A large variety of methods has been proposed for automatic seizure detection in EEG signals. Those achieving maximal performance are based on machine-learning techniques, which require long training sessions with large labelled databases, and produce a verdict with no intuitive justification. As an alternative, we here explore an unsupervised algorithm applicable to intra-cranial EEG recordings that requires good sampling of the non-ictal periods , but imposes no demands on the amount of data during ictal activity. The algorithm analyses how the amount of power in each frequency band fluctuates ( an evaluation physicians are familiar with ) and can be implemented online. The analysis is performed electrode by electrode, thus providing the spatio-temporal sequence in which the affected regions are recruited into the crisis. We test it with 32 crisis registered in 5 patients , for which we also have the degree of loss of consciousness as determined from a behavioural analysis. The method can achieve a precision of 85\% and a recall of 88\% in the identification of seizures, and 77\% and 88\% in the identification of recruited electrodes. The intuitive nature of the analysis allows us to identify certain physiological features that are correlated with the degree of loss of consciousness. For example, epileptic crisis in which the variance of the power in the alpha band increases at around seizure onset are particularly likely to impair consciousness. We conclude that the PCA of the distribution of power in different frequency bands provides information both about the detection and the characterization of epileptic seizures.\\%DIF > for the detection of seizures and 91\\% for the detection of recruited electrodes. To identify the behaviorally relevant correlates of the physiological signal, we identified transient changes in the variance of each band that were correlated with the degree of loss of consciousness, the latter assessed by the so-called Consciousness Seizure Scale, summarizing the performance of the subject in a number of behavioral tests requested during seizures. We concluded that those crisis with maximal impairment of consciousness tended to exhibit an increase of variance approximately 40 seconds after seizure onset, with predominant power in the theta and alpha bands, and reduced delta and beta activity.
The amount of power in different frequency bands of the electroencephalogram (EEG) carries information about the behavioral state of a subject. Hence, neurologists treating epileptic patients monitor the temporal evolution of the different bands. We propose a covariance-based method to detect and characterize epileptic seizures operating on the band-filtered EEG signal. The algorithm is unsupervised, and performs a principal component analysis of intra-cranial EEG recordings , detecting transient fluctuations of the power in each frequency band. Its simplicity makes it suitable for online implementation. Good sampling of the non-ictal periods is required, while no demands are imposed on the amount of data during ictal activity. We tested the method with 32 seizures registered in 5 patients . The area below the resulting receiver-operating characteristic curves was 87\\%DIF > for the detection of seizures and 91\\% for the detection of recruited electrodes. To identify the behaviorally relevant correlates of the physiological signal, we identified transient changes in the variance of each band that were correlated with the degree of loss of consciousness, the latter assessed by the so-called Consciousness Seizure Scale, summarizing the performance of the subject in a number of behavioral tests requested during seizures. We concluded that those crisis with maximal impairment of consciousness tended to exhibit an increase of variance approximately 40 seconds after seizure onset, with predominant power in the theta and alpha bands, and reduced delta and beta activity.
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A large variety of methods has been proposed for automatic seizure detection in EEG signals. Those achieving maximal performance are based on machine-learning techniques, which require long training sessions with large labelled databases, and produce a verdict with no intuitive justification. As an alternative, we here explore an unsupervised algorithm applicable to
The amount of power in different frequency bands of the electroencephalogram (EEG) carries information about the behavioral state of a subject. Hence, neurologists treating epileptic patients monitor the temporal evolution of the different bands. We propose a covariance-based method to detect and characterize epileptic seizures operating on the band-filtered EEG signal. The algorithm is unsupervised, and performs a principal component analysis of
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that requires good
, detecting transient fluctuations of the power in each frequency band. Its simplicity makes it suitable for online implementation. Good
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, but imposes no demands
is required, while no demands are imposed
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The algorithm analyses how the amount of power in each frequency band fluctuates ( an evaluation physicians are familiar with ) and can be implemented online. The analysis is performed electrode by electrode, thus providing the spatio-temporal sequence in which the affected regions are recruited into the crisis. We test it with
We tested the method with
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crisis
seizures
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, for which we also have the degree of loss of consciousness as determined from a behavioural analysis. The method can achieve a precision of 85\% and a recall of 88\% in the identification of seizures, and 77\% and 88\% in the identification of recruited electrodes. The intuitive nature of the analysis allows us to identify certain physiological features that are correlated with the degree of loss of consciousness. For example, epileptic crisis in which the variance of the power in the alpha band increases at around seizure onset are particularly likely to impair consciousness. We conclude that the PCA of the distribution of power in different frequency bands provides information both about the detection and the characterization of epileptic seizures.
. The area below the resulting receiver-operating characteristic curves was 87