• Sleep Medicine and Psychophysiology
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• v.27 no.2
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• pp.67-76
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• 2020

Objectives: Brain maturation involves brain lateralization and asymmetry to achieve efficient information processing and cognitive controls. This study elucidates normal brain maturation change during the gap between ages 6-9 and age 14-17 using resting EEG. Methods: An EEG dataset was acquired from open source MIPDB (Multimodal Resource for Studying Information Processing in the Developing Brain). Ages 6-9 (n = 24) and ages 14-17 (n = 26) were selected for analysis, and subjects with psychiatric illness or EEG with severe noise were excluded. Finally, ages 6-9 (n = 14) and ages 14-17 (n = 11) were subjected to EEG analysis using EEGlab. A 120-sec length of resting EEG when eyes were closed was secured for analysis. Brain topography was compartmentalized into nine regions, best fitted with brain anatomical structure. Results: Absolute power of the delta band and theta band in ages 6-9 was greater than that of ages 14-17 in the whole brain, and, also is relative power of delta band in frontal compartment, which is same line with previous studies. The relative power of the beta band of ages 14-17 was greater than that of ages 6-9 in the whole brain. In asymmetry evaluation, relative power of the theta band in ages 14-17 showed greater power in the left than right frontal compartment; the opposite finding was noted in the parietal compartment. For the alpha band, a strong relative power distribution in the left parietal compartment was observed in ages 14-17. Absolute and relative power of the alpha band is distributed with hemispheric left lateralization in ages 14-17. Conclusion: During the gap period between ages 6-9 and ages 14-17, brain work becomes more complicated and sophisticated, and alpha band and beta band plays important roles in brain maturation in typically developing children.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.1
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• pp.7-12
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• 2020

Various parameters are used to measure anesthetic depth during surgery using brain waves, and in actual clinical use, the linear analysis SEF is widely used. However, with recent studies showing that biological signals including EEG, contain nonlinear properties interest in nonlinear analysis of brain signals is increasing and parameters based on these are being developed. In this study, we are going to develop a parameter that can measure EEG using the nonlinear analysis method and extract noise that can be mixed with external electronic equipment and EEG instrumentation by comparing it with the data from the bispectrum analysis of static waves.

• Smart Media Journal
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• v.13 no.4
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• pp.86-93
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• 2024

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.8
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• pp.725-730
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• 2013

This paper proposed a novel method for an analysis feature of an Electroencephalogram (EEG) at all channels simultaneously. In a BCI (Brain-Computer Interface) system, EEGs are used to control a machine or computer. The EEG signals were weak to noise and had low spatial resolution because they were acquired by a non-invasive method involving, attaching electrodes along with scalp. This made it difficult to analyze the whole channel of EEG signals. And the previous method could not analyze multiple stimuli, the result being that the BCI system could not react to multiple orders. The method proposed in this paper made it possible analyze multiple-stimuli by grouping the channels. We searched the groups making the largest correlation coefficient summation of every member of the group with a BHS (Binary Harmony Search) algorithm. Then we assumed the EEG signal could be written in linear summation of groups using concentration parameters. In order to verify this assumption, we performed a simulation of three subjects, 60 times per person. From the simulation, we could obtain the groups of EEG signals. We also established the types of stimulus from the concentration coefficient. Consequently, we concluded that the signal could be divided into several groups. Furthermore, we could analyze the EEG in a new way with concentration coefficients from the EEG channel grouping.

• Journal of Biomedical Engineering Research
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• v.30 no.2
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• pp.179-184
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• 2009

Magnetic Resonance Electrical Impedance Tomography (MREIT) is a new bio-imaging modality providing cross-sectional conductivity images from measurements of internal magnetic flux densities produced by externally injected currents. Recent MREIT studies demonstrated successful conductivity image reconstructions of postmortem and in vivo canine brain. However, the whole head imaging was not achieved due to technical issues related with electrodes and noise in measured magnetic flux density data. In this study, we used a new carbon-hydrogel electrode with a large contact area and injected 30 mA imaging current through a canine head. Using a 3T MREIT system, we performed a postmortem canine experiment and produced high-resolution conductivity images of the entire canine head. Collecting magnetic flux density data inside the head subject to multiple injection currents, we reconstructed cross-sectional conductivity images using the harmonic $B_z$ algorithm. The conductivity images of the canine head show a good contrast not only inside the brain region including white and gray matter but also outside the brain region including the skull, temporalis muscle, mandible, lingualis proprius muscle, and masseter muscle.

• Investigative Magnetic Resonance Imaging
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• v.21 no.4
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• pp.210-222
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• 2017

Purpose: To develop a novel combination of controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA) with integrated SSFP (CAIPI-iSSFP) for accelerated SSFP imaging without banding artifacts at 3T. Materials and Methods: CAIPI-iSSFP was developed by adding a dephasing gradient to the balanced SSFP (bSSFP) pulse sequence with a gradient area that results in $2{\pi}$ dephasing across a single pixel. Extended phase graph (EPG) simulations were performed to show the signal behaviors of iSSFP, bSSFP, and RF-spoiled gradient echo (SPGR) sequences. In vivo experiments were performed for brain and abdominal imaging at 3T with simultaneous multi-slice (SMS) acceleration factors of 2, 3 and 4 with CAIPI-iSSFP and CAIPI-bSSFP. The image quality was evaluated by measuring the relative contrast-to-noise ratio (CNR) and by qualitatively assessing banding artifact removal in the brain. Results: Banding artifacts were removed using CAIPI-iSSFP compared to CAIPI-bSSFP up to an SMS factor of 4 and 3 on brain and liver imaging, respectively. The relative CNRs between gray and white matter were on average 18% lower in CAIPI-iSSFP compared to that of CAIPI-bSSFP. Conclusion: This study demonstrated that CAIPI-iSSFP provides up to a factor of four acceleration, while minimizing the banding artifacts with up to a 20% decrease in the relative CNR.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.12
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• pp.102-107
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• 2009

As technology gets developed, medical equipments are also modernized and leading-edge systems, such as PACS become popular. Many scientists noticed importance of medical image processing technology. Technique of region segmentation is the first step of digital medical image processing. Segmentation technique helps doctors to find out abnormal symptoms early, such as tumors, edema, and necrotic tissue, and helps to diagnoses correctly. Segmentation of white matter, gray matter and CSF of a brain image is very crucial part. However, the segmentation is not easy due to ambiguous boundaries and inhomogeneous physical characteristics. The rate of incorrect segmentation is high because of these difficulties. Fuzzy-based segmentation algorithms are robust to even ambiguous boundaries. In this paper a modified Fuzzy-based segmentation algorithm is proposed to handle the noise of MR scanners. A proposed algorithm requires minimal computations of mean and variance of neighbor pixels to adjust a new neighbor list. With the addition of minimal compuation, the modified FCM(mFCM) lowers the rate of incorrect clustering below 30% approximately compared the traditional FCM.

• Nuclear Engineering and Technology
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• v.54 no.7
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• pp.2592-2598
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• 2022

Research groups in the field of PET instrumentation are studying time-of-flight(TOF) technology to improve the signal-to-noise ratio of PET images. Scintillation light transport and collection plays an important role in improving the coincidence resolving time(CRT) of PET detector based on a pixelated crystal array. Four crystal arrays were designed by the different optical reflection configuration such as external reflectors and surface treatment on the CRT and compared with the light output, energy resolution and CRT. The design proposed in the study was composed of 8 × 8 LYSO crystal array consisted of 3 × 3 × 15 mm³ pixels. The entrance side was roughened while the other five surfaces were polished. Four sides of all crystal pixels were wrapped with ESR-film, and the entrance surface was covered by Teflon-tape. The design provided an excellent timing resolution of 210 ps and improved the CRT by 16% compared to the conventional method using a polishing treatment and ESR-film. This study provided a method for improving the light output and CRT of a pixelated scintillation crystal-based brain TOF PET detector. The proposed configuration might be an attractive detector design for TOF brain PET requiring fast timing performance with high cost-effectiveness.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.167-172
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• 1999

Purpose : To evaluate the detection rate of hyperacute intracerebral hemorrhage in echo planar imaging (EPI) and other MR sequences. materials and Methods : Intracerebral hemorrhage was experimentally induced in ten rats. EPI, fast spin-echo (FSE) T2 weighted images, fluid attenuated inversion recovery (FLAIR), spin-echo (SE) T1 weighted images and gradient echo (GE) T1 weight ed images of rat's brains were obtained 2 hours after onset of intracerebral hemorrhage. EPI and FSE T2 images were additionally obtained 30 min and 1 hour after onset of hemorrhage in 3 and 6 rat, repeatedly, For objective visual assessment, discrimination between the lesion and normal brain parenchyma was evaluated on various MR sequences by three radiologists. For quantitative assessment, contrast-to-noise ratio (CNR) was calculated fro hemorrhage-normal brain parenchyma. Statistical analysis was performed usning the Wilcoxon-Ranks test. Results : EPI, FLAIR, and FSE T2 images showed high signal intensity lesions. The lesion discrimination was easier on EPI than on other sequences, and also EPI showed higher signal intensity for the subjective visual assessment. In quantitative evaluation, CNR of the hemorrhagic lesion versus normal brain parenchyma were higher on EPI and FLAIR images (p<0.01). There was no difference in CNR between EPI and FLAIR (p>0.10). On MR images obtained 30 minutes and 1 hour after the onset of intracerebral hemorrhage, the lesion detection was feasible on both EPI and FSE T2 images showing high signal intensity. Conclusion : EPI showed higher detection rate as compared with other MR sequences and could be useful in early detection and evaluation of intracerebral hemorrhage.

• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.569-576
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• 2007

Ventricular Assist Device(VAD) has switched its goal from a short-tenn use for bridge-to-transplantation to a long-tenn use for destination therapy, With this goal, the importance of long-tenn reliability gets more interests and importances, H-VAD is an portable extracorporeal biventricular assist device, and adopts an electro-pneumatic driving mechanism. The pneumatic pressure to pump out blood is generated with compression of bellows, and is transmitted in a closed pneumatic circuit through a pneumatic line. The existing pneumatic VAD adopts a air compressor which can generate stable pressures but has defects such as a noise and a size problem. Thus, it is not suitable for being used as a portable device, These problems are covered with adopting a closed pneumatic circuit mechanism with a bellows which has a small size and small noise generation, but it has defects that improper pneumatic setting causes a failure of adequate flow generation. In this study, the pneumatic pressure regulation system is developed to cover these defects of a bellows-type pneumatic VAD. The optimal pneumatic pressure conditions according to various afterload conditions for an optimal flow rate were investigated and the afterload estimation algorithm was developed, The final pneumatic regulation system estimates a current afterload and regulate the pneumatic pressure to the optimal point at a given afterload condition. The afterload estimation algorithm showed a sufficient performance that the standard deviation of error is 8.8 mmHg, The pneumatic pressure regulation system showed a sufficient performance that the flow rate was stably governed to various afterload conditions. In a further study, if a additional sensor such as ultrasonic sensor is developed to monitor the direct movement of diaphragm in a blood pump part, the reliability would be greatly increased. Moreover, if the afterload estimation algorithm gets more accuracy, it would be also helpful to monitor the hemodynamic condition of patients.