Focal and Non-Focal Epilepsy Localization: A Review

Hussein, Ahmed Faeq; Arunkumar, N.; Gomes, Chandima; AlZubaidi, Abbas K.; Habash, Qais Ahmed; Santamaría-Granados, Luz; Mendoza-Moreno, Juan Francisco; Ramírez-González, Gustavo

doi:10.1109/access.2018.2867078

Cited by 50 publications

(15 citation statements)

References 174 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The deep learning starts from the original data to transform each layer of representation (or feature) layer by layer into a higher level and more abstract representation, thus finding a complex structure in the high-dimensional data. 13 High-dimensional eigenvectors provide data basis for clustering analysis and association analysis. Then the dimensionality reduction analysis, such as multidimensional scale analysis and self-organizing mapping, is used to reduce the dimension of its dimension, which facilitates the intelligence analysts to observe the internal association of a large number of terrorist activities from the low dimension (2 dimensional/3 dimensional) angle.…”

Section: Prediction Analysismentioning

confidence: 99%

Research on urban anti-terrorism intelligence perception system from the perspective of Internet of things application

Meng

Nie

Song

2019

The International Journal of Electrical Engineering & Educa

View full text Add to dashboard Cite

Objective/significance Based on the urgent needs of the status quo of urban anti-terrorism, combining the present situation of intelligent city construction, we carry out the research on the key problems such as the collection and analysis of counter-terrorism information for urban environment. This paper puts forward a kind of city anti-terrorism information perception system based on the Internet of things, and realizes to improve the level of surveillance and early warning of terrorist activities facing the urban environment. Method/Process The system collects and merges the heterogeneous information of terrorism, which is based on the information perception layer, the intelligence identification layer and the intelligence inference layer, which is based on the city's focal area. To filter suspicious information by constructing a semantic database of terrorist action signals Combined with complex event processing and machine learning technology, real-time monitoring and mining prediction of mass event flow, using the comparison between historical data of terrorist attacks and real-time data, increased the area of counter-terrorism intelligence. Results/Conclusions We hope to provide a better model and an exploratory study on the construction of the counter-terrorism early warning for the urban environment.

show abstract

Section: Prediction Analysismentioning

confidence: 99%

Research on urban anti-terrorism intelligence perception system from the perspective of Internet of things application

Meng

Nie

Song

2019

The International Journal of Electrical Engineering & Educa

View full text Add to dashboard Cite

show abstract

“…Previous studies have been utilizing time do main, frequency domain and time -frequency domain depending on the study domain and objectives. Traditionally, the features extraction is considered the most significant step in EEG signal assessment [14].…”

Section: Eeg Signal Processingmentioning

confidence: 99%

Examination of Prefrontal Cortex Activity After EEG-Neurofeedback Stimulation in Overweight Cases

Al-Hiyali

Aris

Harun

et al. 2019

IJIE

View full text Add to dashboard Cite

The prevalence of excess weight individuals has increased noticeably in Malaysian societies. Recently, the Institute for Public Health in Malaysia (IPH) has reported that around 45% of the Malaysian population are excess weight cases[1]. The ever-increasing number of excess weight cases in societies is commonly due to excessive eating and lack of physical activity. Another effect factor is the increase in eating of high caloric food rich in sugars and fat. Beside excessive eating of fat and sugar, daily meal types have changed over the last decades, noting a trend for increased meal frequency (i.e. snacking behavior)[2]. Snacking has been proposed to weight increase as well as to its metabolic rate. Thus, a decrease in physical activity, an increase in calorie food rate, and snacking behavior all result in excess weight [3]. The prevalence of excess weight individuals can be said to have reached high sizes thus evidencing that at least an overweight case occurs because of an inability to regulate their own eating behavior which is a key to weight gain prevention. Eating regulation is a complex process involving internal factors such as genetics, neural, and endocrine signals, as well as external factors including the environmental factors that excite eating desire such as sight, smell , and taste[4]. However, studies have shown that specific areas of the brain are involved in the interactiv e processing of food vs. nonfood-related visual stimuli in the different states of hunger and satiety. These include the PreFrontal Cortex (PFC) and the amygdala. Another study shows that food, even when presented only as an image, will cause a larger CNS

show abstract

“…one hemisphere of the brain. On the other hand, non-focal epilepsy affects multiple areas within the brain even though they were not affected directly by the seizures [1], [3]. The majority of epileptic patients (60%) become pharmacoresistant, that is not responding to medications.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, inter-ictal EEG is represented by the presence of temporary sharp waves. Sometimes, clinicians use long-term intercranial EEG recording to detect deeper signals and localize the source of the seizure within the brain [3], [5]. Therefore, visual identification of focal and non-focal epileptic EEG signals is considered time consuming for medical doctors.…”

Section: Introductionmentioning

confidence: 99%

Classification of Focal and Non-Focal Epileptic Patients Using Single Channel EEG and Long Short-Term Memory Learning System

Fraiwan

Alkhodari

2020

IEEE Access

View full text Add to dashboard Cite

The process of inspecting electroencephalography (EEG) signals of patients with epilepsy to distinguish between focal and non-focal seizure source is a crucial step prior to surgical interference. In this paper, a deep learning approach using a long short-term memory (LSTM) algorithm is investigated for the purpose of automatic discrimination between focal and non-focal epileptic EEG signals. The study is carried out by acquiring 7500 pairs of x and y EEG channels signals from the publicly available Bern-Barcelona EEG database. The manual classification of each signal type was visually done by two board-certified electroencephalographers and neurologists. Initially, every channel signals are pre-processed using z-score normalization and Savitzky-Golay filtering. The signals are used as inputs to a pre-defined Bi-directional LSTM algorithm for the training process. The classification is performed using a k-fold cross-validation following 4-, 6-, and 10-fold schemes. At the end, the performance of the algorithm is evaluated using several metrics with a complete summary table of the recent state-of-art studies in the field. The developed algorithm achieved an overall Cohen's kappa κ, accuracy, sensitivity, and specificity values of 99.20%, 99.60%, 99.55%. and 99.65%, respectively, using x channels and 10-fold cross-validation scheme. The study pave the ways toward implementing deep learning algorithms for the purpose of EEG signals identification in a clinical environment to overcome human errors resulting from visually inspection.

show abstract

Focal and Non-Focal Epilepsy Localization: A Review

Cited by 50 publications

References 174 publications

Research on urban anti-terrorism intelligence perception system from the perspective of Internet of things application

Research on urban anti-terrorism intelligence perception system from the perspective of Internet of things application

Examination of Prefrontal Cortex Activity After EEG-Neurofeedback Stimulation in Overweight Cases

Classification of Focal and Non-Focal Epileptic Patients Using Single Channel EEG and Long Short-Term Memory Learning System

Contact Info

Product

Resources

About