• Journal of IKEEE
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• v.25 no.1
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• pp.212-217
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• 2021

Premature contraction arrhythmia is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Most of arrhythmia clasification methods have been developed with the primary objective of the high detection performance without taking into account the computational complexity. Also, personalized difference of ECG signal exist, performance degradation occurs because of carrying out diagnosis by general classification rule. Therefore it is necessary to design efficient method that classifies arrhythmia by analyzing the persons's physical condition and decreases computational cost by accurately detecting minimal feature point based on only QRS features. We propose method for personalized specific classification of premature contraction arrhythmia based on QRS features in smart healthcare environments. For this purpose, we detected R wave through the preprocessing method and SOM and selected abnormal signal sets.. Also, we developed algorithm to classify premature contraction arrhythmia using QRS pattern, RR interval, threshold for amplitude of R wave. The performance of R wave detection, Premature ventricular contraction classification is evaluated by using of MIT-BIH arrhythmia database that included over 30 PVC(Premature Ventricular Contraction) and PAC(Premature Atrial Contraction). The achieved scores indicate the average of 98.24% in R wave detection and the rate of 97.31% in Premature ventricular contraction classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.2
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• pp.233-242
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• 2018

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting feature point based on only R peak through optimal R wave. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using 9 record of MIT-BIH arrhythmia database that included over 30. The achieved scores indicate the average of 99.02% in R wave detection and the rate of 94.85% in PVC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.

• Journal of Chest Surgery
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• v.12 no.4
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• pp.350-354
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• 1979

The incidence of congenital aortic valvular stenosis has been known rare, and approximately 3-6% of congenital heart diseases. Recently, we experienced 1 case of congenital aortic valvular stenosis, and which was corrected surgically under extracorporeal circulation successfully. A 11 years old male pt. was admitted to N.M.C. because of dyspnea, dizziness, chest pain and episode of syncope. An auscultation, harsh systolic murmur [Gr. IV/VI] was noted at aortic area and also palpable strong thrill. ECG showed LVH c strain pattern and suspicious LVH finding in simple chest P-A film. In Lt. cardiac catheterization, abrupt pressure change [110mmHg] between LV & Aorta was noted across the aortic valve. And aortic insufficiency was absent, well visualized both coronary arteries and suspicious bicuspid aortic valve in aortography. Valve form was bicuspid, large one was noncoronary cusp and another cusp was Rt. & Lt. coronary cusp which was interpositioned rudimentary commissure. Central aortic orifice was about 5ram in diameter. Valvulotomy was done along the fusioned commissure between noncoronary cusp and Rt. & Lt.coronary cusp, and then short incision was added between Rt. coronary cusp & Lt. coronary cusp. Immediate postoperative course smooth but unknown cardiac arrest was noted in POD second day. Complete recovery was done without sequelae by resuscitation. After operation, clinical symptoms were subsided but systolic murmur [Gr. II/VI] was audible at aortic area, diastolic murmur was absent. ECG showed still remained LVH but much decreased R wave voltage in Lt. precordial leads. Simple chest P-A showed no interval changes compared to preop film. Control Lt. heart catheterization revealed still remained pressure gradient [40ramrig] between LV & Aorta. But much decreased pressure gradient compared to preop pressure gradient [110mmHg].

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.83-90
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• 2016

In this paper, we performed the analysis of transmission performance for Collision Free Period(CFP) supported by the low-power communication technology, IEEE 802.15.4 MAC (Media Access Control). For the analysis, periodic traffic, original service target of CFP, is considered and, according to the Quality of Service required, packet arrival pattern to MAC layer is categorized as batch and non-batch, and analysis on throughput, delay, and energy is performed for those patterns. On the basis of the obtained analysis, performance comparison with Collision Avoidance Period(CAP) is carried out for the health care applications that generate periodic traffic such as Pedometer, ECG, EMG. The evaluation confirms that CFP is more energy efficient for healthcare applications that generate periodic and time-critical traffic and moreover for the application with high bandwidth requirement CFP achieves up to 46% energy savings compared to CAP.

• YAKHAK HOEJI
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• v.53 no.2
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• pp.51-59
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• 2009

Quantitative analysis of (+)-catechin (C), (-)-epigallocatechin (EGC), (-)-epicatechin (EC), (-)-epigallocatechin gallate (EGCG), (-)-epicatechin gallate (ECG) and caffeine in commercial green tea was carried out by HPLC employing gradient elution of 0.1% acetic acid and acetonitrile on ODS column. The optimized HPLC method provided satisfactory linearity, accuracy and precision. The relationship between the concentration of the components and cultivated region of the commercial green tea was not significant, while the concentration of EGCG, ECG and caffeine decreased significantly in the later harvested green tea samples (p<0.01). Multivariate analysis of the components was performed in order to characterize and evaluate the cultivated region and harvest period-related variation. Hierarchical clustering and discriminant analysis were applied to classify the geographical and seasonal origins of the green tea samples. The classification accuracy of the cultivated region and harvest period by discriminant analysis was 95% and 91%, respectively, indicating that this method could be reliable and convenient for the quality control of herbal products with different origin.

• Journal of Biomedical Engineering Research
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• v.23 no.6
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• pp.485-489
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• 2002

In this study. respiratory efforts were monitored by the change of pulse transit time (PTT) which is related with the arterial pressure PTT is the time interval between the peak of R wave in ECG and the maximal slope point of photoplethysmogram(PPG). Biosignals, ECG and finger photoplethysmogram(PPG), were converted to digital data, and PTT was evaluated in personal computer with every heart beat. Results were presented as a graph using spline interpolation. The software was implemented in C$\^$++/ as a window-based application program. PTT was periodically changed according to airflow in resting respiration. In the resting respiration, PTT was changed according to the respiratory cycle. The amplitude of PTT fluctuation was increased by deep respiration, and increased by partial airway obstruction. These results suggest that PTT is responsible to respiratory effort which could be evaluated by the pattern of PTT change. And it is expected that PTT could be applied in the monitoring of respiratory effort by noninvasive methods, and is very useful method for the evaluation of respiratory distress.

• Journal of Biomedical Engineering Research
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• v.41 no.5
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• pp.185-194
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• 2020

Arrhythmia is basically diagnosed with the electrocardiogram (ECG) signal, however, ECG is difficult to measure and it requires expert help in analyzing the signal. On the other hand, the radial pulse can be measured with easy and uncomplicated way in daily life, and could be suitable bio-signal for the recent untact paradigm and extensible signal for diagnosis of Korean medicine based on pulse pattern. In this study, we developed an irregular radial pulse detection algorithm based on a learning model and considered its applicability as arrhythmia screening. A total of 1432 pulse waves including irregular pulse data were used in the experiment. Three data sets were prepared with minimal preprocessing to avoid the heuristic feature extraction. As classification algorithms, elastic net logistic regression, random forest, and extreme gradient boosting were applied to each data set and the irregular pulse detection performances were estimated using area under the receiver operating characteristic curve based on a 10-fold cross-validation. The extreme gradient boosting method showed the superior performance than others and found that the classification accuracy reached 99.7%. The results confirmed that the proposed algorithm could be used for arrhythmia screening. To make a fusion technology integrating western and Korean medicine, arrhythmia subtype classification from the perspective of Korean medicine will be needed for future research.

• Journal of the Ergonomics Society of Korea
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• v.25 no.4
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• pp.129-135
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• 2006

he purpose of this study is to measure bio-signal to investigate the driver's physiological response change under real situation using train simulator. The train simulator used in this study is KTX model and according to changes of driving situation, The bio-signal controlled by autonomic nervous system, such as GSR(Galvanic Skin Response), SpO2(Saturation percent O2), HR(Heart Rate), ECG(Electrocardiograph), EEG(Electroencephagram) and movement and response of eye were measured. Statistically significant difference in bio-signal data and eye movement activity pattern were investigated under several different driving speeds using analysis of variance (p<0.05). The GSR and HR value measured in average and mission speed operation is higher than in high-speed operation. β wave of EEG in average speed operation become more activated than in high speed operation. In accordance with a characteristic of rail vehicle, movement and response of eye in high-speed operation requiring relatively simple maneuver become less activated than in either average or mission speed operations. Conclusively, due to more careful driving controls in average and mission speed operation are required than in high-speed operation, level of mental and physical stresses of train driver was increased and observed through changes of bio-signal and eye movement measured in this study.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.749-754
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• 2022

High adhesion and water resistance of the skin surface are required for wearable and skin-attachable electronic patches in various medical applications. In this study, we report a stretchable electronic patch that mimics the drainable structure pattern of the hexagonal channels of frog's pads and the sucker of an octopus based on carbon-based conductive polymer composite materials. The hexagonal channel structure that mimics the pads of frogs drains water and improves adhesion through crack arresting effect, and the suction structure that mimics an octopus sucker shows high adhesion on wet surfaces. In addition, the high-adhesive electronic patch has excellent adhesion to various surfaces such as silicone wafer (max. 4.06 N/cm²) and skin replica surface (max. 1.84 N/cm²) in dry and wet conditions. The high skin-adhesive electronic patch made of a polymer composite material based on a polymer matrix and carbon particles can reliably detect electrocardiogram (ECG) in dry and humid environments. The proposed electronic patch presents potential applications for wearable and skin-attachable electronic devices for detecting various biosignals.