• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.959-962
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• 2000

A heart sound algorithm, which separates the heart sound signal into four parts; the first heart sound, the systolic period, the second heart sound, and the diastolic period has been developed. The algorithm uses discrete intensity envelopes of approximations of the wavelet transform analysis method to the phonocard-iogram(PCG)signal. Heart sound a highly nonstation-ary signal, so in the analysis of heart sound, it is important to study the frequency and time information. Further more, Wavelet Transform provides more features and characteristics of the PCG signal that will help physician to obtain qualitative and quantitative measurements of the heart sound.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2E
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• pp.72-78
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• 2009

Heart sound segmentation into its components, S1, systole, S2 and diastole is the first step of analysis and the most important part in the automatic diagnosis of heart sounds. Conventionally, the Shannon energy envelope peak detection method has been popularly used due to its superior performance in locating S1 and S2. Recently, the HMM has been shown to be quite suitable in modeling the heart sound signal and its use in segmenting the heart sound signal has been suggested with some success. In this paper, we compared the two methods for heart sound segmentation using a common database. Experimental tests carried out on the 4 different types of heart sound signals showed that the segmentation accuracy relative to the manual segmentation was 97.4% in the HMM based method which was larger than 91.5% in the peak detection method.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.47-50
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• 2021

Human hearing sensitivity is frequency-dependent. The sensitivity is low at both ends of the audible frequency, and the sensitivity is the highest in the middle band at 3000 Hz. The heart sound of a healthy person is concentrated at a low frequency of 200 Hz or less, and despite using a stethoscope, the hearing sensitivity of the human body is low, and the stethoscope sound is low. Amplifying the sound of the stethoscope is not effective in distinguishing heart sounds in noisy environments because it maintains the same signal-to-noise ratio. In this study, a method of enhancing auditory stimulation was developed by applying a method of moving the spectrum of auscultation sounds into a high-frequency region where the human body is highly sensitive to hearing. The spectrum of the auscultation sound was moved up by 500 Hz in the frequency domain, and an inverse fast Fourier transform (FFT) was performed to reconstruct the auscultation sound. The heart sounds reconstructed by moving the spectra were divided into the first heart and second heart sound components, as in the original heart sound, and it was confirmed that the intensity was large in the cochleagram representing auditory stimulation. Therefore, this study suggested that spectral shift is a method to enhance auditory stimulation during auscultation without increasing the intensity of the auscultation sound.

• 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.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.5
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• pp.59-69
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• 2001

Recently many researches concerning heart sound analysis are being processed with development of digital signal processing and electronic components. But there are few researches about recognition of heart sound, especially full cardiac cycled heart sound, In this paper, a new recognition methods about. full cardiac cycled heart sound was proposed. For the first, the database was built by principal components analysis on training heart sound set. This database is used to recognize new input of heart sound, Ilear sounds were classified into seven classes such as normal(NO) class, pre-systolic murmurr(PS) class, early systolic murmur(ES) class, late systolic murmurr(LS) class, early diastolic murmur(EI) class, late diastolic murmur(LD) class and continuous murmuru(CM) class. As a result, we could verify that our new method has better efficiencies for the recognition the characteristics of heart sound than any precedent research. The maximum recognition rates of the new method are 71% for NO, 80% for PS and ES, 78% for LS, 87% for ED, 60% for LD and 20% for CM. Although the present results aren't practically sufficient to use our new method in recognizing heart sound, the importance of this paper is for recognition of heart sound within full cardiac cycle. We can get a better result by building a more efficient database.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.144-152
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• 2007

Conventional research works on the classification of the heart sound signal have been done mainly with the artificial neural networks. But the analysis results on the statistical characteristic of the heart sound signal have shown that the HMM is suitable for modeling the heart sound signal. In this paper, we model the various heart sound signals representing different heart diseases with the HMM and find that the classification rate is much affected by the clustering of the heart sound signal. Also, the heart sound signal acquired in real environments is a continuous signal without any specified starting and ending points of time. Hence, for the classification based on the HMM, the continuous cyclic heart sound signal needs to be manually segmented to obtain isolated cycles of the signal. As the manual segmentation will incur the errors in the segmentation and will not be adequate for real time processing, we propose a variant of the ergodic HMM which does not need segmentation procedures. Simulation results show that the proposed method successfully classifies continuous heart sounds with high accuracy.

• 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.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.10
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• pp.1444-1450
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• 2013

Esophageal stethoscope is used for monitoring the heart sounds and breath sounds of patients during surgery under a general anesthesia. Recently, an electronic esophageal stethoscope (EES)[1] has been developed for the purpose of real-time monitoring these information visually. This system uses only a microphone as the sound sensor. A drawback of the EES system is that it may be difficult to distinguish the first sound ($S_1$) and the second sound ($S_2$) of heart, because their periods are irregular depending on patients. In this paper, we propose an improved EES system in which the infrasound is measured by adding a pressure sensor as well as a sound sensor. We investigate some correlations between the infrasound and characteristics of the heart sound. The proposed system has been tested on 15 patients. The results show that the new system is capable of detecting the first sound more reliably and easily determining the heart rate and breathing period.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.884-894
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• 2011

We proposed a new algorithm for detection of first(S1) and second heart sound(S2). Many researches for detecting primary components and those algorithms have good performance at normal heart sound, but the performance is degraded at abnormal heart sound which is contain murmurs generated by heart disease. Therefore we proposed the S1, S2 detection algorithm using three-order Shannon energy difference. Using S1, S2's character which has large energy difference than murmurs, it is reduced noise and detected S1, S2. According to simulation results, not only normal heart sound but also abnormal heart sound, the proposed algorithm has better performance than former study at abnormal heart sound.