scholarly journals Profil Electroencephalogram (EEG) Pasien Rumah Sakit Angkatan Laut Dr. Ramelan Surabaya Periode Januari 2018 sampai Desember 2018

2019 ◽  
Vol 16 (2) ◽  
pp. 153
Author(s):  
ERIC HARTONO TEDYANTO ◽  
NI KOMANG SRI DEWI UTAMI ◽  
WIENTA DIASVITRI
Keyword(s):  
Brain Cortex ◽  
Total Population ◽  
Brain Activity ◽  
Descriptive Research ◽  
Different Types ◽  
Naval Hospital ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Eeg Pattern ◽  
Simultaneous Activity

<p><strong>ABSTRAK</strong></p><p><strong>Pendahuluan:</strong> Elektroensefalogram atau rekam kelistrikkan otak adalah metode dalam neurofisiologi yang telah terbukti dapat diaplikasikan dalam ilmu kedokteran. Berbagai tipe dari ritme otak yang simultan menunjukkan bahwa aktivitas dari neuron korteks otak bergantung pada kondisi status mental seseorang. <strong>Tujuan: </strong>untuk mengetahui pola gelombang EEG pasien di RSAL dr. Ramelan Surabaya. <strong>Metode:</strong> Penelitian ini merupakan penelitian deskriptif dengan metode studi prevalensi. Teknik pengambilan sampling adalah <em>Total Population </em>yaitu semua pasien yang melakukan pemeriksaan EEG yang didapat dari rekam medik  ruang EEG RSAL dr. Ramelan Surabaya periode Januari 2018 sampai Desember 2018 yang memenuhi kriteria inklusi dan eksklusi dari sampel. <strong>Hasil:</strong> Pasien yang paling sering melakukan pemeriksaan berdasarkan usia adalah usia balita, berdasarkan jenis kelamin adalah laki-laki, dan berdasarkan diagnosa klinis adalah konvulsif epilepsy dengan pola gelombang tersering yaitu SW.</p><p><strong>Kata kunci:</strong> elektroensefalogram, kelistrikkan otak, epilepsi.</p><p><strong>ABSTRACT</strong></p><p><strong>Introduction:</strong> An electroencephalogram (EEG) is an accepted method in neurophysiology with a wide application. Different types of brain rhythms indicate that simultaneous activity of the brain cortex neurons depend on the person’s mental state<span style="font-family: Calibri; font-size: medium;">.</span> <strong>Aim:</strong> to know the EEG pattern of patients at dr. Ramelan Naval Hospital Surabaya. <strong>Method:</strong> this research is a descriptive research with prevalence study method. Using total population for sampling, all patients who did the electroencephalogram examination at dr. Ramelan Naval Hospital Surabaya period January 2018 until December 2018. <strong>Result:</strong> Toddlers were the most frequently patients who did electroencephalogram examination. Based on sex, male were the most frequent. Based on clinical diagnose, patient with convulsive epilepsy are the most frequent with SW wave result.</p><p><strong>Keywords:</strong> electroencephalogram, brain activity, epilepsy.</p>

scholarly journals Differential Effects of Simulated Cortical Network Lesions on Synchrony and EEG Complexity

2019 ◽  
Vol 29 (04) ◽  
pp. 1850024 ◽  
Author(s):  
Antonio José Ibáñez-Molina ◽  
Sergio Iglesias-Parro ◽  
Javier Escudero
Keyword(s):  
Brain Function ◽  
Brain Activity ◽  
Spatial Location ◽  
Sample Entropy ◽  
Different Types ◽  
Kuramoto Oscillators ◽  
The Relationship ◽  
Electroencephalogram Eeg ◽  
The Brain

Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of brain activity is the characterization of the synchrony among these areas and the resulting complexity of the system. However, the variety of ways to define and, hence, measure brain synchrony and complexity has sometimes led to inconsistent results. Here, we study the relationship between synchrony and commonly used complexity estimators of electroencephalogram (EEG) activity and we explore how simulated lesions in anatomically based cortical networks would affect key functional measures of activity. We explored this question using different types of neural network lesions while the brain dynamics was modeled with a time-delayed set of 66 Kuramoto oscillators. Each oscillator modeled a region of the cortex (node), and the connectivity and spatial location between different areas informed the creation of a network structure (edges). Each type of lesion consisted on successive lesions of nodes or edges during the simulation of the neural dynamics. For each type of lesion, we measured the synchrony among oscillators and three complexity estimators (Higuchi’s Fractal Dimension, Sample Entropy and Lempel-Ziv Complexity) of the simulated EEGs. We found a general negative correlation between EEG complexity metrics and synchrony but Sample Entropy and Lempel-Ziv showed a positive correlation with synchrony when the edges of the network were deleted. This suggests an intricate relationship between synchrony of the system and its estimated complexity. Hence, complexity seems to depend on the multiple states of interaction between the oscillators of the system. Our results can contribute to the interpretation of the functional meaning of EEG complexity.

Decoding the coupling between the brain and skin reactions in auditory stimulation by information-based analysis of EEG and GSR signals

2021 ◽  
pp. 1-10
Author(s):  
Najmeh Pakniyat ◽  
Hamidreza Namazi
Keyword(s):  
Brain Activity ◽  
Skin Resistance ◽  
Research Area ◽  
Important Research ◽  
Skin Reactions ◽  
Analysis Technique ◽  
Information Contents ◽  
Important Research Area ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: The analysis of brain activity in different conditions is an important research area in neuroscience. OBJECTIVE: This paper analyzed the correlation between the brain and skin activities in rest and stimulations by information-based analysis of electroencephalogram (EEG) and galvanic skin resistance (GSR) signals. METHODS: We recorded EEG and GSR signals of eleven subjects during rest and auditory stimulations using three pieces of music that were differentiated based on their complexity. Then, we calculated the Shannon entropy of these signals to quantify their information contents. RESULTS: The results showed that music with greater complexity has a more significant effect on altering the information contents of EEG and GSR signals. We also found a strong correlation (r= 0.9682) among the variations of the information contents of EEG and GSR signals. Therefore, the activities of the skin and brain are correlated in different conditions. CONCLUSION: This analysis technique can be utilized to evaluate the correlation among the activities of various organs versus brain activity in different conditions.

Applying Independent Component Analysis to the Artifact Detection Problem in Magnetoencephalogram Background Recordings

2008 ◽  
pp. 84-92
Author(s):  
Javier Escudero ◽  
Roberto Hornero ◽  
Daniel Abásolo ◽  
Jesús Poza ◽  
Alberto Fernández
Keyword(s):  
Magnetic Fields ◽  
Quantum Interference ◽  
Brain Activity ◽  
External Noise ◽  
Low Noise ◽  
High Temporal Resolution ◽  
Artifact Detection ◽  
Mental Diseases ◽  
Electroencephalogram Eeg ◽  
The Brain

The analysis of the electromagnetic brain activity can provide important information to help in the diagnosis of several mental diseases. Both electroencephalogram (EEG) and magnetoencephalogram (MEG) record the neural activity with high temporal resolution (Hämäläinen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993). Nevertheless, MEG offers some advantages over EEG. For example, in contrast to EEG, MEG does not depend on any reference point. Moreover, the magnetic fields are less distorted than the electric ones by the skull and the scalp (Hämäläinen et al., 1993). Despite these advantages, the use of MEG data involves some problems. One of the most important difficulties is that MEG recordings may be severely contaminated by additive external noise due to the intrinsic weakness of the brain magnetic fields. Hence, MEG must be recorded in magnetically shielded rooms with low-noise SQUID (Superconducting QUantum Interference Devices) gradiometers (Hämäläinen et al., 1993).

Isolation of different types of synaptic membranes from the brain cortex

Life Sciences ◽  
1966 ◽  
Vol 5 (7) ◽  
pp. 577-582 ◽  
Author(s):  
E. De Robertis ◽  
Marta Alberici ◽  
Georgina Rodríguez de Lores Arnaiz ◽  
J.M. Azcurra
Keyword(s):  
Brain Cortex ◽  
Synaptic Membranes ◽  
Different Types ◽  
The Brain

scholarly journals Quantification of EEG Brain Activity in the Self-Paced Regime

2018 ◽  
Vol 4 (1) ◽  
pp. 1-8
Author(s):  
Joana Silva ◽  
A. Martins Da Silva ◽  
Luís Coelho
Keyword(s):  
Brain Activity ◽  
Rhythmic Activity ◽  
The Self ◽  
Automatic Quantification ◽  
Frequency Bands ◽  
Cerebral Activity ◽  
Cognitive Information ◽  
Specific Frequency ◽  
Electroencephalogram Eeg ◽  
The Brain

The processing of motor, sensory and cognitive information by the brain can result in changes of the electroencephalogram (EEG) by Event Related Desynchronization (ERD) or Event Related Synchronization (ERS). The first one concerns a decrease in the amplitude of a rhythmic activity while the second corresponds to its increase. The analysis of these two phenomena in specific frequency bands - alpha (8-13 Hz) and beta (14-30 Hz) - allows the understanding of the cerebral activity. This study focuses on the quantification of cerebral activity by determining the ERD and ERS on the referred band, induced by self-paced movements, by using EEGLAB and MATLAB tools. This was achieved by the creation of a new and automatic quantification algorithm. The results indicate that a greater desynchronization of the signal is accompanied by a decrease in the amplitude of the same. As a conclusion, the cerebral activity varies in terms of synchronization and desynchronization among certain frequency bands in several zones, according to the tasks performed.

scholarly journals Deep Learning on Game Addiction Detection Based on Electroencephalogram

2021 ◽  
Vol 5 (3) ◽  
pp. 963
Author(s):  
Lalu Arfi Maulana Pangistu ◽  
Ahmad Azhari
Keyword(s):  
Brain Activity ◽  
Late Adolescence ◽  
Wave Activity ◽  
Test Results ◽  
Brain Wave ◽  
Brain Waves ◽  
Game Addiction ◽  
Brain Wave Activity ◽  
Electroencephalogram Eeg ◽  
The Brain

Playing games for too long can be addictive. Based on a recent study by Brand et al, adolescents are considered more vulnerable than adults to game addiction. The activity of playing games produces a wave in the brain, namely beta waves where the person is in a focused state. Brain wave activity can be measured and captured using an Electroencephalogram (EEG). Recording brain wave activity naturally requires a prominent and constant brain activity such as when concentrating while playing a game. This study aims to detect game addiction in late adolescence by applying Convolutional Neural Network (CNN). Recording of brain waves was carried out three times for each respondent with a stimulus to play three different games, namely games included in the easy, medium, and hard categories with a consecutive taking time of 10 minutes, 15 minutes, and 30 minutes. Data acquisition results are feature extraction using Fast Fourier Transform to get the average signal for each respondent. Based on the research conducted, obtained an accuracy of 86% with a loss of 0.2771 where the smaller the loss value, the better the CNN model built. The test results on the model produce an overall accuracy of 88% with misclassification in 1 data. The CNN model built is good enough for the detection of game addiction in late adolescence. 

scholarly journals Peculiarities of organization of bioelectric brain activity in subclinical stage of epilepsy

2017 ◽  
Vol 25 (3) ◽  
pp. 399-403
Author(s):  
V. V. Sychev ◽  
V. N. Sychev ◽  
N. V. Shatrova
Keyword(s):  
Fourier Analysis ◽  
Right Hemisphere ◽  
Brain Activity ◽  
Epileptiform Activity ◽  
Clinical Manifestations ◽  
Bioelectric Activity ◽  
Left And Right ◽  
Electroencephalogram Eeg ◽  
The Brain

According to some authors, changes in the electroencephalogram (EEG) in the absence of clinical paroxysmal manifestations should be considered as subclinical epileptic manifestations. Verification of this hypothesis on the basis of the auto-spectral Fourier analysis of the EEG was the purpose of this work. Were examined in 27 women, mean age of 35.4±2.48 years, right-handed, without paroxysmal clinical and EEG manifestations (first group) and 25 women, mean age of 36.2±2.17 years, right-handed, without paroxysmal clinical manifestations, but with epileptiform activity on EEG (second group). In the second group were registered the increase in faverage of the brain EEG (p<0.001), while was increased faverage both of the left and right hemisphere (p<0.01). Zonal peculiarities of bioelectric activity of a brain of the second group surveyed was a significant increase in faverage EEG in all investigated leads (p<0.01), resulting in total liquidation of zonal differences (p>0.05). The results of the analysis allowed to conclude that the registration of the EEG epileptiform paroxysmal phenomena without clinical manifestations should be considered as a subclinical stage of epilepsy.

DECODING OF SIMPLE AND COMPOUND LIMB MOTOR IMAGERY MOVEMENTS BY FRACTAL ANALYSIS OF ELECTROENCEPHALOGRAM (EEG) SIGNAL

Fractals ◽  
2019 ◽  
Vol 27 (03) ◽  
pp. 1950041 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
TIRDAD SEIFI ALA
Keyword(s):  
Fractal Dimension ◽  
Motor Imagery ◽  
Fractal Analysis ◽  
Brain Activity ◽  
Eeg Signal ◽  
Left Hand ◽  
Right Hand ◽  
Significant Difference ◽  
Electroencephalogram Eeg ◽  
The Brain

One of the major attempts in rehabilitation science is to decode different movements of human using physiological signals. Since human movements are mainly controlled by the brain, decoding of movements by analysis of the brain activity has great importance. In this paper, we apply fractal analysis to Electroencephalogram (EEG) signal in order to decode simple and compound limb motor imagery movements. The fractal dimension of EEG signal is analyzed in case of left hand, right hand, both hands, feet, left hand combined with right foot, and right hand combined with left foot movements. Based on the obtained results, EEG signal experiences the lowest and greatest fractal dimension in case of both hands movement, and feet movement, respectively. Besides obtaining different fractal dimension for EEG signal in case of different movements, no significant difference was observed in fractal dimension of EEG signal between different movements. The method of analysis employed in this research can be widely applied to analysis of EEG signal for decoding of different movements of human.

scholarly journals Introduction to the identification of brain waves based on their frequency

2018 ◽  
Vol 210 ◽  
pp. 05012 ◽  
Author(s):  
Zuzana Koudelková ◽  
Martin Strmiska
Keyword(s):  
Brain Activity ◽  
Computer Interface ◽  
Brain Wave ◽  
Non Invasive ◽  
Brain Waves ◽  
Analytical Reasoning ◽  
Brain Signals ◽  
Electroencephalogram Eeg ◽  
The Brain ◽  
Research Type

A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

scholarly journals Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2499 ◽  
Author(s):  
Yue Gu ◽  
Zhenhu Liang ◽  
Satoshi Hagihira
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Brain Activity ◽  
Permutation Entropy ◽  
Depth Of Anesthesia ◽  
Spectral Edge Frequency ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Electroencephalogram Eeg ◽  
The Brain

The electroencephalogram (EEG) can reflect brain activity and contains abundant information of different anesthetic states of the brain. It has been widely used for monitoring depth of anesthesia (DoA). In this study, we propose a method that combines multiple EEG-based features with artificial neural network (ANN) to assess the DoA. Multiple EEG-based features can express the states of the brain more comprehensively during anesthesia. First, four parameters including permutation entropy, 95% spectral edge frequency, BetaRatio and SynchFastSlow were extracted from the EEG signal. Then, the four parameters were set as the inputs to an ANN which used bispectral index (BIS) as the reference output. 16 patient datasets during propofol anesthesia were used to evaluate this method. The results indicated that the accuracies of detecting each state were 86.4% (awake), 73.6% (light anesthesia), 84.4% (general anesthesia), and 14% (deep anesthesia). The correlation coefficient between BIS and the index of this method was 0.892 ( p < 0.001 ). The results showed that the proposed method could well distinguish between awake and other anesthesia states. This method is promising and feasible for a monitoring system to assess the DoA.

Sign in / Sign up

Export Citation Format

Share Document