Seizure pattern-specific epileptic epoch detection in patients with intellectual disability

Wang, Lei; Arends, Johan; Long, Xi; Cluitmans, P.J.M.; Dijk, Hans van

doi:10.1016/j.bspc.2017.02.008

Cited by 20 publications

(38 citation statements)

References 63 publications

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“…SFS starts with a frequency (or frequency pair), and then sequentially adds another frequency (from the remaining set) that results in the smallest increase of the cost (i.e., MPE in a range of [0–2]). The details of the SFS algorithm are described in 29 , 32 .…”

Section: Methodsmentioning

confidence: 99%

Estimating multiple latencies in the auditory system from auditory steady-state responses on a single EEG channel

Wang

Noordanus

Opstal

2021

Sci Rep

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The latency of the auditory steady-state response (ASSR) may provide valuable information regarding the integrity of the auditory system, as it could potentially reveal the presence of multiple intracerebral sources. To estimate multiple latencies from high-order ASSRs, we propose a novel two-stage procedure that consists of a nonparametric estimation method, called apparent latency from phase coherence (ALPC), followed by a heuristic sequential forward selection algorithm (SFS). Compared with existing methods, ALPC-SFS requires few prior assumptions, and is straightforward to implement for higher-order nonlinear responses to multi-cosine sound complexes with their initial phases set to zero. It systematically evaluates the nonlinear components of the ASSRs by estimating multiple latencies, automatically identifies involved ASSR components, and reports a latency consistency index. To verify the proposed method, we performed simulations for several scenarios: two nonlinear subsystems with different or overlapping outputs. We compared the results from our method with predictions from existing, parametric methods. We also recorded the EEG from ten normal-hearing adults by bilaterally presenting superimposed tones with four frequencies that evoke a unique set of ASSRs. From these ASSRs, two major latencies were found to be stable across subjects on repeated measurement days. The two latencies are dominated by low-frequency (LF) (near 40 Hz, at around 41–52 ms) and high-frequency (HF) (> 80 Hz, at around 21–27 ms) ASSR components. The frontal-central brain region showed longer latencies on LF components, but shorter latencies on HF components, when compared with temporal-lobe regions. In conclusion, the proposed nonparametric ALPC-SFS method, applied to zero-phase, multi-cosine sound complexes is more suitable for evaluating embedded nonlinear systems underlying ASSRs than existing methods. It may therefore be a promising objective measure for hearing performance and auditory cortex (dys)function.

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Section: Methodsmentioning

confidence: 99%

Estimating multiple latencies in the auditory system from auditory steady-state responses on a single EEG channel

Wang

Noordanus

Opstal

2021

Sci Rep

Self Cite

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“…In addition, the ictal discharges were labeled either generalized or focal according to their onset location, and their onset visibility were labeled either sudden or gradual according to the ACNS criteria. Five categories were used to describe the dominant morphology patterns: three of five categories-"Spike Wave", "Wave", and "Fast Spike" pattern-were summarized from several terms in ACNS criteria, and the other two empirical categories were added-"Seizure-related EMG Artifacts" 21 , and "Unknown Type". We summarized the annotated ictal discharges by the two raters via an annotation code (appendix A), and estimated the inter-rater agreement.…”

Section: Methodsmentioning

confidence: 99%

Pitfalls in EEG analysis in patients with non-convulsive status epilepticus

Wang

Zibrandtsen

Lazeron

et al. 2020

Preprint

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Objective. Electroencephalography (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. Methods. We 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 the clinical data of individuals. 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. Results. Short 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. Conclusion. A 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.

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“…Mappings between the frequency and time spaces are utilized generally as a part of signal investigation and handling. The strategies in time-frequency domain uses Fast Fourier change (FFT), wavelet transform (WT) 43 , Tunable-Q Wavelet Transform 40 , short time Fourier transform (STFT), wavelet entropy 29 , wavelet energy function 30 , multi domain wavelet threshold 44 , harmonic wavelet packet transform 36 , Stockwell transform 38 , ensemble empirical mode decomposition 37 , Multivariate Empirical Mode Decomposition 14 , Bootstrap Aggregating 45 , visibility graph 2,27 , Cohen class kernel functions 33 and other entropy based methods 17 . Since, Fourier strategies may not be suitablefor non-stationary signal or stationary signals with short-lived segments.…”

Section: Eeg Signal Classification Methodsmentioning

confidence: 99%

“…For instance, the issue with binary classification involves the separation of classes into two category, for example, the target and non-target classes. Classification algorithms rely mostly on labelled output, where the learning is supervised or unsupervised based on statistical or non-statistical data.The supervised classification algorithms foreseescategorical labels as: support vector machine (SVM) [26][27][28][29][30][31][32][33][34][35] , Global modular PCA with SVM [36][37][38][39][40][41] , linear discriminant analysis (LDA), Naive Bayes, decision trees, K-nearest-neighbour (kNN), logistic regression, neural networks, Kernel estimation, linear regression, Kalman filters, Gaussian process regression, fractional linear prediction 2 etc. The aim of these algorithms is to amplify the precision of testing over testing dataset and hence, the supervised algorithms are used mostly in classifying the EEG signals.…”

Section: Eeg Signal Classificationmentioning

confidence: 99%

Review on diverse approaches used for epileptic seizure detection using EEG signals

Baskar

Karthikeyan

2018

Bangladesh J Med Sci

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Epileptic seizure detection is a common diagnosis practiced by the expert clinicians through direct visual observation from the electroencephalography (EEG) signal. This detection by the expert clinicians is considered sensitive to bias and time consuming. Further, it suffers from various problems like unsustainability in larger dataset processing and low power detection. Hence, many computerized detection approaches are highly preferred to eliminate the aforementioned problems and to expedite the research in epilepsy seizure detection for aiding the medical professionals. Many such automated epilepsy diagnosis framework has been designed by various researches, which is made to operate in a single or in a combined manner with other domains. This study reviews different approaches, which is been designed to aid the human diagnosis using new avenues that explains the causes of epilepsy and seizures. Further, this study summarizes various methods used previously to analyze the epilepsy and seizures based on its state of art approach. Also, investigations are carried out in terms of performance evaluation to find the best suitable epileptic seizure detection technique in the application of Neuro-informatics.Bangladesh Journal of Medical Science Vol.17(4) 2018 p.526-531

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Seizure pattern-specific epileptic epoch detection in patients with intellectual disability

Cited by 20 publications

References 63 publications

Estimating multiple latencies in the auditory system from auditory steady-state responses on a single EEG channel

Estimating multiple latencies in the auditory system from auditory steady-state responses on a single EEG channel

Pitfalls in EEG analysis in patients with non-convulsive status epilepticus

Review on diverse approaches used for epileptic seizure detection using EEG signals

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