• 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 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.19 no.3
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• pp.557-566
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• 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.

• 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.

• 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.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 Chest Surgery
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• v.22 no.4
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• pp.630-637
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• 1989

Author studied 30 cases of remained heart murmur patients after VSD repair. The age ranged from 7months to 27 years, and sex ratio was 29: 1 in male and female. Perimembranous trabecular type of VSD was the most common causes of remained murmur after operation 11 cases, and the next was subpulmonic type 9 cases. The VSD size between 1.1 and 2.0cm in diameter was the most common in 15 cases. The operative method frequently used was patch closure in 21 cases, and commonly used surgical approaching way was through right atrium. Mechanisms of origin of postoperative remained murmur was from TR 9 cases, PI 6 cases, PS 5 cases, remnant shunts 5 cases, pulmonary artery dilatation 2 cases, MR 2 cases, and subaortic stenosis 1 cases.

• Journal of Veterinary Clinics
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• v.18 no.4
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• pp.452-454
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• 2001

A two-month-old female Yorkshire terrier was referred to the Veterinary Teaching Hospital, College of Veterinary Medicine, Kyungpook national University. The patient was presented with a history of dyspnea, cough, exercise intolerance and abdominal distension, but she was appetence. In physical examination the puppy was coughed on slight tracheal compression. Rectal temperature, pulse and respiratory rate were normal, and grade 3/6 systolic murmur heard at the left heart base. The murmur was crescendo-decrescendo. Electrocardiography showed sinus arrhythmia, right-ventricular hypertrophy pattern, and right axis deviation. Thoracic radiography revealed cardiomegaly, bulging of the main pulmonary artery, and enlarged left side heart. Abdominal radiography revealed abdkominal distention. Echocardiography showed hypertrophy of right ventricle and turbulence in the pulmonary artery in parasternal oblique view. Subvalvular pulmonic stenosis was diagnosis based upon the clinical signs, physical examination, electrocardiography, radiography and echocardiography. We treated the patient with furosemide, enalapril and $\beta$-blocker. After the clinical signs of cough, abdominal distension and dyspnea were disappeared, she was on just $\beta$-blocker for prevention of occurrence of congestive heart failure. Now she was recovered her health, and she is not on any medication.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.133-140
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• 2010

We improve the performance of cardiac disorder classification by adding new temporal features extracted from continuous heart sound signals. We add three kinds of novel temporal features to a conventional feature based on mel-frequency cepstral coefficients (MFCC): Heart sound envelope, murmur probabilities, and murmur amplitude variation. In cardiac disorder classification and detection experiments, we evaluate the contribution of the proposed features to classification accuracy and select proper temporal features using the sequential feature selection method. The selected features are shown to improve classification accuracy significantly and consistently for neural network-based pattern classifiers such as multi-layer perceptron (MLP), support vector machine (SVM), and extreme learning machine (ELM).

• Journal of Chest Surgery
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• v.17 no.4
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• pp.780-785
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• 1984

We had studied 20 cases of VSD patients whose murmur was sustained after open heart surgery from 1977 to 1984. The postoperative cardiac catheterization was performed on post-op. 20th day. Their ages ranged from 5 to 25 years old. Among them, 4 patients had significant residual shunt which required reoperation. [1 patient; re-op, 3 patient; refused]. Sex ratio was 13:7 in male and female. Associated anomalies were PDA, ASD, Pulmonary stenosis, Mitral insufficiency. Except 1 case, all of them was Kirklin type II VSD. Postoperative complications were I RBBB, residual shunt, cardiac tamponade due to bleeding, wound infection. Preoperative pulmonary artery systolic pressure was highly related to residual shunt in our study. Postoperative LVEDV returned to normal range on the 3rd week.