Wavelet analysis for neonatal electroencephalographic seizures

Kitayama, Masaomi; Otsubo, Hiroshi; Parvez, Shahid; Lodha, Abhay; Ying, Ethel; Parvez, Boriana; Ishii, Ryouhei; Mizuno‐Matsumoto, Yuko; Zoroofi, Reza Aghaeizadeh; Snead, O. Carter

doi:10.1016/s0887-8994(03)00277-7

Cited by 34 publications

(13 citation statements)

References 32 publications

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“…It has been shown that frequencies of neonatal EEG seizures range between 0.5 and 13 Hz and the dominant frequencies of seizures vary between 0.5 and 6 Hz (Kitayama et al, 2003). The EEG is split into 8s epochs with 50% overlap between epochs.…”

Section: Methodsmentioning

confidence: 99%

EEG-based neonatal seizure detection with Support Vector Machines

Temko

Thomas

Marnane

et al. 2011

Clinical Neurophysiology

282

264

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ObjectiveThe study presents a multi-channel patient-independent neonatal seizure detection system based on the Support Vector Machine (SVM) classifier.MethodsA machine learning algorithm (SVM) is used as a classifier to discriminate between seizure and non-seizure EEG epochs. Two post-processing steps are proposed to increase both the temporal precision and the robustness of the system. The resulting system is validated on a large clinical dataset of 267 h of EEG data from 17 full-term newborns with seizures.ResultsThe performance of the system using event-based metrics is reported. The system showed the best up-to-date performance of a neonatal seizure detection system. The system was able to achieve an average good detection rate of ∼89% with one false seizure detection per hour, ∼96% with two false detections per hour, or ∼100% with four false detections per hour. An analysis of errors revealed sources of misclassification in terms of both missed seizures and false detections.ConclusionsThe results obtained with the proposed SVM-based seizure detection system allow for its practical application in neonatal intensive care units.SignificanceThe proposed SVM-based seizure detection system can greatly assist clinical staff, in a neonatal intensive care unit, to interpret the EEG. The system allows control of the final decision by choosing different confidence levels which makes it flexible for clinical needs. The obtained results may provide a reference for future seizure detection systems.

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Section: Methodsmentioning

confidence: 99%

EEG-based neonatal seizure detection with Support Vector Machines

Temko

Thomas

Marnane

et al. 2011

Clinical Neurophysiology

282

264

View full text Add to dashboard Cite

show abstract

“…His thorough evaluation resulted in a sensitivity of 69% and 2.3 false positives per hour (27). More recent algorithms were based on time‐frequency (28) or wavelet analysis (29), that detect the repetitive structures by their characteristic evolution of frequency in time. These algorithms were not evaluated.…”

Section: Discussionmentioning

confidence: 99%

An algorithm for the automatic detection of seizures in neonatal amplitude‐integrated EEG

et al. 2007

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“…The following 8 EEG channels in bipolar pairs are used to feed the EEG data into the system: F4–C4, C4–O2, F3–C3, C3–O1, T4–C4, C4–Cz, Cz–C3 and C3–T3. It has been shown that frequencies of neonatal EEG seizures range between 0.5 and 13 Hz and the dominant frequencies of seizures vary between 0.5 and 6 Hz [26] . The EEG from the 8 channels is downsampled to 32 Hz with an anti-aliasing filter set at 12.8 Hz.…”

Section: Neonatal Seizure Detectormentioning

confidence: 99%

Clinical implementation of a neonatal seizure detection algorithm

Temko

Marnane

Boylan

et al. 2015

Decision Support Systems

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Technologies for automated detection of neonatal seizures are gradually moving towards cot-side implementation. The aim of this paper is to present different ways to visualize the output of a neonatal seizure detection system and analyse their influence on performance in a clinical environment. Three different ways to visualize the detector output are considered: a binary output, a probabilistic trace, and a spatio-temporal colormap of seizure observability. As an alternative to visual aids, audified neonatal EEG is also considered. Additionally, a survey on the usefulness and accuracy of the presented methods has been performed among clinical personnel. The main advantages and disadvantages of the presented methods are discussed. The connection between information visualization and different methods to compute conventional metrics is established. The results of the visualization methods along with the system validation results indicate that the developed neonatal seizure detector with its current level of performance would unambiguously be of benefit to clinicians as a decision support system. The results of the survey suggest that a suitable way to visualize the output of neonatal seizure detection systems in a clinical environment is a combination of a binary output and a probabilistic trace. The main healthcare benefits of the tool are outlined. The decision support system with the chosen visualization interface is currently undergoing pre-market European multi-centre clinical investigation to support its regulatory approval and clinical adoption.

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Wavelet analysis for neonatal electroencephalographic seizures

Cited by 34 publications

References 32 publications

EEG-based neonatal seizure detection with Support Vector Machines

EEG-based neonatal seizure detection with Support Vector Machines

An algorithm for the automatic detection of seizures in neonatal amplitude‐integrated EEG

Clinical implementation of a neonatal seizure detection algorithm

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