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Time- and frequency-resolved covariance analysis for detection and characterization of seizures from intracraneal EEG recordings
Neurons and Cognition · Submitted 28 Feb 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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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The abstract, sentence by sentence
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\% 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The amount of power in different frequency bands of the electroencephalogram (EEG) carries information about the behavioral state of a subject. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s1 | Hence, neurologists treating epileptic patients monitor the temporal evolution of the different bands. | background 1.00 | 0.05 | 0.01 | asserted 1.00 |
| s2 | We propose a covariance-based method to detect and characterize epileptic seizures operating on the band-filtered EEG signal. | contribution 0.95 | 0.86 | 0.05 | asserted 0.99 |
| s3 | 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. | method 0.97 | 0.90 | 0.04 | asserted 1.00 |
| s4 | Its simplicity makes it suitable for online implementation. | contribution 0.42 | 0.43 | 0.03 | asserted 0.99 |
| s5 | Good sampling of the non-ictal periods is required, while no demands are imposed on the amount of data during ictal activity. | limitation 0.60 | 0.39 | 0.03 | asserted 1.00 |
| s6 | We tested the method with 32 seizures registered in 5 patients. | method 0.88 | 0.96 | 0.02 | asserted 1.00 |
| s7 | The area below the resulting receiver-operating characteristic curves was 87\% for the detection of seizures and 91\% for the detection of recruited electrodes. | result 1.00 | 0.96 | 0.08 | asserted 1.00 |
| s8 | 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. | method 0.46 | 0.96 | 0.06 | asserted 1.00 |
| s9 | 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. | result 0.66 | 0.95 | 0.84 | asserted 0.98 |
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Original abstract
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\% 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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