• Proceedings of the KSMRM Conference
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• 2002.11a
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• pp.101-101
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• 2002

PURPOSE： To separate activations from other physiological and artifactual component that contribute to functional MRI(fMRI) recordings and detect one consistently task-related component activated during the acupuncture stimulation, we try to take the ICA(Independent Component Analysis) method.

• Journal of Biomedical Engineering Research
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• v.33 no.1
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• pp.15-24
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• 2012

Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.

• Biomedical Science Letters
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• v.20 no.4
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• pp.237-243
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• 2014

Triglyceride (TG) can cause death of macrophages and formation of foam cells thereby increasing inflammation in atherosclerotic plaques. Accumulation of cholesterol in macrophages is another critical event that promotes development of inflammatory cardiovascular diseases. Several proteins are known to transport intracellular cholesterol outside of the cell and these proteins are thought to be protective against atherosclerosis pathogenesis. It is unknown whether TG can affect cholesterol efflux in macrophages. In the current study, we examined mRNA expression levels of genes that promote efflux of cholesterol (ABCA1, ABCG1 and SR-B1). We found that TG treated THP-1 macrophages exhibited an increase in ABCG1 expression in a dose- and time-dependent manner. In contrast, the expression of ABCA1 and SR-B1 remained unchanged. To identify cell signaling pathways that participate in up-regulation of ABCG1, THP-1 macrophages were treated with various cell signaling inhibitors. We found that inhibition of the JNK and p38 MAPK pathway completely abrogated up-regulation of ABCG1 whereas inhibition of MEK1 further enhanced ABCG1 expression in TG treated THP-1 macrophages. Also, TG induced phosphorylation of JNK and p38 MAPK in THP-1 macrophages. These results suggest that TG may potentially influence cholesterol efflux in macrophages.

• Journal of Biomedical Engineering Research
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• v.36 no.5
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• pp.143-149
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• 2015

In this paper, we proposed a novel method for automatic detection for snoring and heart beat using a single piezoelectric sensor. For this study multi-rate signal processing technique was applied to detect snoring and heart beat from the single source signal. The sound event duration and intensity features were used to snore detection and heart beat was found by autocorrelation. The performance of the proposed method was evaluated on clinical database, which is the nocturnal piezoelectric snoring data of 30 patients that suffered obstructive sleep apnea. The method achieved sensitivity of 88.6%, specificity of 96.1% with accuracy of 95.6% for snoring and sensitivity of 94.1% and positive predictive value of 87.6% for heart beat, respectively. These results suggest that the proposed method can be a useful tool in sleep monitoring and sleep disordered breathing diagnosis.

• Biomedical Science Letters
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• v.24 no.3
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• pp.253-262
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• 2018

Platelets are activated at sites of vascular injury via several molecules, such as adenosine diphosphate, collagen and thrombin. Full platelet aggregation is absolutely essential for normal hemostasis. Moreover, this physiological event can trigger circulatory disorders, such as thrombosis, atherosclerosis, and cardiovascular disease. Therefore, platelet function inhibition is a promising approach in preventing platelet-mediated circulatory disease. Many studies reported the involvement of mitogen-activated protein kinases (MAPKs) signaling pathways in platelet functions. However, these studies were limited. Thus, we examined MAPK signaling pathways in human platelets using specific MAPK inhibitors, such as PD98059, SB203580, and SP600125. We observed that these inhibitors were involved in calcium mobilization and influx in human platelets. They also suppressed thrombin-induced ${\alpha}$- and ${\delta}$-granule release. These results suggest that PD98059, SB203580, and SP600125 exhibit $Ca^{2+}$ antagonistic effects.

• Biomedical Science Letters
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• v.26 no.1
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• pp.14-21
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• 2020

Quantitative evaluation of the sense of smell is done by the olfactory event-related potential (OERP) test. OERP consists of N1, P1, N2, P2, and P3, of which N1 and P1 latency are known to be affected by the concentration of odor stimuli associated with the pre-sensory phase and P2 and P3 are the stages at which odors are perceived and are known to change by subjective evaluation of the stimulus. The purpose of this study was to clarify the correlation between the expression of OERP and the subjective evaluation of the object on the fragrance stimulus using various fragrances. Therefore, the study examined the relative waveform power ratio, preference for each scent stimulus, and finally the amplitude and latency change of the components of OERP, N1 and P2. In contrast, it was found that the late P2 response waveform was an effective aroma stimulus recognition waveform in OERP compared to the initial response waveforms.

• Biomedical Science Letters
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• v.25 no.2
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• pp.139-148
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• 2019

The role of electroencephalography (EEG) in the development of brain-computer interface (BCI) technology is increasing. In particular, the importance of the analysis of event related potential (ERP) in various situations is becoming more significant in BCI technology. Studies on past maze and fire situations have been difficult due to risks and realistic problems. Nowadays, according to the development of virtual reality (VR) technology, realistic maze and fire situation can be realized. In this study, ERPs (P300, and evented related negativity) were analyzed to collect objective data on case determination in an emergency situation. In order to overcome the limitations of previous methods that evaluating the EEG frequency change, ERPs were derived by setting epochs for stimulation and standardizing them, and evaluated for ERPs in this study. P3a and P3b, which are subcomponents of P300, were analyzed and the evented related negativity (ERN) was analyzed together with error positivity (Pe). As a result of the study, statistically significant changes of ERPs were observed, this result, which has little related research, is considered to be meaningful as medical basic statistics.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.715-724
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• 2010

Recurrent event data occurs when a subject experience the event of interest several times and has been found in biomedical studies, sociology and engineering. Several diverse approaches have been applied to analyze the recurrent events (Cook and Lawless, 2007). In this study, we analyzed the YTOP(Young Traffic Offenders Program) dataset which consists of 192 drivers with conviction dates by speeding violation and traffic rule violation. We consider a subject-specific effect, frailty, to reflect the individual's driving behavior and extend to time-varying frailty effect. Another feature of this study is about the redefinition of risk set. During the study, subject may be under suspension and this period is regarded as non-risk period. Thus the risk variables are reformatted according to suspension and termination time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1670-1683
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• 2017

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.345-351
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• 2000

This study is to dvelop a radiotelemetry system to transmit and receive ECG (electrocardiograph) and event signals by using the frequency division multiplexing(FDM) technique. ECG signal sensed by the electrodes is amplified and added to the event signals acting in different frequency range for lead-off, nurse call and low level battery by using FDM. The sub-carrier oscillator using Colpitts circuits and main carrier frequency which is multiplied is frequency modulated by this superhetrodyne technique, and demodulated from the compose IF signal through the quadrature demodulator. A pulse counter demodulator and filtering circuits extract the original ECG and event signals.