scholarly journals Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

2019 ◽  
Author(s):  
Shannon Clarke ◽  
Pip Karoly ◽  
Ewan Nurse ◽  
Udaya Seneviratne ◽  
Janelle Taylor ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
False Positive Rate ◽  
Detection Algorithm ◽  
Epileptic Activity ◽  
Automated Detection ◽  
Computer Assisted ◽  
Reference Standard ◽  
Idiopathic Generalized Epilepsy ◽  
Therapeutic Outcomes ◽  
Generalized Epilepsy

AbstractEpilepsy diagnosis can be costly, time-consuming and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-EEG monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially herald a more quantitative approach to therapeutic outcomes. There is substantial research into automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic; and, despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG.This study reports on a deep learning algorithm for computer-assisted EEG review. Deep, convolutional neural networks were trained to detect epileptic discharges using a pre-existing dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data was curated and confirmed independently by two epilepsy specialists (Seneviratne et al, 2016). The resulting automated detection algorithm was then used to review diagnostic scalp-EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting.The automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes.

Computer-assisted estimation of interictal discharge burden in idiopathic generalized epilepsy

2020 ◽  
Vol 105 ◽  
pp. 106970
Author(s):  
Dominique Eden ◽  
Ewan S. Nurse ◽  
Shannon Clarke ◽  
Philippa J. Karoly ◽  
Udaya Seneviratne ◽  
...  
Keyword(s):  
Computer Assisted ◽  
Idiopathic Generalized Epilepsy ◽  
Generalized Epilepsy ◽  
Interictal Discharge

The use of computer-assisted-telephone-interviewing to diagnose seizures, epilepsy and Idiopathic Generalized Epilepsy

2010 ◽  
Vol 91 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Wendyl J. D'Souza ◽  
Jim Stankovich ◽  
Terence J. O’Brien ◽  
Simon Bower ◽  
Neil Pearce ◽  
...  
Keyword(s):  
Computer Assisted ◽  
Idiopathic Generalized Epilepsy ◽  
Generalized Epilepsy

scholarly journals Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 79 ◽  
Author(s):  
S. Kok ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Phase I ◽  
Phase Ii ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Application Programming Interface ◽  
Detection Algorithm ◽  
Positive Rate ◽  
New Type ◽  
Two Phases

Ransomware is a relatively new type of intrusion attack, and is made with the objective of extorting a ransom from its victim. There are several types of ransomware attacks, but the present paper focuses only upon the crypto-ransomware, because it makes data unrecoverable once the victim’s files have been encrypted. Therefore, in this research, it was proposed that machine learning is used to detect crypto-ransomware before it starts its encryption function, or at the pre-encryption stage. Successful detection at this stage is crucial to enable the attack to be stopped from achieving its objective. Once the victim was aware of the presence of crypto-ransomware, valuable data and files can be backed up to another location, and then an attempt can be made to clean the ransomware with minimum risk. Therefore we proposed a pre-encryption detection algorithm (PEDA) that consisted of two phases. In, PEDA-Phase-I, a Windows application programming interface (API) generated by a suspicious program would be captured and analyzed using the learning algorithm (LA). The LA can determine whether the suspicious program was a crypto-ransomware or not, through API pattern recognition. This approach was used to ensure the most comprehensive detection of both known and unknown crypto-ransomware, but it may have a high false positive rate (FPR). If the prediction was a crypto-ransomware, PEDA would generate a signature of the suspicious program, and store it in the signature repository, which was in Phase-II. In PEDA-Phase-II, the signature repository allows the detection of crypto-ransomware at a much earlier stage, which was at the pre-execution stage through the signature matching method. This method can only detect known crypto-ransomware, and although very rigid, it was accurate and fast. The two phases in PEDA formed two layers of early detection for crypto-ransomware to ensure zero files lost to the user. However in this research, we focused upon Phase-I, which was the LA. Based on our results, the LA had the lowest FPR of 1.56% compared to Naive Bayes (NB), Random Forest (RF), Ensemble (NB and RF) and EldeRan (a machine learning approach to analyze and classify ransomware). Low FPR indicates that LA has a low probability of predicting goodware wrongly.

scholarly journals Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

2019 ◽  
pp. 106556 ◽  
Author(s):  
Shannon Clarke ◽  
Philippa J. Karoly ◽  
Ewan Nurse ◽  
Udaya Seneviratne ◽  
Janelle Taylor ◽  
...  
Keyword(s):  
Computer Assisted ◽  
Idiopathic Generalized Epilepsy ◽  
Generalized Epilepsy

A New SYNGAP1 Stop Mutation in a Patient with Idiopathic Generalized Epilepsy Showing Photosensitivity and Electroencephalography Normalization after Eye Opening

2014 ◽  
Vol 45 (S 01) ◽  
Author(s):  
C. von Stülpnagel-Steinbeis ◽  
C. Funke ◽  
C. Haberl ◽  
K. Hörtnagel ◽  
J. Jüngling ◽  
...  
Keyword(s):  
Idiopathic Generalized Epilepsy ◽  
Stop Mutation ◽  
Eye Opening ◽  
Generalized Epilepsy

scholarly journals The Impact of Idiopathic Generalized Epilepsy on Executive Functions

2021 ◽  
pp. 097275312096875
Author(s):  
Haritha Koganti ◽  
Shasthara Paneyala ◽  
Harsha Sundaramurthy ◽  
Nemichandra SC ◽  
Rithvik S Kashyap ◽  
...  
Keyword(s):  
Executive Functions ◽  
Executive Dysfunction ◽  
Cross Sectional Study ◽  
Idiopathic Epilepsy ◽  
Idiopathic Generalized Epilepsy ◽  
Word Fluency ◽  
Cross Sectional ◽  
Generalized Epilepsy ◽  
Multiple Domains ◽  
The Impact

Background: Idiopathic generalized epilepsy is defined as seizures with a possible hereditary predisposition without an underlying cause or structural pathology. Assessment of executive dysfunction in idiopathic generalized epilepsies based on standard Indian battery is not available in the literature. Aims and Objectives: To assess specific executive functions affected in patients with idiopathic epilepsy and their association with various variables. Materials and Methods: Type of observational cross-sectional study, where clinical profile of all idiopathic epilepsy patients attending the neurology OPD was studied and their executive higher mental functions were assessed using the NIMHANS battery. Results: A total of 75 idiopathic generalized epilepsy patients were included in the study. Executive functions that were commonly found abnormal in our study were word fluency ( P ≤ .001), category fluency ( P < .001), verbal n-back ( P < .001), Tower of London ( p < 0.01), and Stroop test ( P < 0.01). Executive functions showed a significant correlation with age at symptom onset, duration of epilepsy, and in those with uncontrolled seizures. Conclusion: Patients of idiopathic generalized epilepsy according to the present study were found to have significant executive dysfunction in multiple domains. This necessitates the screening for executive dysfunctions, which if detected should prompt the clinician to initiate cognitive retraining.

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