Detection of seizures in newborns using time-frequency analysis of EEG signals

Boashash, Boualem; Carson, Helen; Mesbah, Mostefa

doi:10.1109/ssap.2000.870188

Cited by 18 publications

(13 citation statements)

References 4 publications

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“…We have seen the presence of tracks on the time frequency plane during seizure events as also observed by other authors [11,50]. In [11,9,10] the authors found a time frequency seizure criteria based on two calibrations in time and time frequency domain.…”

Section: Discussionsupporting

confidence: 84%

“…Each resulting segment is processed using time frequency analysis and then we apply peak energy matching on the TF plane based on the MQ sinusoidal analysis, with the objective of extracting features and using them for the task of detection. We assume the existence of some wave in epileptic seizures from results obtained by others authors [11,23,50] that have observed tracks along the time frequency plane during a seizure. Our approach is detailed in what follows.…”

Section: Methodsmentioning

confidence: 99%

“…Other group of methods potentially useful for detecting and analyzing nonstationary signals are time frequency distributions (TFDs) [11,23,44]. These methods allow us to visualize the evolution of the frequency behavior during some non-stationary event by mapping a one dimensional (1-D) time signal into a two-dimensional (2-D) function of time and frequency.…”

Section: Introductionmentioning

confidence: 99%

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New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

Guerrero-Mosquera

Trigueros

Franco

et al. 2010

Med Biol Eng Comput

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This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.

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Section: Discussionsupporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

Guerrero-Mosquera

Trigueros

Franco

et al. 2010

Med Biol Eng Comput

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“…SPWV) and MQ sinusoidal analysis with the objective of extracting features and using them for the task of detection. We assume the existence of some wave in epileptic seizures from results obtained by others authors [6], [13] that have shown tracks along the timefrequency plane when there is a seizure. Our approach can be summarized in Fig.…”

Section: Processing Methodsmentioning

confidence: 99%

“…These methods reinforce the nonstationary EEG concept and call for the necessity of extracting more information to understand the brain signals and its dynamics. Other group of methods potentially useful for detecting and analyzing nonstationary signals are time-frequency distributions (TFDs) [6]. These methods allow us to visualize the evolution of the frequency behavior during some nonstationary event by mapping a one dimensional (1-D) time signal into a two-dimensional (2-D) function of time and frequency.…”

Section: Introductionmentioning

confidence: 99%

New approach in features extraction for EEG signal detection

Guerrero-Mosquera

Vázquez

2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the Smoothed Pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature based on the length of the track that, combined with energy and frequency features, allows to isolate a continuous energy trace from another oscillations when an epileptic seizure is beginning. We evaluate our approach using data consisting of 16 different seizures from 6 epileptic patients. The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs.

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Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving

et al. 2017

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Conventional neuroimaging analyses have ascribed function to particular brain regions, exploiting the power of the subtraction technique in fMRI and event-related potential analyses in EEG. Moving beyond this convention, many researchers have begun exploring network-based neurodynamics and coordination between brain regions as a function of behavioral parameters or environmental statistics; however, most approaches average evoked activity across the experimental session to study task-dependent networks. Here, we examined on-going oscillatory activity as measured with EEG and use a methodology to estimate directionality in brain-behavior interactions. After source reconstruction, activity within specific frequency bands (delta: 2-3Hz; theta: 4-7Hz; alpha: 8-12Hz; beta: 13-25Hz) in a priori regions of interest was linked to continuous behavioral measurements, and we used a predictive filtering scheme to estimate the asymmetry between brain-to-behavior and behavior-to-brain prediction using a variant of Granger causality. We applied this approach to a simulated driving task and examined directed relationships between brain activity and continuous driving performance (steering behavior or vehicle heading error). Our results indicated that two neuro-behavioral states may be explored with this methodology: a Proactive brain state that actively plans the response to the sensory information and is characterized by delta-beta activity, and a Reactive brain state that processes incoming information and reacts to environmental statistics primarily within the alpha band.

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Detection of seizures in newborns using time-frequency analysis of EEG signals

Cited by 18 publications

References 4 publications

New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

New approach in features extraction for EEG signal detection

Estimating direction in brain-behavior interactions: Proactive and reactive brain states in driving

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