• 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.

• Journal of Biomedical Engineering Research
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• v.12 no.3
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• pp.209-214
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• 1991

Digital fetal monitoring system based on the personal computer combined with the digital signal processing (DSP) board was implemented. The DSP board acquires and digitally processes ultra- sound fetal Doppler signal for digital signal conditioning, rectification, low -pass filtering, autocorrealtion function calculation and its peak detection. The personal computer interfaced with the DSP board is in charge of graphic display, hardcopy, data transmission and on -line analysis of fetal heart rate change including on - line warning system, base -line estmation, acceleration, deceleration and variability. It is one of the most suitable situation to apply the DSP chip for siganl conditioning, digital filtering of ultrasound fetal Dopier signal and fetal heart rate estimation using autocorrelation technique .

• Journal of Industrial Technology
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• v.32 no.A
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• pp.15-20
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• 2012

For emergency patients as heart disease, fast treatment is very important. This paper describes a smart phone software to detect emergency circumstance and request rescue. Detection of emergency condition of heart disease patients is based on physical motion and biological heart signals as electrocardiogram. Most smart phones have three axis acceleration sensor. On emergency condition, patients remain stationary. Thus the software detect stationary condition by using acceleration sensor data. For more precise detection, it combines electrocardiogram of patients. To request rescue, it sends help messages to designated persons. In addition, it generates emergency sound to surrounding people and plays a video of emergency measure that any person on the place can help the patient temporarily.

• 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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.314-319
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• 2002

To inspect how the different sensitivities in BAS (or BIS) moderate on the HRV pattern stimulated by positive or negative affective sound, we measured the electrocardiogram (ECG) of 25 students, consisted of 4 groups depending on the BAS (or BIS) sensitivity, while listening music or being exposed to noise. The power spectral density of HRV was derived from the ECG, and the power of HRV was calculated for 3 major frequency ranges (low frequency [LF], medium frequency [MF], and high frequency [HF]). We found that the index MF/(LF+HF), while listening music, was 20 % higher in the individuals with a low BIS but high BAS than in the individuals with a low sensitivity In both BIS and BAS. Moreover, in the former group this index was 30 % higher while listening music than while being exposed to noise. From these results we suggest that individuals with a low BIS but high BAS be more sensitive to positive affective stimuli than other groups, and the index MF/(LF+HF) be applicable to evaluate positive affects.

• Journal of Preventive Medicine and Public Health
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• v.33 no.1
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• pp.36-44
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• 2000

Objective : The heart diseases are known as a major cause of sudden death, as well as a cause of poor life-quality of school-age children. But there have been few mass screening of heart diseases in these children in Korea. This study was done to estimate the prevalence of heart diseases of these population. Methods : We screened all elementary students(grade 1) in 12 cities and 16 counties(Gun) in Kyonggi province from 1992 to 1955. The first screening was done by auscultation of doctors and simultaneously by checking using 'auto-interpreter of EKG-cardiac sound' (Fukuda Densi ECP 50A). We conducted luther examinations to whom classified as being abnormal condition in first screening, by using EKG, chest x-ray, doppler echocardiograpy(if needed). Results : The total number of examined students was 161,308(92% of the population), the male were 83,238 and female were 78,070. The congenital heart diseases(CHD) patients were 290(18 per 10,000) - male 155(18.6 per 10,000) and female 135(17.3 per 10,000). The most frequent disease was ventricula septal defect(VSD, 45.5%), Atrial septal defect(ASD, 14.8%), Tetralogy of Follot(TOF, 11.7%), and Patent Dutus Arteriosis(PDA, 7.6%) in order. In female, the order was VSD(48.1%), ASD(13.3%), TOF(11.1%), and PDA(10.4%). The total number of EKG abnormality were 433(62.7 per 10,000) among 69,056 screened children in 1995. The complete right bundle branch block(CRBBB) and paroxymal ventricular contraction(PVC) were frequent(26.6%, 26.3% in each), and incomplete right bunddle branch block(IRBBB, 14.6%), paroxymal atrial contraction(PAC, 6.7%), abnormal Q(5.8%), Wolf-Pakinson-White syndrom (5.5%) in order. In female, the most frequent abnormality was PVC(29.8%), and CRBBB(19.9%) in order. Conclusion : We could present the stable prevalence of the rare heart disease. The prevalence of congenital heart diseases was 18.0 per 10,000 and of EKG abnormality was 62.7 per 10,000 among school children.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.321-325
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• 1998

This paper presents a simple, practical, and efficient method for estimating the speed of sound in reflection mode in ultrasound medical imaging. Its accurate determination is indispensable in order to obtain both good resolution and correct geometrical and volumetric information about human organs such as heart and kidney. Up to now, there have been several methods available, but they all suffer from either poor performance or high complexity. The proposed method finds out an optimum focusing delay profile in such a way that the brightness in a region of interest is maximized using continuous dynamic focusing in receive under fixed transmit focusing. Experiments carried out on a real ultrasound medical phantom reveals that the method a quite simple and effective in providing good speed of sound estimation, hence improved resolution and images, adding to dignostic utility.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.61-70
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• 2017

Physiological responses have been measured to recognize emotion. Although physiological responses have been interrelated between organs, their connectivities have been less considered for emotion recognizing. The connectivities have been assumed to enhance emotion recognition. Specially, autonomic nervous system is physiologically modulated by the interrelated functioning. Therefore, this study has been tried to analyze connectivities between heart and respiration and to find the significantly connected variables for emotion recognition. The eighteen subjects(10 male, age $24.72{\pm}2.47$) participated in the experiment. The participants were asked to listen to predetermined sound stimuli (arousal, relaxation, negative, positive) for evoking emotion. The bio-signals of heart and respiration were measured according to sound stimuli. HRV (heart rate variability) and BRV (breathing rate variability) spectrum were obtained from spectrum analysis of ECG (electrocardiogram) and RSP (respiration). The synchronization of HRV and BRV spectrum was analyzed according to each emotion. Statistical significance of relationship between them was tested by one-way ANOVA. There were significant relation of synchronization between HRV and BRV spectrum (synchronization of HF: F(3, 68) = 3.605, p = 0.018, ${\eta}^2_p=0.1372$, synchronization of LF: F(3, 68) = 5.075, p = 0.003, ${\eta}^2_p=0.1823$). HF difference of synchronization between ECG and RSP has been able to classify arousal from relaxation (p = 0.008, d = 1.4274) and LF's has negative from positive (p = 0.002, d = 1.7377). Therefore, it was confirmed that the heart and respiration to recognize the dimensional emotion by connectivity.

• Science of Emotion and Sensibility
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• v.4 no.2
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• pp.79-88
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• 2001

The objectives of this study were to investigate the relationship of sound parameters with subjective sensation and physiological responses, and to figure out the interrelationship between the subjective sensation and physiological responses. Sound parameters calculated were LPT, ΔL, Δf, loudness[Z], and sharpness[Z]. Subjective sensation was evaluated in 7 aspects(soft-hard, loud-quiet, pleasant-unpleasant, sharp-dull, clear-obscure, rough-smooth, high-low) by thirty participants. We acquired physiological responses when each fabric sound was presented to 10 participants. Physiological signals obtained in this study were electroencephalogram(EEG), pulse volume(PV), skin conductance level(SCL), and LF/HF of heart rate variability. The larger the values of loudness[Z] and LPT, the louder and the rougher the subjective sensation of the perceived fabric sound. Also, the larger the values of loudness[Z] and LPT, the harder, the duller, and the less pleasant. As LPT increased, PV decreased. Loudness[Z] increased in proportion to SCL and so did sharpness[Z] to LF/HF. As the sound perceived to be quieter and clearer, the relative power of slow alpha rose. As the sound perceived to be more pleasant and smoother, PV rose.

• Journal of Biomedical Engineering Research
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• v.45 no.3
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• pp.109-117
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• 2024

We developed a device that integrates digital otoscope and stethoscope for telemedicine. The integrated device was utilized for the collection of tympanic membrane images and cardiac auscultation data. Data accumulated on the platform server can support real-time diagnosis of heart and eardrum diseases using artificial intelligence. Public data from Kaggle were used for deep learning. After comparing with various deep learning models, the MobileNetV2 model showed superior performance in analyzing tympanic membrane data, and the VGG16 model excelled in analyzing cardiac data. The classification algorithm achieved an accuracy of 89.9% for eardrums data and 100% for heart sound data. These results demonstrate the possibility of diagnosing diseases without the limitations of time and space by using this platform.