• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2016년도 춘계학술대회
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• pp.252-255
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• 2016

심음은 가장 쉽게 추출, 보관이 가능하고 가장 빨리 심장 질환을 진단하는데 도움을 줄 수 있기에 많이 사용되고 있다. 심음은 청진, 전자 청진을 통하여 얻어지는데 질환의 판정을 위해서는 전문의 많은 경험에 의존하고 있고, 자동 진단을 위한 장비는 매우 고가이며, 이를 위하여 심음의 정량화 과정이 선행되어야 한다. 본 연구에서는 심음의 한 종류인 심잡음을 심장 질환 별로 추출하여 정량화하여 자동 진단에 도움을 주고자 하였다. 심잡음은 심잡음 에너지율을 계산하여 정량화에 이용하였다. 추출된 심잡음 에너지의 파워 스펙트럼은 심장 질환별로 분류 가능한 형태학적 특징을 나타내었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.2051-2052
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• 2011

The structural defects of a heart often reflects the sounds that the heart produces. This paper describes heart sound analysis method using Wavelet transform and average Shannon energy. This can extract the features of heart sounds in various disease identify the heart sounds. Experimental results show that the presented method has potential application in detecting various heart diseases.

• 한국멀티미디어학회논문지
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• 제24권11호
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• pp.1500-1507
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• 2021

Identification and analysis of the first and second heart sounds(S1, S2) is the easiest way for cardiovascular disease prevention and early diagnosis. However, accurate identification is difficult because the heart sound includes organ movement, blood vortex, user experience, and noise influenced by subjective judgment. Therefore, an algorithm to automatically identify the S1 and S2 heart sounds based on blood vessel transit time(VTT) is presented in this paper. According to the experimental results of comparing the algorithm developed for S1 and S2 heart sound analysis with the conventional Shannon energy algorithm in 10 volunteers, it has been proven that the proposed algorithm can automatically identify S1 and S2 heart sounds with higher accuracy than existing algorithms.

• 한국멀티미디어학회논문지
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• 제19권3호
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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.

• ETRI Journal
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• 제37권4호
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• pp.824-832
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• 2015

Heart sounds are the main obstacle in lung sound analysis. To tackle this obstacle, we propose a diagnosis algorithm that uses singular spectrum analysis (SSA) and frequency features of heart and lung sounds. In particular, we introduce a frequency coefficient that shows the frequency difference between heart and lung sounds. The proposed algorithm is applied to a synthetic mixture of heart and lung sounds. The results show that heart sounds can be extracted successfully and localizations for the first and second heart sounds are remarkably performed. An error analysis of the localization results shows that the proposed algorithm has fewer errors compared to the SSA method, which is one of the most powerful methods in the localization of heart sounds. The presented algorithm is also applied in the cases of recorded respiratory sounds from the chest walls of five healthy subjects. The efficiency of the algorithm in extracting heart sounds from the recorded breathing sounds is verified with power spectral density evaluations and listening. Most studies have used only normal respiratory sounds, whereas we additionally use abnormal breathing sounds to validate the strength of our achievements.

• 한국음향학회지
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• 제28권1호
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• pp.32-43
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• 2009

본 논문은 자동 분할과 extreme learning machine (ELM)을 이용하여 연속 심음신호에 의한 심장질환 분류의 성능을 개선한다. 자동 분할을 위한 전처리 단계에서 비정상적인 심음신호는 심잡음 (murmur)과 클릭음 (click)을 포함하고 있기 때문에 제1음 (S1)과 제2음 (S2) 시작점 검출 결과가 부정확하거나 누락되어 기존의 심장질환 분류 시스템의 정확도를 저하시키게된다. 이러한 분할 오류에 의한 성능 저하를 감소하기 위해 S1 및 S2의 위치를 찾고, S1 및 S2의 시간 차이를 이용하여 부정확한 시작점을 교정한 다음 한 주기 심음 신호를 추출한다. 특징벡터로는 단일 주기의 심음 신호로부터 추출된 멜척도 필터뱅크 로그 에너지 계수와 포락선을 사용한다. 심장질환을 분류하기 위하여 한 개의 은닉층을 가진 ELM 알고리듬을 사용한다. 9가지 심장질환 분류 실험을 수행한 결과, 제안 방법은 81.6%의 분류 정확도를 나타내며, multi-layer perceptron(MLP), support vector machine (SVM), hidden Markov model (HMM) 중에서 가장 높은 분류 정확도를 보여준다.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권4호
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• pp.167-174
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• 2002

This study describes a fetal heart rate(FHR) estimation algorithm using phonogram. Using a phonogram amplifier, various fetal heart sounds are collected in a university hospital. The FHR estimation algorithms consists of a lowpass filter, decimation, envelop detection, pitch detection, and post-processing. The post-processing is the FHR decision procedure using all informations of fetal heart rates. Using the algorithm and other parameters of fetal heart sound, a fetal monitoring software was developed. This can display the original signals, the FFT spectra, FHR and its trajectory. Even though the fetal phonogram amplifier detects the fetal heart sounds well, the sound quality is not so good as the ultrasonography. In case of very week fetal heart sound, autocorrelation of it showed clear periodicity. But two main peaks in one period is an obstacle in pitch detection and peaks are not so vivid. The proposed FHR estimation algorithm showed very accurate and stable results. Since the developed software displays multiple parameters in real time and has convenient functions, it will be useful for the phonogram-style fetal monitoring device.

• 한국음향학회지
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• 제25권3호
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• pp.144-150
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• 2006

심장병이 있는 환자들을 진료할 때 의사들은 청진기를 이용하여 심음 (heart sound)을 듣고 이를 기준으로 환자의 병의 유무나 질환의 종류에 대한 기초적인 판단을 하게 된다. 하지만, 심음은 환자의 상태나 외부 잡음의 영향에 따라서 신호의 특성이 변하고 또한 정상적인 심음과 질병을 나타내는 심음과의 차이가 비교적 구분하기 어려울 정도로 작기 때문에 숙달된 전문의가 아니면, 진단의 정확도가 떨어질 가능성이 있다. 따라서 신호처리 기법을 이용하여 심음을 분석해서 심음이 정상적인지의 유무를 자동으로 판단할 수 있다면, 진단을 하는 의사들에게 유용한 정보가 될 것이라 생각된다. 본 연구에서는 심음의 질병유무와 질병종류를 자동으로 판단하기 위해서 기존에 많이 사용되었던 artificial neural network (ANN) 대신에 hidden Markov model (HMM)을 사용하는 방법을 제안하였으며, 기초적인 실험결과 상당히 우수한 성능을 보임을 알 수 있었다.

• 한국멀티미디어학회논문지
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• 제25권9호
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• pp.1251-1256
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• 2022

Auscultation of heart sounds using a stethoscope is the basic method to diagnose the cardiovascular disease and observation of abnormalities. However, the heart sound transmitted to the ear through the stethoscope is greatly affected by internal sounds such as organ movement or breathing. In addition, the user's experience significantly influences the accuracy of the auscultation result. Therefore, in this paper, we developed a wearable device that simultaneously measures heart sound and PPG signals for cardiac condition monitoring. The structure of the proposed device is designed to simultaneously measure heart sound and PPG signals when worn on a finger and placed on the chest. A prototype was implemented according to the design structure, and it was confirmed that the performance of measurements and collection for physiological signals was excellent through experiments.

• Child Health Nursing Research
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• 제16권3호
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• pp.211-219
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• 2010

Purpose: The study was done to measure the effects of maternal heart sound on body weight, physiologic reactions (heart rate [HR] and cortisol) and behavioral states of preterm infants. Methods: Thirty-five preterm infants were recruited from a neonatal intensive care unit at a university hospital. Institutional Review Board approval and informed consent were obtained. The infants were assigned to an experimental group (n=18) with an auditory stimulation for 7 days of life, a continuous delivery of maternal heart sound using MP3 attached inside the incubator, or to a control (n=17) without any auditory stimulation. The outcome variables, daily variations in weight, HR and behavioral states, and differences in cortisol were analyzed. Results: There were differences in variations of daily weights (F=3.431, p=.011) and in cortisol (t=3.184, p=.006) between groups, but no difference in variations of daily HR (F=0.331, p=.933) and behavioral states (F=1.842, p=.323). Conclusion: The findings support the safety of continuous maternal heart sound as no changes in HR and behavioral states occurred, and the efficacy as weight increased and cortisol decreased. This auditory simulation may lead to more efficient utilization of energy in preterm infants by consistently providing familiar sounds from intrauterine life and blocking noxious sounds from NICU environments.