scholarly journals Absence Seizure Detection Algorithm for Portable EEG Devices

2021 ◽  
Vol 12 ◽  
Author(s):  
Pawel Glaba ◽  
Miroslaw Latka ◽  
Małgorzata J. Krause ◽  
Sławomir Kroczka ◽  
Marta Kuryło ◽  
...  
Keyword(s):  
Detection Rate ◽  
Low Cost ◽  
Clinical Manifestations ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Detection Algorithm ◽  
Absence Epilepsy ◽  
Absence Seizure ◽  
Epileptiform Discharges ◽  
Morlet Continuous Wavelet Transform

Absence seizures are generalized nonmotor epileptic seizures with abrupt onset and termination. Transient impairment of consciousness and spike-slow wave discharges (SWDs) in EEG are their characteristic manifestations. This type of seizure is severe in two common pediatric syndromes: childhood (CAE) and juvenile (JAE) absence epilepsy. The appearance of low-cost, portable EEG devices has paved the way for long-term, remote monitoring of CAE and JAE patients. The potential benefits of this kind of monitoring include facilitating diagnosis, personalized drug titration, and determining the duration of pharmacotherapy. Herein, we present a novel absence detection algorithm based on the properties of the complex Morlet continuous wavelet transform of SWDs. We used a dataset containing EEGs from 64 patients (37 h of recordings with almost 400 seizures) and 30 age and sex-matched controls (9 h of recordings) for development and testing. For seizures lasting longer than 2 s, the detector, which analyzed two bipolar EEG channels (Fp1-T3 and Fp2-T4), achieved a sensitivity of 97.6% with 0.7/h detection rate. In the patients, all false detections were associated with epileptiform discharges, which did not yield clinical manifestations. When the duration threshold was raised to 3 s, the false detection rate fell to 0.5/h. The overlap of automatically detected seizures with the actual seizures was equal to ~96%. For EEG recordings sampled at 250 Hz, the one-channel processing speed for midrange smartphones running Android 10 (about 0.2 s per 1 min of EEG) was high enough for real-time seizure detection.

AUTOMATED SEIZURE DETECTION USING EKG

2014 ◽  
Vol 24 (02) ◽  
pp. 1450001 ◽  
Author(s):  
IVAN OSORIO
Keyword(s):  
False Negative ◽  
Epileptic Seizures ◽  
Cost Effective ◽  
Seizure Detection ◽  
Detection Algorithm ◽  
High Signal ◽  
Care Givers ◽  
Clinical Onset ◽  
Epileptiform Discharges ◽  
Effective Manner

Changes in heart rate, most often increases, are associated with the onset of epileptic seizures and may be used in lieu of cortical activity for automated seizure detection. The feasibility of this aim was tested on 241 clinical seizures from 81 subjects admitted to several Epilepsy Centers for invasive monitoring for evaluation for epilepsy surgery. The performance of the EKG-based seizure detection algorithm was compared to that of a validated algorithm applied to electrocorticogram (ECoG). With the most sensitive detection settings [threshold T: 1.15; duration D: 0 s], 5/241 seizures (2%) were undetected (false negatives) and with the highest [T: 1.3; D: 5 s] settings, the number of false negative detections rose to 34 (14%). The rate of potential false positive (PFP) detections was 9.5/h with the lowest and 1.1/h with the highest T, D settings. Visual review of 336 ECoG segments associated with PFPs revealed that 120 (36%) were associated with seizures, 127 (38%) with bursts of epileptiform discharges and only 87 (26%) were true false positives. Electrocardiographic (EKG)-based seizure onset detection preceded clinical onset by 0.8 s with the lowest and followed it by 13.8 s with the highest T, D settings. Automated EKG-based seizure detection is feasible and has potential clinical utility given its ease of acquisition, processing, high signal/noise and ergonomic advantages viz-a-viz EEG (electroencephalogram) or ECoG. Its use as an "electronic" seizure diary will remedy in part, the inaccuracies of those generated by patients/care-givers in a cost-effective manner.

scholarly journals Shall we treat patients with benign epileptiform discharges of childhood without epileptic seizures?

2019 ◽  
Vol 14 (1) ◽  
pp. 7-13
Author(s):  
N. A. Ermolenko ◽  
I. S. Bakhtin ◽  
I. A. Buchneva
Keyword(s):  
Focal Epilepsy ◽  
Clinical Manifestations ◽  
Epileptic Seizures ◽  
Healthy Children ◽  
Epileptiform Discharges ◽  
First Choice ◽  
Long Term Follow Up ◽  
Potential Risks

Benign epileptiform discharges of childhood are age-dependent electroencephalogram patterns associated with idiopathic benign focal epilepsy. Multiple studies have demonstrated that focal epileptiform discharges can be registered in patients without any clinical manifestations of epilepsy. Long-term follow-up of clinically healthy children with benign epileptiform discharges of childhood on electroencephalogram demonstrated that 14 % of them developed epileptic seizures with age and 50 % developed various cognitive and behavioral disorders. The question of whether or not to treat such patients (with benign epileptiform discharges of childhood on electroencephalogram but without epileptic seizure) is still being widely discussed. Individual decision making with the consideration of potential risks and benefits for a patient is preferable in this case. Valproic acid is the drug of first choice in these patients.

scholarly journals Long-Term Intake ofUncaria rhynchophyllaReduces S100B and RAGE Protein Levels in Kainic Acid-Induced Epileptic Seizures Rats

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Nou-Ying Tang ◽  
Yi-Wen Lin ◽  
Tin-Yun Ho ◽  
Chin-Yi Cheng ◽  
Chao-Hsiang Chen ◽  
...  
Keyword(s):  
Kainic Acid ◽  
Metabotropic Glutamate Receptor ◽  
Low Cost ◽  
Treatment Options ◽  
Clinical Manifestations ◽  
Herbal Medicines ◽  
Epileptic Seizures ◽  
Cell Loss ◽  
Monocyte Chemoattractant Protein 1 ◽  
Protein Levels

Epileptic seizures are crucial clinical manifestations of recurrent neuronal discharges in the brain. An imbalance between the excitatory and inhibitory neuronal discharges causes brain damage and cell loss. Herbal medicines offer alternative treatment options for epilepsy because of their low cost and few side effects. We established a rat epilepsy model by injecting kainic acid (KA, 12 mg/kg, i.p.) and subsequently investigated the effect ofUncaria rhynchophylla(UR) and its underlying mechanisms. Electroencephalogram and epileptic behaviors revealed that the KA injection induced epileptic seizures. Following KA injection, S100B levels increased in the hippocampus. This phenomenon was attenuated by the oral administration of UR and valproic acid (VA, 250 mg/kg). Both drugs significantly reversed receptor potentiation for advanced glycation end product proteins. Rats with KA-induced epilepsy exhibited no increase in the expression of metabotropic glutamate receptor 3, monocyte chemoattractant protein 1, and chemokine receptor type 2, which play a role in inflammation. Our results provide novel and detailed mechanisms, explaining the role of UR in KA-induced epileptic seizures in hippocampal CA1 neurons.

Computationally-Efficient Algorithm for Real-Time Absence Seizure Detection in Wearable Electroencephalography

2020 ◽  
Vol 30 (11) ◽  
pp. 2050035
Author(s):  
Jonathan Dan ◽  
Benjamin Vandendriessche ◽  
Wim Van Paesschen ◽  
Dorien Weckhuysen ◽  
Alexander Bertrand
Keyword(s):  
Computational Cost ◽  
Seizure Detection ◽  
Absence Epilepsy ◽  
Absence Seizure ◽  
Computationally Efficient ◽  
Detection Algorithms ◽  
Processing Power ◽  
Juvenile Absence Epilepsy ◽  
Standard Linear ◽  
Novel Algorithms

Advances in electroencephalography (EEG) equipment now allow monitoring of people with epilepsy in their daily-life environment. The large volumes of data that can be collected from long-term out-of-clinic monitoring require novel algorithms to process the recordings on board of the device to identify and log or transmit only relevant data epochs. Existing seizure-detection algorithms are generally designed for post-processing purposes, so that memory and computing power are rarely considered as constraints. We propose a novel multi-channel EEG signal processing method for automated absence seizure detection which is specifically designed to run on a microcontroller with minimal memory and processing power. It is based on a linear multi-channel filter that is precomputed offline in a data-driven fashion based on the spatial-temporal signature of the seizure and peak interference statistics. At run-time, the algorithm requires only standard linear filtering operations, which are cheap and efficient to compute, in particular on microcontrollers with a multiply-accumulate unit (MAC). For validation, a dataset of eight patients with juvenile absence epilepsy was collected. Patients were equipped with a 20-channel mobile EEG unit and discharged for a day-long recording. The algorithm achieves a median of 0.5 false detections per day at 95% sensitivity. We compare our algorithm with state-of-the-art absence seizure detection algorithms and conclude it performs on par with these at a much lower computational cost.

scholarly journals Augmentation of Tonic GABAAInhibition in Absence Epilepsy: Therapeutic Value of Inverse Agonists at Extrasynaptic GABAAReceptors

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Adam C. Errington ◽  
David W. Cope ◽  
Vincenzo Crunelli
Keyword(s):  
Epileptic Seizures ◽  
Absence Epilepsy ◽  
Vital Role ◽  
Gaba Uptake ◽  
Absence Seizure ◽  
Pharmacological Agents ◽  
Absence Seizures ◽  
Inverse Agonists ◽  
Potential Therapeutic Role ◽  
Necessary And Sufficient

It is well established that impaired GABAergic inhibition within neuronal networks can lead to hypersynchronous firing patterns that are the typical cellular hallmark of convulsive epileptic seizures. However, recent findings have highlighted that a pathological enhancement of GABAergic signalling within thalamocortical circuits is a necessary and sufficient condition for nonconvulsive typical absence seizure genesis. In particular, increased activation of extrasynaptic GABAAreceptors (eGABAAR) and augmented “tonic” GABAAinhibition in thalamocortical neurons have been demonstrated across a range of genetic and pharmacological models of absence epilepsy. Moreover, evidence from monogenic mouse models (stargazer/lethargic) and the polygenic Genetic Absence Epilepsy Rats from Strasbourg (GAERS) indicate that the mechanism underlying eGABAAR gain of function is nonneuronal in nature and results from a deficiency in astrocytic GABA uptake through the GAT-1 transporter. These results challenge the existing theory that typical absence seizures are underpinned by a widespread loss of GABAergic function in thalamocortical circuits and illustrate a vital role for astrocytes in the pathology of typical absence epilepsy. Moreover, they explain why pharmacological agents that enhance GABA receptor function can initiate or exacerbate absence seizures and suggest a potential therapeutic role for inverse agonists at eGABAARs in absence epilepsy.

scholarly journals A study of psychogenic non-epileptic seizures in an inpatient video-electroencephalography monitoring service in Tabriz, Northwest of Iran

2021 ◽  
Author(s):  
Mazyar Hashemilar ◽  
Saeid Charsouei ◽  
Darioush Savadi-Oskouei ◽  
Elyar Sadeghi-Hokmabadi ◽  
Mohammad Farzipour
Keyword(s):  
Demographic Data ◽  
Clinical Manifestations ◽  
Epileptic Seizures ◽  
Signs And Symptoms ◽  
Final Diagnosis ◽  
Cross Sectional ◽  
Epileptiform Discharges ◽  
Drug History ◽  
Antiepileptic Drug Treatment ◽  
Monitoring Service

Background: Psychogenic non-epileptic seizures (PNES) are manifested as paroxysmal alterations in motor, sensory, autonomic, and/or cognitive and behavioral signs and symptoms, without associated ictal epileptiform discharges. A misdiagnosis of PNES as epilepsy results in a prolonged and unnecessary (antiepileptic) drug treatment and social and psychological stigma of epilepsy in these patients. This study aimed to determine the epidemiology, clinical manifestations, and associated factors of PNES in hospitalized patients in the video-electroencephalography (EEG) monitoring (VEM) service of Razi Hospital, Tabriz, Iran. Methods: In this cross-sectional descriptive study, 55 patients with a final diagnosis of PNES were selected from the patients referred to the VEM unit of Razi Hospital for the evaluation of epilepsy. The study was performed from May 2017 to June 2019. Patient information included demographic data and medical history (drug history, comorbidities, trauma, and family history). The clinical manifestations (semiology and duration of attacks) and EEG findings, as recorded by VEM during hospitalization, were collected. Results: 55 patients with PNES were studied with VEM, 27 (49.1%) of which were men, and 28 (50.9%) were women. The mean and standard deviation (SD) of age of the patients was 34.16 ± 12.64 years. No significant differences were observed in the semiology of PNES between men and women. Depression was the most common psychiatric comorbidity. Conclusion: The clinical manifestations of PNES in the present study were similar to those in most previous studies from other countries. The culture and sex of the patients did not seem to be a contributing factor in PNES semiology.

scholarly journals A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1179
Author(s):  
Carolina Del-Valle-Soto ◽  
Carlos Mex-Perera ◽  
Juan Arturo Nolazco-Flores ◽  
Alma Rodríguez ◽  
Julio C. Rosas-Caro ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Performance Metrics ◽  
Low Cost ◽  
Detection Algorithm ◽  
Anomalous Behavior ◽  
Wireless Sensor ◽  
Jamming Detection ◽  
Minimal Energy Expenditure

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

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