HRV feature selection based on discriminant and redundancy analysis for neonatal seizure detection

Malarvili, M. B.; Mesbah, Mostefa

doi:10.1109/icics.2007.4449765

Cited by 13 publications

(9 citation statements)

References 17 publications

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“…The filter-wrapper-based feature selection method in [22] is a two-phase process used to reduce the computation load associated with the conventional wrappers by reducing the dimension of the search space and, there- fore, speeding up the convergence process. The first phase, which acts as pre-processing phase, involves a filter [21] that uses discriminant and redundancy analysis to select a feature subset with high discriminatory power between the two newborn EEG classes. As the result of this, a set of relevant features, f, with minimum redundancy and maximum class discriminability is obtained.…”

Section: Feature Selectionmentioning

confidence: 99%

A Multi-Channel Fusion Based Newborn Seizure Detection

Malarvili¹,

Colditz²,

Boashash³

2014

JBiSE

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How to cite this paper: Malarvili, M.B., et al. (2014) A Multi-Channel Fusion Based Newborn Seizure Detection. J. Biomedical Science and Engineering, 7, 533-545. http://dx. AbstractWe propose and compare two multi-channel fusion schemes to utilize the information extracted from simultaneously recorded multiple newborn electroencephalogram (EEG) channels for seizure detection. The first approach is known as the multi-channel feature fusion. It involves concatenating EEG feature vectors independently obtained from the different EEG channels to form a single feature vector. The second approach, called the multi-channel decision/classifier fusion, is achieved by combining the independent decisions of the different EEG channels to form an overall decision as to the existence of a newborn EEG seizure. The first approach suffers from the large dimensionality problem. In order to overcome this problem, three different dimensionality reduction techniques based on the sum, Fisher's linear discriminant and symmetrical uncertainty (SU) were considered. It was found that feature fusion based on SU technique outperformed the other two techniques. It was also shown that feature fusion, which was developed on the basis that there was inter-dependence between recorded EEG channels, was superior to the independent decision fusion. Keywords EEG, Newborn Seizure Detection, Multi-Channel, Feature Fusion, Decision/Classifier FusionThe dimension of the reduced composite feature vector, SU J , is the same as the dimension of the feature vectors, J i , from each channel.The reduced composite feature vector is then fed to the statistical classifiers investigated in the process of newborn seizure detection. The performance of the proposed multi-channel newborn EEG seizure detection using the three different dimension reduction methods is presented in Section 3.

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Section: Feature Selectionmentioning

confidence: 99%

A Multi-Channel Fusion Based Newborn Seizure Detection

Malarvili¹,

Colditz²,

Boashash³

2014

JBiSE

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“…In this article, we used feature selection methods we previously developed [33,48,49] to select the optimal feature subset with minimum redundancy and maximum class discriminability. The feature selection process was considered successful if the dimensionality of the feature set was reduced while the accuracy of the classification was either improved or remained unchanged relative to the full set.…”

Section: Feature Selectionmentioning

confidence: 99%

“…In this approach, as illustrated in Figure 1, the optimal HRV-EEG feature subsets were selected using the filterbased selection method described in [48] where it has been shown to be able to significantly reduce the number of features while maintaining a high classification performance. The optimal HRV and EEG feature selected as a result of employing the filter-based feature selection method are given below.…”

Section: Feature Fusion Casementioning

confidence: 99%

“…The first phase, which acts as pre-processing phase, involves the filter [48] discussed earlier. As the result, a set of relevant features, f, with minimum redundancy and maximum class discriminability is obtained.…”

Section: Decision Fusion Casementioning

confidence: 99%

“…The multichannel EEG and the HRV were processed as in the feature fusion scheme previously described prior to being combined at the decision level. In the current approach, however, the extracted HRV and EEG features were submitted to a two-phase wrapper-based feature selection process described in [48]. The matched feature vectors associated with the HRV and EEG where then fed to the two separate classifiers previously identified as optimum, namely linear and 1-NN classifiers.…”

Section: The Classifier/decision Fusion Approachmentioning

confidence: 99%

See 2 more Smart Citations

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%).

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Relevance and Redundancy Analysis for Ensemble Classifiers

Duangsoithong¹,

Windeatt²

2009

Lecture Notes in Computer Science

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HRV feature selection based on discriminant and redundancy analysis for neonatal seizure detection

Cited by 13 publications

References 17 publications

A Multi-Channel Fusion Based Newborn Seizure Detection

A Multi-Channel Fusion Based Newborn Seizure Detection

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

Relevance and Redundancy Analysis for Ensemble Classifiers

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