Classification of Epilepsy

2021 ◽  
pp. 705-718
Author(s):  
Jamal F. Khattak ◽  
David B. Burkholder
Keyword(s):  
Differential Diagnosis ◽  
Neuronal Activity ◽  
Initial Approach ◽  
Key Characteristics ◽  
The Brain

An understanding of the definitions, classification, and key characteristics of seizures and epilepsy is vital in the initial approach to a patient presenting with seizures. Classifying seizures appropriately helps narrow the differential diagnosis and guide further testing, treatment, and prognosis. This chapter reviews the basic definitions and classifications of seizures and epilepsy and summarizes select epilepsy syndromes. A seizure is a transient occurrence of signs or symptoms due to abnormal, excessive, or synchronous neuronal activity in the brain.

Seizures

2018 ◽  
Author(s):  
Dirk Bäumer
Keyword(s):  
Differential Diagnosis ◽  
Neuronal Activity ◽  
Brain Region ◽  
Diagnostic Tests ◽  
Partial Seizures ◽  
Complex Partial Seizures ◽  
Focal Seizures ◽  
Generalized Seizures ◽  
The Brain

Seizures are transient neurological events caused by abnormal excessive or synchronous neuronal activity in the brain. This can arise from a localized brain region, causing focal seizures, or simultaneously from both hemispheres, leading to generalized seizures. Epilepsy is the tendency to develop recurrent seizures and is usually diagnosed after two or more unprovoked seizures. This chapter covers simple partial seizures (sometimes called aura), complex partial seizures, and focal (or partial) seizures, their differential diagnosis, context, approach to diagnosis, key diagnostic tests, therapy, and prognosis, as well as dealing with uncertainty in a diagnosis.

scholarly journals Neuronal activity causes a reciprocal cationic flux in the extracellular space in the brain: a hypothesis

2014 ◽  
Author(s):  
Nick J Beaumont
Keyword(s):  
Neuronal Activity ◽  
Extracellular Space ◽  
Action Potentials ◽  
Traumatic Injury ◽  
The Body ◽  
Potassium Ions ◽  
Key Characteristics ◽  
The Brain ◽  
Sodium And Potassium ◽  
Cationic Flux

The fluid in the extracellular space around the neurons and glial cells is enclosed within the brain, kept separate from the circulation and the rest of the body-fluid. This brain interstitial fluid forms a distinct compartment; a sponge-like “inverse cell” that surrounds all the cells. During neuronal resting and action potentials, sodium and potassium ions shuttle into, and out of, this “Reciprocal Domain” within the brain. This localised flux of ions is the counterpart to all the neuronal electrochemical activity (having the same intensity and duration, at the same sites in the brain), so a complementary version of all that potential information is integrated into this space within the brain. This flux of cations in the Reciprocal Domain may indirectly influence neuronal activity in the brain, creating immensely complex feedback. This Reciprocal Domain is unified throughout the brain, and exists continuously throughout life. This model identifies which species have such Reciprocal Domains, and how many times similar systems evolved. This account of the Reciprocal Domain of the brain may have clinical implications; it could be vulnerable to disruption by chemical insult, traumatic injury or pathology. These are key characteristics of our core selves; this encourages the idea that this Reciprocal Domain makes a crucial contribution to the brain. This hypothesis is explored and developed here.

scholarly journals Neuronal activity causes a reciprocal cationic flux in the extracellular space in the brain: a hypothesis

2015 ◽  
Author(s):  
Nick J Beaumont
Keyword(s):  
Neuronal Activity ◽  
Extracellular Space ◽  
Action Potentials ◽  
Traumatic Injury ◽  
The Body ◽  
Potassium Ions ◽  
Key Characteristics ◽  
The Brain ◽  
Sodium And Potassium ◽  
Cationic Flux

The fluid in the extracellular space around the neurons and glial cells is enclosed within the brain, kept separate from the circulation and the rest of the body-fluid. This brain interstitial fluid forms a distinct compartment; a sponge-like “inverse cell” that surrounds all the cells. During neuronal resting and action potentials, sodium and potassium ions shuttle into, and out of, this “Reciprocal Domain” within the brain. This localised flux of ions is the counterpart to all the neuronal electrochemical activity (having the same intensity and duration, at the same sites in the brain), so a complementary version of all that potential information is integrated into this space within the brain. This flux of cations in the Reciprocal Domain may indirectly influence neuronal activity in the brain, creating immensely complex feedback. This Reciprocal Domain is unified throughout the brain, and exists continuously throughout life. This model identifies which species have such Reciprocal Domains, and how many times similar systems evolved. This account of the Reciprocal Domain of the brain may have clinical implications; it could be vulnerable to disruption by chemical insult, traumatic injury or pathology. These are key characteristics of our core selves; this encourages the idea that this Reciprocal Domain makes a crucial contribution to the brain. This hypothesis is explored and developed here.

Classification of papillary thyroid carcinoma histological images based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Yaning Liu ◽  
Lin Han ◽  
Hexiang Wang ◽  
Bo Yin
Keyword(s):  
Neural Network ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Image Features ◽  
Papillary Thyroid ◽  
Histological Image ◽  
Histological Images

Papillary thyroid carcinoma (PTC) is a common carcinoma in thyroid. As many benign thyroid nodules have the papillary structure which could easily be confused with PTC in morphology. Thus, pathologists have to take a lot of time on differential diagnosis of PTC besides personal diagnostic experience and there is no doubt that it is subjective and difficult to obtain consistency among observers. To address this issue, we applied deep learning to the differential diagnosis of PTC and proposed a histological image classification method for PTC based on the Inception Residual convolutional neural network (IRCNN) and support vector machine (SVM). First, in order to expand the dataset and solve the problem of histological image color inconsistency, a pre-processing module was constructed that included color transfer and mirror transform. Then, to alleviate overfitting of the deep learning model, we optimized the convolution neural network by combining Inception Network and Residual Network to extract image features. Finally, the SVM was trained via image features extracted by IRCNN to perform the classification task. Experimental results show effectiveness of the proposed method in the classification of PTC histological images.

scholarly journals Wireless logging of extracellular neuronal activity in the telencephalon of free-swimming salmonids

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Susumu Takahashi ◽  
Takumi Hombe ◽  
Riku Takahashi ◽  
Kaoru Ide ◽  
Shinichiro Okamoto ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Environmental Cues ◽  
Fish Movement ◽  
Water Tank ◽  
Head Direction ◽  
Free Swimming ◽  
Rodent Brain ◽  
River Migration ◽  
Spike Waveforms ◽  
The Brain

Abstract Background Salmonids return to the river where they were born in a phenomenon known as mother-river migration. The underpinning of migration has been extensively examined, particularly regarding the behavioral correlations of external environmental cues such as the scent of the mother-river and geomagnetic compass. However, neuronal underpinning remains elusive, as there have been no biologging techniques suited to monitor neuronal activity in the brain of large free-swimming fish. In this study, we developed a wireless biologging system to record extracellular neuronal activity in the brains of free-swimming salmonids. Results Using this system, we recorded multiple neuronal activities from the telencephalon of trout swimming in a rectangular water tank. As proof of principle, we examined the activity statistics for extracellular spike waveforms and timing. We found cells firing maximally in response to a specific head direction, similar to the head direction cells found in the rodent brain. The results of our study suggest that the recorded signals originate from neurons. Conclusions We anticipate that our biologging system will facilitate a more detailed investigation into the neural underpinning of fish movement using internally generated information, including responses to external cues.

scholarly journals An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

2021 ◽  
Author(s):  
Muhammad Irfan Sharif ◽  
Jian Ping Li ◽  
Javeria Amin ◽  
Abida Sharif
Keyword(s):  
Brain Tumor ◽  
Tumor Detection ◽  
Entropy Method ◽  
Feature Extraction And Selection ◽  
Tumor Region ◽  
Selection For ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Complicated Task

AbstractBrain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

scholarly journals fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task

2021 ◽  
Vol 11 (11) ◽  
pp. 4922
Author(s):  
Tengfei Ma ◽  
Wentian Chen ◽  
Xin Li ◽  
Yuting Xia ◽  
Xinhua Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Total Duration ◽  
Signal Classification ◽  
Imagery Task ◽  
Neural Activities ◽  
Computer Interfaces ◽  
Acquisition Device ◽  
The Time Domain ◽  
The Brain

To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can achieve 97% accuracy in RPS classification. CNN-based TSC method is suitable for the classification of fNIRS signals in RPS motor imagery tasks, and may find new application directions for the development of brain–computer interfaces (BCI).

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

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