Combination of EEG and ECG for improved automatic neonatal seizure detection

Greene, Barry R.; Boylan, Geraldine B.; Reilly, Richard B.; Chazal, Philip de; Connolly, Seán

doi:10.1016/j.clinph.2007.02.015

Cited by 98 publications

(62 citation statements)

References 23 publications

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“…The final decision is obtained by integrating the separate decisions of the two classifiers. We also compare our detectors to the ones proposed by Greene et al [38] and discuss the possible sources of the differences in performance. We end the article by discussing different limitations of this study and how to overcome them in the future.…”

Section: Introductionmentioning

confidence: 95%

“…In an attempt to combine EEG and ECG information to detect newborn seizures, Greene et al [38] proposed two approaches, namely patient-specific and patient-independent. Both approaches were considered with fusion at feature-level (referred to as early integration or EI) and at decision-level (referred to as late integration or LI).…”

Section: Detection Performance Comparison With Greene's Algorithmmentioning

confidence: 99%

“…The success of this endeavor would result in a decrease on complete reliance on the EEG as the sole information source for detection of newborn seizure and, therefore, a reduction of false detections caused by confounding factors in EEG. Although it uses similar structures as the method proposed in [38], the present approach differs from [38] in a significant number of points, such as the nature of feature extracted, the use of feature selection, and the rules for combining the classifiers as detailed within the text.…”

Section: Introductionmentioning

confidence: 99%

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Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

Mesbah

Malarvili

Colditz

et al. 2012

EURASIP J. Adv. Signal Process.

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This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

show abstract

Section: Introductionmentioning

confidence: 95%

Section: Detection Performance Comparison With Greene's Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information

Mesbah

Malarvili

Colditz

et al. 2012

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

show abstract

“…Greene et al [16] presented an algorithm in which the combination of EEG and ECG recordings are incorporated to detect epileptic seizures. The proposed algorithm extracts six different features from each EEG signals including line length, power ratio, nonlinear energy, dominant spectral peak and its bandwidth and spectral entropy.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed algorithm extracts six different features from each EEG signals including line length, power ratio, nonlinear energy, dominant spectral peak and its bandwidth and spectral entropy. Similarly, the algorithm extracts six features for ECG including "mean R-R interval, standard deviation between R-R intervals, mean spectral entropy, mean change in the R-R interval, coefficient of variation and the power spectral density" [16].…”

Section: Introductionmentioning

confidence: 99%