scholarly journals Update to the dataset of cerebral ischemia in juvenile pigs with evoked potentials

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin G. Frasch ◽  
Bernd Walter ◽  
Christoph Anders ◽  
Reinhard Bauer
Keyword(s):  
Evoked Potentials ◽  
Data Set ◽  
Maxillary Nerve ◽  
High Frequency Oscillations ◽  
Full Band ◽  
Technical Validation ◽  
Monitoring Technologies ◽  
Neurological Injuries ◽  
Electroencephalogram Eeg ◽  
Electrical Activities

AbstractWe expand from a spontaneous to an evoked potentials (EP) data set of brain electrical activities as electrocorticogram (ECoG) and electrothalamogram (EThG) in juvenile pig under various sedation, ischemia and recovery states. This EP data set includes three stimulation paradigms: auditory (AEP, 40 and 2000 Hz), sensory (SEP, left and right maxillary nerve) and high-frequency oscillations (HFO) SEP. This permits derivation of electroencephalogram (EEG) biomarkers of corticothalamic communication under these conditions. The data set is presented in full band sampled at 2000 Hz. We provide technical validation of the evoked responses for the states of sedation, ischemia and recovery. This extended data set now permits mutual inferences between spontaneous and evoked activities across the recorded modalities. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries.

scholarly journals Multimodal pathophysiological dataset of gradual cerebral ischemia in a cohort of juvenile pigs

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin G. Frasch ◽  
Bernd Walter ◽  
Christophe L. Herry ◽  
Reinhard Bauer
Keyword(s):  
Brain Injuries ◽  
Experimental Conditions ◽  
Data Set ◽  
High Frequency Oscillations ◽  
Full Band ◽  
Technical Validation ◽  
Monitoring Technologies ◽  
Neurological Injuries ◽  
Electrocardiogram Ecg ◽  
Electrical Activities

AbstractIschemic brain injuries are frequent and difficult to detect reliably or early. We present the multi-modal data set containing cardiovascular (blood pressure, blood flow, electrocardiogram) and brain electrical activities to derive electroencephalogram (EEG) biomarkers of corticothalamic communication under normal, sedation, and hypoxic/ischemic conditions with ensuing recovery. We provide technical validation using EEGLAB. We also delineate the corresponding changes in the electrocardiogram (ECG)-derived heart rate variability (HRV) with the potential for future in-depth analyses of joint EEG-ECG dynamics. We review an open-source methodology to derive signatures of coupling between the ECoG and electrothalamogram (EThG) signals contained in the presented data set to better characterize the dynamics of thalamocortical communication during these clinically relevant states. The data set is presented in full band sampled at 2000 Hz, so the additional potential exists for insights from the full-band EEG and high-frequency oscillations under the bespoke experimental conditions. Future studies on the dataset may contribute to the development of new brain monitoring technologies, which will facilitate the prevention of neurological injuries.

scholarly journals Time-ResNeXt for epilepsy recognition based on EEG signals in wireless networks

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shaoqiang Wang ◽  
Shudong Wang ◽  
Song Zhang ◽  
Yifan Wang
Keyword(s):  
Deep Learning ◽  
Network Structure ◽  
Signal Recognition ◽  
Eeg Signals ◽  
Real World Data ◽  
Data Set ◽  
Practical Applications ◽  
Epilepsy Diagnosis ◽  
Single Data ◽  
Electroencephalogram Eeg

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

Electroencephalogram activity in the anoxic turtle brain

1997 ◽  
Vol 273 (3) ◽  
pp. R911-R919 ◽  
Author(s):  
J. A. Fernandes ◽  
P. L. Lutz ◽  
A. Tannenbaum ◽  
A. T. Todorov ◽  
L. Liebovitch ◽  
...  
Keyword(s):  
Transition Period ◽  
Activity Level ◽  
Sharp Waves ◽  
Energy Demands ◽  
Order Of Magnitude ◽  
Activity State ◽  
Low Amplitude ◽  
Electroencephalogram Eeg ◽  
Electrical Activities ◽  
The Brain

The anoxia-tolerant turtle brain slowly undergoes a complex sequence of changes in electroencephalogram (EEG) activity as the brain systematically downregulates its energy demands. Following N2 respiration, the root mean square voltage rapidly fell, reaching approximately 20% of normoxic levels after approximately 100 min of anoxia. During the first 20- to 40-min transition period, the power of the EEG decreased substantially, particularly in the 12- to 24-Hz band, with low-amplitude slow wave activity predominating (3-12 Hz). Bursts of high voltage rhythmic slow (approximately 3-8 Hz) waves were seen during the 20- to 100-min period of anoxia, accompanied by large sharp waves. During the next 400 min of N2 respiration, two distinct patterns of electrical activity characterized the anoxic turtle brain: 1) a sustained but depressed activity level, with an EEG amplitude approximately 20% of the normoxic control and with total EEG power reduced by one order of magnitude at all frequencies, and 2) short (3-15 s) periodic (0.5-2/min) bursts of mixed-frequency activity that interrupted the depressed activity state. We speculate that the EEG patterns seen during sustained anoxia represent the minimal or basic electrical activities that are compatible with the survival of the anoxic turtle brain as an integrated unit, which allow the brain to return to normal functioning when air respiration resumed.

scholarly journals Patient-specific optimization of automated detection improves seizure onset zone localization based on high frequency oscillations

2020 ◽  
Author(s):  
Casey L. Trevino ◽  
Jack J. Lin ◽  
Indranil Sen-Gupta ◽  
Beth A. Lopour
Keyword(s):  
High Frequency ◽  
Classification Accuracy ◽  
Patient Specific ◽  
Seizure Onset ◽  
Data Set ◽  
Localization Accuracy ◽  
High Frequency Oscillations ◽  
Seizure Onset Zone ◽  
Wide Range ◽  
The Impact

AbstractHigh frequency oscillations (HFOs) are a promising biomarker of epileptogenicity, and automated algorithms are critical tools for their detection. However, previously validated algorithms often exhibit decreased HFO detection accuracy when applied to a new data set, if the parameters are not optimized. This likely contributes to decreased seizure localization accuracy, but this has never been tested. Therefore, we evaluated the impact of parameter selection on seizure onset zone (SOZ) localization using automatically detected HFOs. We detected HFOs in intracranial EEG from twenty medically refractory epilepsy patients with seizure free surgical outcomes using an automated algorithm. For each patient, we assessed classification accuracy of channels inside/outside the SOZ using a wide range of detection parameters and identified the parameters associated with maximum classification accuracy. We found that only three out of twenty patients achieved maximal localization accuracy using conventional HFO detection parameters, and optimal parameter ranges varied significantly across patients. The parameters for amplitude threshold and root-mean-square window had the greatest impact on SOZ localization accuracy; minimum event duration and rejection of false positive events did not significantly affect the results. Using individualized optimal parameters led to substantial improvements in localization accuracy, particularly in reducing false positives from non-SOZ channels. We conclude that optimal HFO detection parameters are patient-specific, often differ from conventional parameters, and have a significant impact on SOZ localization. This suggests that individual variability should be considered when implementing automatic HFO detection as a tool for surgical planning.

scholarly journals Analysis of EEG signals using Machine Learning for the Detection and Diagnosis of Epilepsy

2020 ◽  
Vol 10 (1) ◽  
pp. 89-93
Keyword(s):  
Machine Learning ◽  
Epileptic Seizures ◽  
Severe Damage ◽  
Tree Model ◽  
Data Set ◽  
Feature Extraction Method ◽  
Precautionary Measures ◽  
Detection And Diagnosis ◽  
Electroencephalogram Eeg ◽  
Mean Variance

Electroencephalogram (EEG) is one of the most commonly used tools for epilepsy detection. In this paper we have presented two methods for the diagnosis of epilepsy using machine learning techniques.EEG waveforms have five different kinds of frequency bands. Out of which only two namely theta and gamma bands carry epileptic seizure information. Our model determines the statistical features like mean, variance, maximum, minimum, kurtosis, and skewness from the raw data set. This reduces the mathematical complexities and time consumption of the feature extraction method. It then uses a Logistic regression model and decision tree model to classify whether a person is epileptic or not. After the implementation of the machine learning models, parameters like accuracy, sensitivity, and recall have been found. The results for the same are analyzed in detail in this paper. Epileptic seizures cause severe damage to the brain which affects the health of a person. Our key objective from this paper is to help in the early prediction and detection of epilepsy so that preventive interventions can be provided and precautionary measures are taken to prevent the patient from suffering any severe damage

Seismic resolution enhancement in shale-oil reservoirs

Geophysics ◽  
2018 ◽  
Vol 83 (5) ◽  
pp. B281-B287 ◽  
Author(s):  
Xiwu Liu ◽  
Fengxia Gao ◽  
Yuanyin Zhang ◽  
Ying Rao ◽  
Yanghua Wang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Oil Reservoirs ◽  
Inversion Method ◽  
Shale Oil ◽  
Data Set ◽  
Extension Method ◽  
Seismic Resolution ◽  
Full Band ◽  
Shale Oil Reservoirs

We developed a case study of seismic resolution enhancement for shale-oil reservoirs in the Q Depression, China, featured by rhythmic bedding. We proposed an innovative method for resolution enhancement, called the full-band extension method. We implemented this method in three consecutive steps: wavelet extraction, filter construction, and data filtering. First, we extracted a constant-phase wavelet from the entire seismic data set. Then, we constructed the full-band extension filter in the frequency domain using the least-squares inversion method. Finally, we applied the band extension filter to the entire seismic data set. We determined that this full-band extension method, with a stretched frequency band from 7–70 to 2–90 Hz, may significantly enhance 3D seismic resolution and distinguish reflection events of rhythmite groups in shale-oil reservoirs.

scholarly journals Statistical Method to Extract Evoked Potentials from Noise

2020 ◽  
Vol 170 ◽  
pp. 02005
Author(s):  
Besma Benchabane ◽  
Moncef Benkherrat ◽  
Salah Djelel
Keyword(s):  
Wavelet Transform ◽  
Evoked Potentials ◽  
A Priori ◽  
Brain Regions ◽  
Statistical Test ◽  
A Priori Information ◽  
Time Frequency ◽  
Priori Information ◽  
Electrical Activities ◽  
The Brain

Evoked Potentials are induced by visual or auditory stimulation. The Evoked Potentials represent transient electrical activities of some limited brain regions. The signal-noise ratio (SNR) of the EPs is typically around -10 dB. In order to study brain activities related to information processing in the brain, one has to “extract” the single EPs from the noise. We propose a method does not require a priori information concerning the characteristics (time, frequency) of the signal and does not use a template. The method proposed in this work use the wavelet transform associated with a statistical test.

High frequency oscillations in early cortical somatosensory evoked potentials

1998 ◽  
Vol 108 (6) ◽  
pp. 536-542 ◽  
Author(s):  
Isamu Ozaki ◽  
Chieko Suzuki ◽  
Yukoh Yaegashi ◽  
Masayuki Baba ◽  
Muneo Matsunaga ◽  
...  
Keyword(s):  
Evoked Potentials ◽  
Somatosensory Evoked Potentials ◽  
High Frequency ◽  
High Frequency Oscillations

P300 Feature Extraction of Visual and Auditory Evoked EEG Signal

2014 ◽  
Vol 490-491 ◽  
pp. 1374-1377 ◽  
Author(s):  
Xiao Yan Qiao ◽  
Jia Hui Peng
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Evoked Potentials ◽  
Brain Computer Interface ◽  
Auditory Stimulation ◽  
Computer Interface ◽  
Eeg Signal ◽  
Significant Issue ◽  
Eeg Signals ◽  
Electroencephalogram Eeg

It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wavelet transform. Finally, the approximate coefficients of the fifth layer were reconstructed to extract P300 feature. The results have shown that the method can effectively extract the P300 feature under the different visual-auditory stimulation modes and lay a foundation for processing visual-auditory evoked EEG signals under the different mental tasks.

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