• Journal of Dental Anesthesia and Pain Medicine
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• v.17 no.4
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• pp.241-251
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• 2017

Devices that monitor the depth of hypnosis based on the electroencephalogram (EEG) have long been commercialized, and clinicians use these to titrate the dosage of hypnotic agents. However, these have not yet been accepted as standard monitoring devices for anesthesiology. The primary reason is that the use of these monitoring devices does not completely prevent awareness during surgery, and the development of these devices has not taken into account the neurophysiological mechanisms of hypnotic agents, thus making it possible to show different levels of unconsciousness in the same brain status. An alternative is to monitor EEGs that are not signal processed with numerical values presented by these monitoring devices. Several studies have reported that power spectral analysis alone can distinguish the effects of different hypnotic agents on consciousness changes. This paper introduces the basic concept of power spectral analysis and introduces the EEG characteristics of various hypnotic agents that are used in sedation.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.179-182
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• 2015

Detecting and assessing human-related risks is critical to improve the on-site safety condition and reduce the loss in lives, time and budget for construction industry. Recent research in neural science and psychology suggest inattentional blindness that caused by overload in working memory is the major cause of unexpected human related accidents. Due to the limitation of human mental workload, laborers are vulnerable to unexpected hazards while focusing on complicated and dangerous construction tasks. Therefore, detecting the risk perception abilities of workers could help to identify vulnerable individuals and reduce unexpected injuries. However, there are no available measurement approaches or devices capable of monitoring construction workers' mental conditions. The research proposed in this paper aims to develop such a measurement framework to evaluate hazards through monitoring electroencephalogram of labors. The research team developed a wearable safety monitoring helmet, which can collect the brain waves of users for analysis. A bispectrum approach has been developed in this paper to enrich the data source and improve accuracy.

• Journal of Dental Anesthesia and Pain Medicine
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• v.18 no.4
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• pp.235-244
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• 2018

Background: Patients with intellectual disability (ID) often require general anesthesia during oral procedures. Anesthetic depth monitoring in these patients can be difficult due to their already altered mental state prior to anesthesia. In this study, the utility of electroencephalographic indexes to reflect anesthetic depth was evaluated in pediatric patients with ID. Methods: Seventeen patients (mean age, $9.6{\pm}2.9years$) scheduled for dental procedures were enrolled in this study. After anesthesia induction with propofol or sevoflurane, a bilateral sensor was placed on the patient's forehead and the bispectral index (BIS) was recorded. Anesthesia was maintained with sevoflurane, which was adjusted according to the clinical signs by an anesthesiologist blinded to the BIS value. The index performance was accessed by correlation (with the end-tidal sevoflurane [EtSevo] concentration) and prediction probability (with a clinical scale of anesthesia). The asymmetry of the electroencephalogram between the left and right sides was also analyzed. Results: The BIS had good correlation and prediction probabilities (above 0.5) in the majority of patients; however, BIS was not correlated with EtSevo or the clinical scale of anesthesia in patients with Lennox-Gastaut, West syndrome, cerebral palsy, and epilepsy. BIS showed better correlations than SEF95 and TP. No significant differences were observed between the left- and right-side indexes. Conclusion: BIS may be able to reflect sevoflurane anesthetic depth in patients with some types of ID; however, more research is required to better define the neurological conditions and/or degrees of disability that may allow anesthesiologists to use the BIS.

• Journal of Biomedical Engineering Research
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• v.22 no.1
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• pp.81-90
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• 2001

본 연구에서는 디지털 영상 뇌파계(digital video electroencephalogram, Digital VEEG)에서 비디오 영상과 뇌전도 파형의 동기화된 편집 시스템을 구성한다. 이 시스템은 기존 아날로그 영상 뇌파계(analog video electroencephalogram)의 동기화 문제와 디지털 영상 시스템에서의 영상편집 문제를 해결하기 위하여 MPEG-I(이하 MPEG) 고압축 기술을 이용한 MPEG 인코딩 보드(encoding board)와 MPEG 편집 엔진(editing engine)을 각각 사용하였다. 시스템은 디지털 영상뇌파계모듈과 디지털 편집 모듈로 구성되며, 뇌전도모듈에서는 환자에게 연결된 전극을 통해 들어온 뇌파를 생체신호증폭기를 이용하여 증폭한 후 AD 보드(analog to digital board)를 이용 디지털화한다. 디지털 카메라로 촬영된 환자영상의 아날로그 영상신호(NTSC 신호)는 MPEG 인코딩 보드를 이용하여 고압축 디지털화한다. 이후 디지털화된 뇌전도신호와 MPEG 형식의 영상을 시간 동기화하여 두 개의 모니터에 각각보여준다. 편집 모듈에서는 영상신호와 뇌파신호를 어느 부분이든 간단한 조작으로 오려 붙이기(cut and paste) 기능을 이용할 수 있다. 본 시스템은 사용된 데이터 모두 디지털 기술을 이용하여 영상과 뇌파신호의 정확한 동기화 및 각각의 데이터의 오려 붙이기 기능을 가능케 하였으며, 이는 환자의 데이터를 관리 및 보관하는데 있어, 임상의에게 의미 있는 자료만을 모아서 효율적으로 관리할 수 있게 해준다. 이와 같은 장점을 갖는 디지 영상뇌파계 편집시스템을 구현하였다.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.3
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• pp.294-299
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• 2017

The aim of this study was to detect the status of epilepticus and seizure based on the initial patterns observed in the first 30 minutes of continuous electroencephalogram (cEEG) monitoring. An cEEG was recorded digitally using electrodes applied according to the International 10~20 System. The EEG data were reviewed from January 2014 to December 2015. The baselines of the EEG patterns were characterized by lateralized periodic discharges, generalized periodic discharges, burst suppression, focal epileptiform, asymmetric background, generalized slowing, and generalized periodic discharges with a triphagic wave. The etiology was classified into five categories. The subjects of this study were 128 patients (age: $56.9{\pm}17.5years$, male:female, 74:54). The mean cEEG monitoring duration was $5.5{\pm}5.1$ (min:max, 1:33) days. The EEG pattern categories included lateralized periodic discharges (N=7), generalized periodic discharges (N=10), burst suppression (N=6), focal epileptiform (N=19), asymmetric background (N=24), generalized slowing (N=51), and generalized periodic discharges with a triphagic wave (N=11). The etiological classifications of the patients with status epilepticus were remote symptomatic (N=4), remote symptomatic with acute precipitant (N=9), acute symptomatic (N=6), progressive encephalopathy (N=2), and febrile seizure (N=1). cEEG monitoring was found to be useful for the diagnosis of non-convulsive epileptic seizures or status epilepticus. The seizure was confirmed by the EEG pattern.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.887-895
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• 2014

Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.768-773
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1331-1338
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• 2007

A new method for detecting and eliminating the Electrocardiogram(ECG) artifact from the scalp Electroencephalogram(EEG) is proposed. Based on the single channel EEG, the proposed method consists of 4 procedures: emphasizing the R-wave of ECG artifact from EEG using the least squares acceleration(LSA) filter, detecting the R-wave from the LSA filtered EEG using the phase space method and R-R interval, generating the delayed impulse synchronized to the R-wave and elimination of the ECG artifacts based on the adaptive digital filter using the impulse and raw EEG. The performance of the proposed method was evaluated in the two separating parts of R-wave detection and, ECG estimation and elimination from EEG. In the R-wave detection, the proposed method showed the mean error rate of 6.285(%). In the ECG estimation and elimination using simulated and/or real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, in which independent component analysis and ensemble average method are used. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifact from single channel EEG and simple for ambulatory/portable EEG monitoring system.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.