• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.52 no.1
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• pp.42-47
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• 2016

This study was conducted to investigate visual threshold of nigro (Cichlasoma nigrofasciatum) on white LED light. The visual threshold was obtained by analyzing electrocardiogram (ECG) of the nigro. 5 individuals (body weight: 15.62~45.49 g; TL: 8.9~12.4 cm) were trained for lights by an electric stimulus. And then the heart rate (beats/10s) before and after switching on the light were compared. Light intensity range was from 0.00 to 226.4 lux. Average heart rate was 10.36 beats/10s in the normal condition. When the fish perceived the light, the heart rate was decreased. Visual threshold of the fish was 2.59 lux.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.129-130
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• 1998

In this paper, We designed the ECG-NIBP-$SpO_2$ patient monitor. This production can measure Electrocardiograph, Heart Rate, Noninvasive Blood Pressure, and Oxygen Saturation for Noninvasive Mehod and can display each information. These informations were implemented by the electrodes of ECG part, the cuff of NIBP module and the finger probe with light sensor of $SpO_2$ without injection of needle or catheter. In addition, We developed a new analysis algorithm and measurement technique for NIBP and $SpO_2$ to observe patient's conditions correctly.

• Journal of Magnetics
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• v.17 no.2
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• pp.133-137
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• 2012

We investigated the effects of pulsed magnetic fields (PMF) stimulus on electroencephalogram (EEG) alpha activity and heart rate variability (HRV) from electrocardiogram (ECG) measurements with various stimulus durations at acupoint PC9. The alpha activity in the EEG and the ratio of low frequency power and high frequency power (LHR) in the HRV, a reflection of sympathovagal activity, were increased and decreased, respectively, after PMF stimulus of 3 min. Our spectral analysis quantitatively proved that the changes in the EEG alpha activity were consistent with an autonomic function in the ECG. These findings suggest that appropriate PMF stimulus results in the same effect as that of acupuncture applied to the acupoint PC9, which is closely related to the parasympathetic activity of the autonomic nervous system.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.464-467
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• 1997

In this study, ECG was recorded during sleep patients with obstructive sleep apnea. We detecte(heart rate variability) signal from the ECG wa QRS detection algorithm. And we observed HRV by the power spectrum density using autoregr modeling. The experimental results were analysis sleep stage 1, sleep stage 2, sleep stage 3, sleep s sleep stage REM. In experimental result, the PSD with obstructive sleep apnea patients was distributed low frequency band except sleep step 4. These effect means that the sympathetic nervous system affected the sleep stage 1, 2, REM and the parasympathetic nervous system affected the sleep stage 3, 4 with obstructive sleep apnea patients.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.05a
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• pp.313-316
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• 2009

A novel approach for electrocardiogram (ECG) analysis within a functional sensor node has been developed and evaluated. The main aim is to reduce data collision, traffic over loads and power consumption in healthcare applications of wireless sensor networks (WSN). The sensor node attached on the patient's bodysurface around the heart can perform ECG analysis based on a QRS detection algorithm to detect abnormal condition of the patient. Data transfer is activated only after detected abnormality in the ECG. This system can reduce packet loss during transmission by reducing traffic overload. In addition, it saves power supply energy leading to more reliable, cheap and user-friendly operation in the WSN based ubiquitous health monitoring.

• Journal of Biomedical Engineering Research
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• v.12 no.4
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• pp.277-284
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• 1991

This study describes the ambulatory ECG monitoring system for the remote autom atic diagnosis. System: tlardware is based on one chip microcomputer(80c31) and its peripherals which consists of A/D, EPROM, RAM, LCD display and two preamplifiers, Power circuits, control logic circuits. A/D converted data were differentiated and low pass filtered. The detection of QRS complex and R point were accomplished by software algorithm based on adaptive threshold computed on low pass fi:leered signal. Rhythm analysis is performed by RR interval and average RR interval. The performance of QRS detection algorithm is evaluated by using MIT/BIH data base. Using this system, the trends of the arrythmia during the long term could be saved and displayed.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.4
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• pp.521-526
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• 2015

Research on how to use the biological characteristics of human to confirm the identity of the individual is being actively conducted. Electrocardiogram(: ECG) based biometric system is difficult to counterfeit and does not cause skin irritation on the subject. It can be easily combined with conventional biometrics such as fingerprint and face recognition to give multimodal biometric systems. In this thesis, biometric identification method analysing ECG waveform characteristics from Discrete Wavelet Transform(DWT) coefficients is suggested. Feature selection is performed on the 9 coefficients of DWT using the correlation analysis. The verification is achieved by using the error back propagation neural networks. Using the proposed approach on 24 subjects of MIT-BIH QT Database, 98.88% verification rate has been obtained.

• ETRI Journal
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• v.45 no.1
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2693-2695
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• 2004

Recently A virtual environment provides patient with stimuli which arouses phobia, and exposing to that environment makes him having ability to over come the fear. ECG and HRV are used in most virtual reality system. GSR is electrical impedance of biological tissues and the changes in impedance accompanying physiological activity. GSR is better than ECG or HRV for explaining mental states in other study. In this study, we will analysis GSR signal when a acrophobia patient and a normal is on high floor.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.11a
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• pp.267-268
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• 2002

We developed nausea, caused by disorder of autonomic nervous system, detection system using bio-signal analysis and artificial neural network in virtual reality enironment. We used 16 bio-signals, 9 EEGs, EOG, ECG, SKT, PPG, GSR, RSP, EGC, which has own analysis methods. We estimated nausea level by artificial neural network.