• 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.

• 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.

• The Journal of the Acoustical Society of Korea
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• v.28 no.1
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• pp.32-43
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• 2009

In this paper, we improve the performance of cardiac disorder classification by continuous heart sound signals using automatic segmentation and extreme learning machine (ELM). The accuracy of the conventional cardiac disorder classification systems degrades because murmurs and click sounds contained in the abnormal heart sound signals cause incorrect or missing starting points of the first (S1) and the second heart pulses (S2) in the automatic segmentation stage, In order to reduce the performance degradation due to segmentation errors, we find the positions of the S1 and S2 pulses, modify them using the time difference of S1 or S2, and extract a single period of heart sound signals. We then obtain a feature vector consisting of the mel-scaled filter bank energy coefficients and the envelope of uniform-sized sub-segments from the single-period heart sound signals. To classify the heart disorders, we use ELM with a single hidden layer. In cardiac disorder classification experiments with 9 cardiac disorder categories, the proposed method shows the classification accuracy of 81.6% and achieves the highest classification accuracy among ELM, multi-layer perceptron (MLP), support vector machine (SVM), and hidden Markov model (HMM).