Dataset Information

NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification.

ABSTRACT: Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure-induced body movements--called seizure semiology. Thus, synchronous EEG and (2D)video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m(3) vs. 0.14 m(3), p<0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p<0.01) and presented higher jerking levels (mean = 345 ms(-3) vs 172 ms(-3), p<0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis.

SUBMITTER: Cunha JP

PROVIDER: S-EPMC4723069 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification.

Cunha João Paulo Silva JP Choupina Hugo Miguel Pereira HM Rocha Ana Patrícia AP Fernandes José Maria JM Achilles Felix F Loesch Anna Mira AM Vollmar Christian C Hartl Elisabeth E Noachtar Soheyl S

PloS one 20160122 1

Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure-induced body movements--called seizure semiology. Thus, synchronous EEG and (2D)video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on ...[more]

PMID: 26799795

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Project description:ObjectiveMarkers of seizure recurrence are needed to personalize antiseizure medication (ASM) therapy. In the clinical practice, EEG features are considered to be related to the risk of seizure recurrence for genetic generalized epilepsies (GGE). However, to our knowledge, there are no studies analyzing systematically specific EEG features as indices of ASM efficacy in GGE. In this study, we aimed at identifying EEG indicators of ASM responsiveness in Juvenile Myoclonic Epilepsy (JME), which, among GGE, is characterized by specific electroclinical features.MethodsWe compared the features of prolonged ambulatory EEG (paEEG, 22 h of recording) of JME patients experiencing seizure recurrence within a year ("cases") after EEG recording, with those of patients with sustained seizure freedom for at least 1 year after EEG ("controls"). We included only EEG recordings of patients who had maintained the same ASM regimen (dosage and type) throughout the whole time period from the EEG recording up to the outcome events (which was seizure recurrence for the "cases", or 1-year seizure freedom for "controls"). As predictors, we evaluated the total number, frequency, mean and maximum duration of epileptiform discharges (EDs) and spike density (i.e. total EDs duration/artifact-free EEG duration) recorded during the paEEG. The same indexes were assessed also in standard EEG (stEEG), including activation methods.ResultsBoth the maximum length and the mean duration of EDs recorded during paEEG significantly differed between cases and controls; when combined in a binary logistic regression model, the maximum length of EDs emerged as the only valid predictor. A cut-off of EDs duration of 2.68 seconds discriminated between cases and controls with a 100% specificity and a 93% sensitivity. The same indexes collected during stEEG lacked both specificity and sensitivity.SignificanceThe occurrence of prolonged EDs in EEG recording might represent an indicator of antiepileptic drug failure in JME patients.

Project description:OBJECTIVE:The purpose of this study was to evaluate and validate an offline, automated scalp EEG-based seizure detection system and to compare its performance to commercially available seizure detection software. METHODS:The test seizure detection system, IdentEvent™, was developed to enhance the efficiency of post-hoc long-term EEG review in epilepsy monitoring units. It translates multi-channel scalp EEG signals into multiple EEG descriptors and recognizes ictal EEG patterns. Detection criteria and thresholds were optimized in 47 long-term scalp EEG recordings selected for training (47 subjects, ?3653h with 141 seizures). The detection performance of IdentEvent was evaluated using a separate test dataset consisting of 436 EEG segments obtained from 55 subjects (?1200h with 146 seizures). Each of the test EEG segments was reviewed by three independent epileptologists and the presence or absence of seizures in each epoch was determined by majority rule. Seizure detection sensitivity and false detection rate were calculated for IdentEvent as well as for the comparable detection software (Persyst's Reveal®, version 2008.03.13, with three parameter settings). Bootstrap re-sampling was applied to establish the 95% confidence intervals of the estimates and for the performance comparison between two detection algorithms. RESULTS:The overall detection sensitivity of IdentEvent was 79.5% with a false detection rate (FDR) of 2 per 24h, whereas the comparison system had 80.8%, 76%, and 74% sensitivity using its three detection thresholds (perception score) with FDRs of 13, 8, and 6 per 24h, respectively. Bootstrap 95% confidence intervals of the performance difference revealed that the two detection systems had comparable detection sensitivity, but IdentEvent generated a significantly (p<0.05) smaller FDR. CONCLUSIONS:The study validates the performance of the IdentEvent™ seizure detection system. SIGNIFICANCE:With comparable detection sensitivity, an improved false detection rate makes the automated seizure detection software more useful in clinical practice.

Dataset Information

NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification.

Publications

NeuroKinect: A Novel Low-Cost 3Dvideo-EEG System for Epileptic Seizure Motion Quantification.

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