conference
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• 2000.07b
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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.

• The Journal of the Acoustical Society of Korea
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• v.27 no.2E
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• pp.57-63
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• 2008

Stethoscopic auscultation is still one of the primary tools for the diagnosis of heart diseases due to its easy accessibility and relatively low cost. It is, however, a difficult skill to acquire. Many research efforts have been done on the automatic classification of heart sound signals to support clinicians in heart sound diagnosis. Recently, hidden Markov models (HMMs) have been used quite successfully in the automatic classification of the heart sound signal. However, in the classification using HMMs, there are so many heart sound signal types that it is not reasonable to assign a new class to each of them. In this paper, rather than constructing an HMM for each signal type, we propose to build an HMM for a set of acoustically-similar signal types. To define the classes, we use the KL (Kullback-Leibler) distance between different signal types to determine if they should belong to the same class. From the classification experiments on the heart sound data consisting of 25 different types of signals, the proposed method proved to be quite efficient in determining the optimal set of classes. Also we found that the class determination approach produced better results than the heuristic class assignment method.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2156-2159
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• 2003

Thoracic sound has been widely known as a good method to examine thoracic disease. But, it's difficult to diagnose with correct data according to patient's thoracic position from same patient who has thoracic disease. Therefore, it is necessary to normalize the data for lung sound objectively In this paper, we'd like to detect a useful data for medical examination by applying PCA(Principal Component Analysis) to thoracic sound data and then present a objective data about lung and heart sound for thoracic disease.

• 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 Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.753-758
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• 2009

This paper is proposed the hardware structure and signal processing method of the RF-Tag based on heart sound to develop the mobile biomedical information device for the u-healthcare system. The RF-Tag in this study is consisted of a skin temperature sensor, a dynamic microphone for heart sound detection, Bluetooth communication to transmute healthcare data, and pulse period detection algorithm with adaptive gain controller. We experimented to evaluate performance of the RF-Tag in noisy environments. In addition, the RF-Tag has shown the good performance in the results of experiment. If the proposed methods in this study apply to design the u-healthcare device, we will be able to get the exact health data on real time in mobile environments.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.75-78
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• 1997

In this paper we present new sound spectral analysis methods or prosthetic heart valve sounds. Phonocardiograms(PCG) of prosthetic heart valve were analyzed in order to derive frequency domain feature suitable or the classification of the valve state. The fast orthogonal search method and MUSIC (MUltiple SIgnal Classification) method are described or finding the significant frequencies in PCG. The fast orthogonal search method is effective with short data records and cope with noisy, missing and unequally-spaced data. MUSIC method's key to the performance is the division of the information in the autocorrelation matrix or the data matrix into two vector subspaces, one a signal subspace and the other a noise subspace.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8B
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• pp.830-838
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• 2009

Oriental medicine lacks diagnosis data in fixed quantity possible to express visually to patients by depending on clinician's intuition than Western medicine that continues to development by various diagnosis devices. For that, this paper intends to examine relation between heart and voice signal regarded as center organ and source of life and mind in order to implement objectification through the visualization of oriental diagnosis method above all. According to because the heart is related to the tongue among five organs, by thinking with sounds, we would design the way of identifying existence of heart diseases focused on the fact that lingual sound pronunciation of heart patient is inexact. For this, we achieved a comparison, analysis of statistical bandwidth and morphological modeling of the second formants frequency about a lingual sound for their voice constituted subject group of heart diseases and normal people. Finally, we analyzed interrelationship to the result of experiment by designed method.

• 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.45 no.3
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• pp.109-117
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• 2024

We developed a device that integrates digital otoscope and stethoscope for telemedicine. The integrated device was utilized for the collection of tympanic membrane images and cardiac auscultation data. Data accumulated on the platform server can support real-time diagnosis of heart and eardrum diseases using artificial intelligence. Public data from Kaggle were used for deep learning. After comparing with various deep learning models, the MobileNetV2 model showed superior performance in analyzing tympanic membrane data, and the VGG16 model excelled in analyzing cardiac data. The classification algorithm achieved an accuracy of 89.9% for eardrums data and 100% for heart sound data. These results demonstrate the possibility of diagnosing diseases without the limitations of time and space by using this platform.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2708-2714
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• 2009

Due to poor linguistic communication skills of sucklings and infants, crying mostly is only means of communication to express their body conditions and desires. We, therefore, developed an infant auscultation system which detects which part of the body has a pathological problem, by analysing infant's crying sound patterns. Specifically, in this paper, we accomplished an auscultation system for congenital heart diseases detection by performing pitch, intensity and spectrum analysis of the crying sounds between the normal infants group and the congenital heart diseases group. With this system, we can diagnose congenital heart diseases of infants with poor communication capacity, and, in the near future, can build a home care diagnosis system based on crying sound analysis technologies through additional experiments on medical data.