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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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Related in: MedlinePlus

RRD examples for Problems II and III. Diagrams are calculated for the referenced classes noted with asterisks. (a)A∗–B, (b)A∗–C, (c)A∗–D, (d)B∗–C, (e)B∗–D, (f)B∗–E, (g)C∗–D, (h)C∗–E, (i)D∗–E.
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Fig8: RRD examples for Problems II and III. Diagrams are calculated for the referenced classes noted with asterisks. (a)A∗–B, (b)A∗–C, (c)A∗–D, (d)B∗–C, (e)B∗–D, (f)B∗–E, (g)C∗–D, (h)C∗–E, (i)D∗–E.

Mentions: Likewise, the accomplished RRD representations of Problems II and III are shown in Figure 8. For the sake of simplicity, the top row holds RRD, for which subset A is the referenced neural activity, middle row holds the B-referenced RRD, and the bottom row has both remaining C-referenced and D-referenced RRD representations. In all cases, the referenced class is noted with asterisk. As seen in the top row, all rhythm weights estimated for classes C and D exhibit different behavior if compared to normal brain activity (subset A∗), in which rhythm weights tend to be higher. When compared to class B, however, only the high normal class rhythms (α and β) increase their contribution. That is, if the referenced class becomes the B class, the obtained RRD shows rhythm contributions that are different from all compared classes. Regarding the epileptic seizure free zones, subset C mostly tends to be confused with D, as already discussed in [6].Figure 8


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)

RRD examples for Problems II and III. Diagrams are calculated for the referenced classes noted with asterisks. (a)A∗–B, (b)A∗–C, (c)A∗–D, (d)B∗–C, (e)B∗–D, (f)B∗–E, (g)C∗–D, (h)C∗–E, (i)D∗–E.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig8: RRD examples for Problems II and III. Diagrams are calculated for the referenced classes noted with asterisks. (a)A∗–B, (b)A∗–C, (c)A∗–D, (d)B∗–C, (e)B∗–D, (f)B∗–E, (g)C∗–D, (h)C∗–E, (i)D∗–E.
Mentions: Likewise, the accomplished RRD representations of Problems II and III are shown in Figure 8. For the sake of simplicity, the top row holds RRD, for which subset A is the referenced neural activity, middle row holds the B-referenced RRD, and the bottom row has both remaining C-referenced and D-referenced RRD representations. In all cases, the referenced class is noted with asterisk. As seen in the top row, all rhythm weights estimated for classes C and D exhibit different behavior if compared to normal brain activity (subset A∗), in which rhythm weights tend to be higher. When compared to class B, however, only the high normal class rhythms (α and β) increase their contribution. That is, if the referenced class becomes the B class, the obtained RRD shows rhythm contributions that are different from all compared classes. Regarding the epileptic seizure free zones, subset C mostly tends to be confused with D, as already discussed in [6].Figure 8

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