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Identification and monitoring of brain activity based on stochastic relevance analysis of short-time EEG rhythms.

Duque-Muñoz L, Espinosa-Oviedo JJ, Castellanos-Dominguez CG - Biomed Eng Online (2014)

Bottom Line: The obtained results show that the developed relevance analysis was capable of accurately differentiating normal, ictal and interictal activities.The proposed approach provides the reliable identification of traces of interictal/ictal states of epilepsy.The developed variability-based relevance analysis can be translated to other monitoring applications involving time-variant biomedical data.

View Article: PubMed Central - PubMed

Affiliation: Grupo de Automática y Electrónica, Instituto Tecnológico Metropolitano, Medellin, Colombia. leonardoduque@itm.edu.co.

ABSTRACT

Background: The extraction of physiological rhythms from electroencephalography (EEG) data and their automated analyses are extensively studied in clinical monitoring, to find traces of interictal/ictal states of epilepsy.

Methods: Because brain wave rhythms in normal and interictal/ictal events, differently influence neuronal activity, our proposed methodology measures the contribution of each rhythm. These contributions are measured in terms of their stochastic variability and are extracted from a Short Time Fourier Transform to highlight the non-stationary behavior of the EEG data. Then, we performed a variability-based relevance analysis by handling the multivariate short-time rhythm representation within a subspace framework. This maximizes the usability of the input information and preserves only the data that contribute to the brain activity classification. For neural activity monitoring, we also developed a new relevance rhythm diagram that qualitatively evaluates the rhythm variability throughout long time periods in order to distinguish events with different neuronal activities.

Results: Evaluations were carried out over two EEG datasets, one of which was recorded in a noise-filled environment. The method was evaluated for three different classification problems, each of which addressed a different interpretation of a medical problem. We perform a blinded study of 40 patients using the support-vector machine classifier cross-validation scheme. The obtained results show that the developed relevance analysis was capable of accurately differentiating normal, ictal and interictal activities.

Conclusions: The proposed approach provides the reliable identification of traces of interictal/ictal states of epilepsy. The introduced relevance rhythm diagrams of physiological rhythms provides effective means of monitoring epileptic seizures; additionally, these diagrams are easily implemented and provide simple clinical interpretation. The developed variability-based relevance analysis can be translated to other monitoring applications involving time-variant biomedical data.

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Performed classifier accuracy.A–E bi–class problem for both databases, blue line DB1, red line DB2. Classification accuracy was computed when individually adding their weights ranked by their decreasing relevance. As labeled on the horizontal axis: 1: δ, 2: δ+θ, 3: δ+θ+α, 4: δ+θ+α+β.
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Fig5: Performed classifier accuracy.A–E bi–class problem for both databases, blue line DB1, red line DB2. Classification accuracy was computed when individually adding their weights ranked by their decreasing relevance. As labeled on the horizontal axis: 1: δ, 2: δ+θ, 3: δ+θ+α, 4: δ+θ+α+β.

Mentions: EEG segments were categorized using the support-vector-machines-based with the RBF kernel. To extend the basic binary capabilities of the SVM classifier to a multi–class tool, we employed a one-against-all algorithm to categorize a given input EEG segment among the three studied classes (i.e., normal, interictal and ictal). Figure 5 shows the estimated accuracy computed when individually adding weights of short–time rhythms of the bi–class problem by decreasing relevance. Estimated weights can be grouped according to the following four training scenarios (indicated on the horizontal axis): 1) δ rhythm, 2) →δ+θ, 3) →δ+θ+α, and 4) →δ+θ+α+β. When validation was carried out over both databases, it is clear that low frequency band rhythms provide significant contributions. The starting contribution of the δ rhythm is high, and is the greatest when considering both δ+θ. However, classifier performances diminished when adding higher low band rhythms.Figure 5


Identification and monitoring of brain activity based on stochastic relevance analysis of short-time EEG rhythms.

Duque-Muñoz L, Espinosa-Oviedo JJ, Castellanos-Dominguez CG - Biomed Eng Online (2014)

Performed classifier accuracy.A–E bi–class problem for both databases, blue line DB1, red line DB2. Classification accuracy was computed when individually adding their weights ranked by their decreasing relevance. As labeled on the horizontal axis: 1: δ, 2: δ+θ, 3: δ+θ+α, 4: δ+θ+α+β.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4459461&req=5

Fig5: Performed classifier accuracy.A–E bi–class problem for both databases, blue line DB1, red line DB2. Classification accuracy was computed when individually adding their weights ranked by their decreasing relevance. As labeled on the horizontal axis: 1: δ, 2: δ+θ, 3: δ+θ+α, 4: δ+θ+α+β.
Mentions: EEG segments were categorized using the support-vector-machines-based with the RBF kernel. To extend the basic binary capabilities of the SVM classifier to a multi–class tool, we employed a one-against-all algorithm to categorize a given input EEG segment among the three studied classes (i.e., normal, interictal and ictal). Figure 5 shows the estimated accuracy computed when individually adding weights of short–time rhythms of the bi–class problem by decreasing relevance. Estimated weights can be grouped according to the following four training scenarios (indicated on the horizontal axis): 1) δ rhythm, 2) →δ+θ, 3) →δ+θ+α, and 4) →δ+θ+α+β. When validation was carried out over both databases, it is clear that low frequency band rhythms provide significant contributions. The starting contribution of the δ rhythm is high, and is the greatest when considering both δ+θ. However, classifier performances diminished when adding higher low band rhythms.Figure 5

Bottom Line: The obtained results show that the developed relevance analysis was capable of accurately differentiating normal, ictal and interictal activities.The proposed approach provides the reliable identification of traces of interictal/ictal states of epilepsy.The developed variability-based relevance analysis can be translated to other monitoring applications involving time-variant biomedical data.

View Article: PubMed Central - PubMed

Affiliation: Grupo de Automática y Electrónica, Instituto Tecnológico Metropolitano, Medellin, Colombia. leonardoduque@itm.edu.co.

ABSTRACT

Background: The extraction of physiological rhythms from electroencephalography (EEG) data and their automated analyses are extensively studied in clinical monitoring, to find traces of interictal/ictal states of epilepsy.

Methods: Because brain wave rhythms in normal and interictal/ictal events, differently influence neuronal activity, our proposed methodology measures the contribution of each rhythm. These contributions are measured in terms of their stochastic variability and are extracted from a Short Time Fourier Transform to highlight the non-stationary behavior of the EEG data. Then, we performed a variability-based relevance analysis by handling the multivariate short-time rhythm representation within a subspace framework. This maximizes the usability of the input information and preserves only the data that contribute to the brain activity classification. For neural activity monitoring, we also developed a new relevance rhythm diagram that qualitatively evaluates the rhythm variability throughout long time periods in order to distinguish events with different neuronal activities.

Results: Evaluations were carried out over two EEG datasets, one of which was recorded in a noise-filled environment. The method was evaluated for three different classification problems, each of which addressed a different interpretation of a medical problem. We perform a blinded study of 40 patients using the support-vector machine classifier cross-validation scheme. The obtained results show that the developed relevance analysis was capable of accurately differentiating normal, ictal and interictal activities.

Conclusions: The proposed approach provides the reliable identification of traces of interictal/ictal states of epilepsy. The introduced relevance rhythm diagrams of physiological rhythms provides effective means of monitoring epileptic seizures; additionally, these diagrams are easily implemented and provide simple clinical interpretation. The developed variability-based relevance analysis can be translated to other monitoring applications involving time-variant biomedical data.

Show MeSH
Related in: MedlinePlus