• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.237-240
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• 2009

We suggested the portable measurement system that estimate heart-condition for heart disease patients and healthy. We used informations of ECG and PCG in this system. The informations of ECG and PCG obtained by using electrodes and microphone, respectively.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.47 no.1
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• pp.47-55
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• 2010

Although heart auscultation is an important tool for the diagnosis of heart disease, it is seen that the human ear is poorly suited for cardiac auscultation such as the paradoxical split of S2, the split of S2, and the summation of S2 and S3 gallop. In this paper, the analysis-synthesis method that is suitable for the characteristics of PCG is proposed to classify the heart sound. In analyzing the PCG according to matching pursuit method and synthesizing the PCG in each bandwidth based on the frequency characteristics, it sorts normal hearts with abnormal hearts that is difficult to classify as cardiac auscultation. The result shows that it qualifies the appearance of split in S2 and the paradoxical split of S2 and also can estimate the delay between the two components.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3001-3008
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• 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.

• Journal of Biomedical Engineering Research
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• v.38 no.6
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• pp.330-335
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• 2017

Cardiac arrest is owing to the failure of the heart that makes the blood circulation stop. Arrested blood circulation prevents the supply of the oxygen and the glucose and it results the loss of consciousness and, finally, brain death. Many public institution installed the AED for emergency treatment, but, it is not efficient when the patient is alone. In this paper, we made multiplexed wearable device for cardiac arrest detection. With this device, we measure the individual's electrocardiography, heart sound and motion. If the cardiac arrest is detected, the device make a warning horn and transmit the signal for defibrillation. We obtain 98.33% of ECG data, 94.5% of PCG data and 98.38% of IMU data accuracy for each evaluation and 93.33% accuracy for integrated evaluation.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2126-2134
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• 2015

The present study suggests a localization method for the first (S₁) and the second (S₂) feature of heart sounds, based on an algorithm involving frequency filtering, energy detection, and interval regulation. Localization accuracy was evaluated by comparing the algorithm with the traditional Hilbert transform-based localization method. Results show that the sensitivity and the positive predictivity value of proposed method, respectively, were 97.27 % and 99.94 % in S₁ detection and 94.99 % and 100 % in S₂ detection.

• 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.