• Journal of the Institute of Convergence Signal Processing
• /
• v.16 no.4
• /
• pp.134-138
• /
• 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.

• Journal of Korea Multimedia Society
• /
• v.15 no.11
• /
• pp.1330-1340
• /
• 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 Korea Multimedia Society
• /
• v.19 no.3
• /
• pp.557-566
• /
• 2016

Heart sounds generated by the beating heart and blood flow reflect the turbulence created when the heart valves snap shut. Cardiac diagnosis is typically started by an auscultation using a stethoscope, from which a medical doctor, depending on his hearing capabilities and training, listens and interprets the acoustic signal. This method of diagnostic is uncertain, mostly due to the fact that human ear loses the acoustic frequency sensitivity through the years. Even though an auscultation has some weaknesses like uncertainty, it is considered as a primary tool due to its simplicity. In this paper, heart murmur detection algorithm is proposed using time and frequency characteristics of heart sound. The propose heart murmur detection method adapted conventional primary heart sound detection method in time domain and modified spectral flatness method in frequency domain for detecting heart murmurs. From experimental results, it is confirmed that the proposed algorithm detect the heart murmurs efficiently.

• Journal of Korea Multimedia Society
• /
• v.14 no.7
• /
• pp.884-894
• /
• 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 Korea Institute of Information and Communication Engineering
• /
• v.17 no.12
• /
• pp.3001-3008
• /
• 2013

To diagnose the cardiac valve abnormalities using analysis of phonocardiogram, first of all, accurate detection of S1, S2 components is needed for heart sound segmentation. In this paper, a new method that uses the third moment characteristics of an envelope of the PCG is proposed for accurate detection of S1 and S2 components of the heart sound with cardiac murmurs. The envelope of the PCG is obtained from the short-time energy profile, and its third moment profile with slope information is used for accurate time gating of the S1, S2 components. Experimental results have shown that the proposed method is superior to the conventional second moment method for detection of S1 and S2 regions from the heart sound signals with cardiac murmurs.

• Proceedings of the IEEK Conference
• /
• 2000.07b
• /
• pp.1045-1048
• /
• 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.

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

• Korean Journal of Veterinary Research
• /
• v.47 no.1
• /
• pp.99-102
• /
• 2007

A 3-year-old, intact female, Shih-tzu dog was presented with a 15-day history of vomiting,depression, and anorexia. On physical examination, systolic ejection murmurs with precordial thril atthe left heart base were detected. A diagnosis of congenital pulmonic stenosis (PS) was made mainlyfrom the thoracic radiography, electrocardiography, and echocardiography. On complete blood counts andconfirmed that main pulmonary artery was tremendously buldged and electrocardiography was suggestiveof severe right ventricular hypertrophy. Echocardiographic findings revealed the pulmonic valve stenosiscontaining valvular dysplasia and poststenotic dilation. On Doppler echocardiography, ejection velocityof the lesion accounted for 3.38 m/sec, meaning mild velocity through the stenotic area. The dog'sproblem and resulted in death. However, there has been no reliable relation between PS and CRF. Primarymalformation of pulmonic valve was confirmed at necropsy after death.

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

• Journal of Chest Surgery
• /
• v.18 no.2
• /
• pp.165-173
• /
• 1985

A clinical analysis was performed n 706 uses of patent ductus arteriosus experienced at Department of Thoracic and Cardiovascular Surgery, Seoul National University Hospital during 27 years period from 1958 to 1984. Of the 706 patients of PDA, 244 patients were male and 462 patients were female and ages ranged from 2 months to 53 years old with the average age of 8.5 years. The chief complaints on admission were dyspnea on exertion and frequent URI in 58.9%, non specific symptoms such as palpitation and easy fatigability in 9.7%, symptoms of CHF in 2.0% and no subjective symptoms in 29.4%. On auscultation of heart, continuous machinery murmurs were heard in 82% and only systolic murmurs were heard in 18% of patients. On simple chest PA of patients, cardiomegalies were detected in 78% and there were increased pulmonary vascularities in 93% of patients. EKG findings were as followed; LVH 56.9%, BVH 12.6%, RVH 2.9% and WNL 27.6%. Cardiac Catheterizations were performed in 512 patients and mean Qp/Qs was 2.56 and mean systolic pulmonary artery pressure was 45mmHg. Operation methods were as followed; in patients in whom operations were performed on PDA only, ligation 94.3%, division 3.7% and ligation [0.5%] or trans-pulmonary artery suture closure [1.5%] under cardiopulmonary bypass 2.0% and in patients in whom operations were performed with associated anomalies, ligation 17.6%, division 2.4%, and ligation [44.7%] or trans-pulmonary artery suture closure [35.3%] under cardiopulmonary bypass 80%. 52 postoperative complications [8.4%] were developed in 42 patients [6.8%] and its were as followed; permanent or transient hoarseness 16 [2.6%], intraoperative rupture of PDA 8 [1.3%], recannalization 6 [1.[%], operative death 5 [0.8%], late death 4 [0.6%] and other miscellaneous complications 13 [2.1%]. 140 associated cardiac anomalies [19.8%] were found in 105 patients [14.9%] and its were as followed; VSD 68 [9.6%], COA 15 [2.1%], Subaortic discrete membrane 7 [0.9%], ASD 6 [0.8%], TOF 5 [0.7%] and other miscellaneous and