• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.149-157
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• 2011

In this paper, we propose a new cardiac disorder classification method using an support vector machine (SVM) to combine hidden Markov model (HMM) and murmur existence information. Using cepstral features and the HMM Viterbi algorithm, we segment input heart sound signals into HMM states for each cardiac disorder model and compute log-likelihood (score) for every state in the model. To exploit the temporal position characteristics of murmur signals, we divide the input signals into two subbands and compute murmur probability of every subband of each frame, and obtain the murmur score for each state by using the state segmentation information obtained from the Viterbi algorithm. With an input vector containing the HMM state scores and the murmur scores for all cardiac disorder models, SVM finally decides the cardiac disorder category. In cardiac disorder classification experimental results, the proposed method shows the relatively improvement rate of 20.4 % compared to the HMM-based classifier with the conventional cepstral features.

• 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.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.252-255
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• 2016

The objective of this paper is to find an easier and non-invasive a way of diagnosing heart diseases based on the heart sound, rigidly heart murmurs, recordings from subjects. Although most of the heart sounds can be easily heard, analysis of the findings by auscultation strongly depends on skills and experience of the physician. Therefore, the heart murmur is require quantitative analysis for automatic diagnosis equipment. For a good sound analysis, the noisy component ware filtered. This can be done using Wiener filter. Once the signal is filtered, it can be segmented into its basic components by signal energy using FFT. After segment the heart sound signal, the relative positions of the different heart sound components will be identified and will be used for quantification purposes. We are using murmur energy ratio. The experimental results are fairly good in relation to automatic diagnosis.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.246-248
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• 2012

심전도는 측정 기기에서 발생하는 전기적 잡음이나 근육에서 발생하는 근전도에 의한 잡음, 전극을 부착한 사람의 움직임에 의한 동잡음 등에 민감한 특성을 보인다. 또한 심장의 이상으로 인하여 왜곡이 심하게 발생하므로 심전도에서 의미 있는 구간을 검출하기 위해서는 이들을 보완하는 알고리즘이 필수적이라 할 수 있다. 논문에서는 심전도 분석의 보조를 위하여 입력된 심전도가 가지는 잡음과 왜곡을 제거하고 구간의 위치를 출력하는 시스템을 제안한다. 이를 위해 관련 알고리즘 중, 가장 널리 알려진 'Pan & tompkins algorithm'을 시스템에 이식하였고 알고리즘의 각 단계를 알아보기 쉽게 출력하는 인터페이스를 구성하였다. 시스템의 기능을 확인하기 위해 MIT/BIH 데이터베이스를 이용하였으며, 잡음과 왜곡이 심하여 육안으로 구간을 확인하기 힘든 심전도에서도 높은 구간검출 정확도를 확인할 수 있었다.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.287-292
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• 2003

Even though MCG has many advantages over ECG, MCG signa)s are easily corrupted by external magnetic noises Since multi-channel MCG signals are recorded simultaneously at many spatial positions, it is effective to apply spatial fitters as well as the conventional temporal filters to remove external magnetic noises. The spatial filters can be designed by utilizing the fact that the noise signals caused by external noise sources are more spatially correlated than the original MCG signals. In this paper, we introduce a spatial filtering method for the noise reduction in MCG based on the principal component analysis. Healthy volunteer study results obtained with a 61-channel MCG system are presented.

• 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 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 KIEE Conference
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• 2004.07d
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• pp.2685-2686
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• 2004

심자도(Magnetocardiography: MCG)는 심장에서 발생하는 자기신호로 크기가 수 pico Tesla에서 수 femto Tesla 정도로 지구 자기에 비하여 $10^{-6}{\sim}10^{-10}$ 정도로 매우 작기 때문에 보통 3층의 차폐 막 구조로 되어 있는 자기차폐실을 사용하여 외부 잡음을 줄인다. 그러나 자기차폐실의 비용이 크기 때문에, 자기차폐실의 비용을 줄이고 다양한 신호처리를 병행하여 신호대 잡음비를 높이고 있다. 본 논문에서는 1Giga FLOPS (FLoating point Operationals Per Second)의 부동 소숫점 연산능력을 가진 TMS320C6701을 사용하여 실시간 신호처리가 가능한 신호처리 시스템을 설계하였다. 개발된 DSP 보드는 PCI-bus 기반으로 설계하여 신호 측정 컴퓨터에 내장이 가능하도록 하였다. 프로그램과 데이터 처리를 위한 외부 메모리를 장착하였고, PCI 콘트롤러를 갖추어 PC 와의 대용량 메모리 공유가 가능하도록 하였다. 제작된 DSP 보드를 사용하여, 심자도 신호에서 실시간으로 적응 잡음 소거 및 필터링을 구현하여 신호대 잡음비의 향상을 확인할 수 있었다.