Automated analysis of brain activity for seizure detection in zebrafish models of epilepsy

2017 ◽  
Vol 287 ◽  
pp. 13-24 ◽  
Author(s):  
Borbála Hunyadi ◽  
Aleksandra Siekierska ◽  
Jo Sourbron ◽  
Daniëlle Copmans ◽  
Peter A.M. de Witte
2020 ◽  
Author(s):  
Ying Wang ◽  
Ivan C Zibrandtsen ◽  
Richard HC Lazeron ◽  
Johannes P van Dijk ◽  
Xi Long ◽  
...  

AbstractObjectiveElectroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis are still not reliable for the diagnosis of non-convulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided.MethodsWe analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) were visually analyzed by two independent raters. We investigated whether unreliable EEG visual interpretations quantified by low inter-rater agreement can be predicted by the characteristics of ictal discharges and individuals’ clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, two epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis.ResultsShort ictal discharges with a gradual onset (developing over 3 seconds in length) were liable to be misinterpreted. An extra 2 minutes of ictal discharges contributed to an increase in the kappa statistics of > 0.1. Other problems were the misinterpretation of abnormal background activity (slow wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges.ConclusionA longer duration criterion for NCSE-EEGs than 10 seconds that commonly used in NCSE working criteria is needed. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.


2021 ◽  
pp. 155005942110504
Author(s):  
Ying Wang ◽  
Ivan C. Zibrandtsen ◽  
Richard H. C. Lazeron ◽  
Johannes P. van Dijk ◽  
Xi Long ◽  
...  

Objective: Electroencephalography (EEG) interpretations through visual (by human raters) and automated (by computer technology) analysis were still not reliable for the diagnosis of nonconvulsive status epilepticus (NCSE). This study aimed to identify typical pitfalls in the EEG analysis and make suggestions as to how those pitfalls might be avoided. Methods: We analyzed the EEG recordings of individuals who had clinically confirmed or suspected NCSE. Epileptiform EEG activity during seizures (ictal discharges) was visually analyzed by 2 independent raters. We investigated whether unreliable EEG visual interpretations quantified by low interrater agreement can be predicted by the characteristics of ictal discharges and individuals’ clinical data. In addition, the EEG recordings were automatically analyzed by in-house algorithms. To further explore the causes of unreliable EEG interpretations, 2 epileptologists analyzed EEG patterns most likely misinterpreted as ictal discharges based on the differences between the EEG interpretations through the visual and automated analysis. Results: Short ictal discharges with a gradual onset (developing over 3 s in length) were liable to be misinterpreted. An extra 2 min of ictal discharges contributed to an increase in the kappa statistics of >0.1. Other problems were the misinterpretation of abnormal background activity (slow-wave activities, other abnormal brain activity, and the ictal-like movement artifacts), continuous interictal discharges, and continuous short ictal discharges. Conclusion: A longer duration criterion for NCSE-EEGs than 10 s that is commonly used in NCSE working criteria is recommended. Using knowledge of historical EEGs, individualized algorithms, and context-dependent alarm thresholds may also avoid the pitfalls.


Author(s):  
Rekh Ram Janghel ◽  
Yogesh Kumar Rathore ◽  
Gautam Tatiparti

Epilepsy is a brain ailment identified by unpredictable interruptions of normal brain activity. Around 1% of mankind experience epileptic seizures. Around 10% of the United States population experiences at least a single seizure in their life. Epilepsy is distinguished by the tendency of the brain to generate unexpected bursts of unusual electrical activity that disrupts the normal functioning of the brain. As seizures usually occur rarely and are unforeseeable, seizure recognition systems are recommended for seizure detection during long-term electroencephalography (EEG). In this chapter, ANN models, namely, BPA, RNN, CL, PNN, and LVQ, have been implemented. A prominent dataset was employed to assess the proposed method. The proposed method is capable of achieving an accuracy of 97.5%; the high accuracy obtained has confirmed the great success of the method.


IBRO Reports ◽  
2019 ◽  
Vol 6 ◽  
pp. S522
Author(s):  
Armen Sargsyan ◽  
Pablo Casillas-Espinosa ◽  
Wayne Frankel ◽  
Dmitri Melkonian ◽  
Terence O’Brien

2018 ◽  
Author(s):  
Zachary Huang ◽  
Yiming Ying

Epilepsy is a devastating neurological disorder that affects approximately 1-3% population worldwide. Wearables have recently gained more popularity as having a promising future in epilepsy management including seizure alert and close-loop therapy for severe forms of seizures. Due to the random and low frequency of seizures, seizure evaluation requires continuous, long-term EEG monitoring, which produces a large volume of data. The future treatment systems will rely on algorithms that can detect seizures with high precision and low computational cost. Previous implemented algorithms have used various computationally expensive methods of data transformation and feature extraction. In the present study, we developed a new computationally efficient seizure detection algorithm based on analysis of the broad, global shape EEG data of tonic-clonic seizures from a mouse model of temporal lobe epilepsy (TLE). To perform this algorithm, EEG data was normalized and processed through a rolling mean function, producing smoothed, simplified EEG clips that represent the global shape of the clip. These signals were then directly inputted for SVM training and testing. This novel method of seizure analysis only requires a small fraction of EEG data points, yet achieved an accuracy rate of approximately 98.51%. Our study provides a proof of principle that this simpler method could have an advantage in low-power platform such as wearables. Recently, the FDA approved a seizure detection app called embrace2 by Empatica. However, their algorithm detects seizures indirectly by monitoring heart rate and muscle contractions. Our novel algorithm detects seizures directly through analysis of brain activity. Thus, our algorithm may be better suited for future wearables in epilepsy management.


2019 ◽  
Vol 33 (2) ◽  
pp. 109-118
Author(s):  
Andrés Antonio González-Garrido ◽  
Jacobo José Brofman-Epelbaum ◽  
Fabiola Reveca Gómez-Velázquez ◽  
Sebastián Agustín Balart-Sánchez ◽  
Julieta Ramos-Loyo

Abstract. It has been generally accepted that skipping breakfast adversely affects cognition, mainly disturbing the attentional processes. However, the effects of short-term fasting upon brain functioning are still unclear. We aimed to evaluate the effect of skipping breakfast on cognitive processing by studying the electrical brain activity of young healthy individuals while performing several working memory tasks. Accordingly, the behavioral results and event-related brain potentials (ERPs) of 20 healthy university students (10 males) were obtained and compared through analysis of variances (ANOVAs), during the performance of three n-back working memory (WM) tasks in two morning sessions on both normal (after breakfast) and 12-hour fasting conditions. Significantly fewer correct responses were achieved during fasting, mainly affecting the higher WM load task. In addition, there were prolonged reaction times with increased task difficulty, regardless of breakfast intake. ERP showed a significant voltage decrement for N200 and P300 during fasting, while the amplitude of P200 notably increased. The results suggest skipping breakfast disturbs earlier cognitive processing steps, particularly attention allocation, early decoding in working memory, and stimulus evaluation, and this effect increases with task difficulty.


2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.


2010 ◽  
Vol 24 (2) ◽  
pp. 76-82 ◽  
Author(s):  
Martin M. Monti ◽  
Adrian M. Owen

Recent evidence has suggested that functional neuroimaging may play a crucial role in assessing residual cognition and awareness in brain injury survivors. In particular, brain insults that compromise the patient’s ability to produce motor output may render standard clinical testing ineffective. Indeed, if patients were aware but unable to signal so via motor behavior, they would be impossible to distinguish, at the bedside, from vegetative patients. Considering the alarming rate with which minimally conscious patients are misdiagnosed as vegetative, and the severe medical, legal, and ethical implications of such decisions, novel tools are urgently required to complement current clinical-assessment protocols. Functional neuroimaging may be particularly suited to this aim by providing a window on brain function without requiring patients to produce any motor output. Specifically, the possibility of detecting signs of willful behavior by directly observing brain activity (i.e., “brain behavior”), rather than motoric output, allows this approach to reach beyond what is observable at the bedside with standard clinical assessments. In addition, several neuroimaging studies have already highlighted neuroimaging protocols that can distinguish automatic brain responses from willful brain activity, making it possible to employ willful brain activations as an index of awareness. Certainly, neuroimaging in patient populations faces some theoretical and experimental difficulties, but willful, task-dependent, brain activation may be the only way to discriminate the conscious, but immobile, patient from the unconscious one.


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