Dataset Information

Detection of epileptic seizure based on entropy analysis of short-term EEG.

ABSTRACT: Entropy measures that assess signals' complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems were considered, i.e., 1) classifying interictal and ictal EEGs (epileptic group) from normal EEGs; and 2) classifying ictal from interictal EEGs. For each problem, we explored two protocols to analyze the entropy of EEG: i) using a single analytical window with different window lengths, and ii) using an average of multiple windows for each window length. Two entropy methods-fuzzy entropy (FuzzyEn) and distribution entropy (DistEn)-were used that have valid outputs for any given data lengths. We performed feature selection and trained classifiers based on a cross-validation process. The results show that performance of FuzzyEn and DistEn may complement each other and the best performance can be achieved by combining: 1) FuzzyEn of one 5-s window and the averaged DistEn of five 1-s windows for classifying normal from epileptic group (accuracy: 0.93, sensitivity: 0.91, specificity: 0.96); and 2) the averaged FuzzyEn of five 1-s windows and DistEn of one 5-s window for classifying ictal from interictal EEGs (accuracy: 0.91, sensitivity: 0.93, specificity: 0.90). Further studies are warranted to examine whether this proposed short-term analysis procedure can help track the epileptic activities in real time and provide prompt feedback for clinical practices.

SUBMITTER: Li P

PROVIDER: S-EPMC5854404 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Detection of epileptic seizure based on entropy analysis of short-term EEG.

Li Peng P Karmakar Chandan C Yearwood John J Venkatesh Svetha S Palaniswami Marimuthu M Liu Changchun C

PloS one 20180315 3

Entropy measures that assess signals' complexity have drawn increasing attention recently in biomedical field, as they have shown the ability of capturing unique features that are intrinsic and physiologically meaningful. In this study, we applied entropy analysis to electroencephalogram (EEG) data to examine its performance in epilepsy detection based on short-term EEG, aiming at establishing a short-term analysis protocol with optimal seizure detection performance. Two classification problems ...[more]

PMID: 29543825

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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.

Project description:Automated seizure detection is a valuable asset to health professionals, which makes adequate treatment possible in order to minimize brain damage. Most research focuses on two separate aspects of automated seizure detection: EEG feature computation and classification methods. Little research has been published regarding optimal training dataset composition for patient-independent seizure detection. This paper evaluates the performance of classifiers trained on different datasets in order to determine the optimal dataset for use in classifier training for automated, age-independent, seizure detection. Three datasets are used to train a support vector machine (SVM) classifier: (1) EEG from neonatal patients, (2) EEG from adult patients and (3) EEG from both neonates and adults. To correct for baseline EEG feature differences among patients feature, normalization is essential. Usually dedicated detection systems are developed for either neonatal or adult patients. Normalization might allow for the development of a single seizure detection system for patients irrespective of their age. Two classifier versions are trained on all three datasets: one with feature normalization and one without. This gives us six different classifiers to evaluate using both the neonatal and adults test sets. As a performance measure, the area under the receiver operating characteristics curve (AUC) is used. With application of FBC, it resulted in performance values of 0.90 and 0.93 for neonatal and adult seizure detection, respectively. For neonatal seizure detection, the classifier trained on EEG from adult patients performed significantly worse compared to both the classifier trained on EEG data from neonatal patients and the classier trained on both neonatal and adult EEG data. For adult seizure detection, optimal performance was achieved by either the classifier trained on adult EEG data or the classifier trained on both neonatal and adult EEG data. Our results show that age-independent seizure detection is possible by training one classifier on EEG data from both neonatal and adult patients. Furthermore, our results indicate that for accurate age-independent seizure detection, it is important that EEG data from each age category are used for classifier training. This is particularly important for neonatal seizure detection. Our results underline the under-appreciated importance of training dataset composition with respect to accurate age-independent seizure detection.

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.

Dataset Information

Detection of epileptic seizure based on entropy analysis of short-term EEG.

Publications

Detection of epileptic seizure based on entropy analysis of short-term EEG.

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