Time- and frequency-resolved covariance analysis for detection and characterization of seizures from intracraneal EEG re
- PDF / 2,286,053 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 68 Downloads / 145 Views
ORIGINAL ARTICLE
Time- and frequency-resolved covariance analysis for detection and characterization of seizures from intracraneal EEG recordings Melisa Maidana Capitán1 · Nuria Cámpora2 · Claudio Sebastián Sigvard1 · Silvia Kochen2 · Inés Samengo1 Received: 30 October 2019 / Accepted: 17 June 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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 in variance approximately 40 s after seizure onset, with predominant power in the theta and alpha bands and reduced delta and beta activity. Keywords EEG · Epilepsy · Consciousness · Principal component analysis
1 Introduction Communicated by Benjamin Lindner. This work was supported by Agencia Nacional de Investigaciones Científicas y Técnicas PICT Raíces 2014 N. 1004, Consejo Nacional de Investigaciones Científicas y Técnicas PIP 0256.
B
Inés Samengo [email protected] Melisa Maidana Capitán [email protected] Nuria Cámpora [email protected] Claudio Sebastián Sigvard [email protected] Silvia Kochen [email protected]
1
Instituto Balseiro and Departamento de Física Médica, Centro Atómico Bariloche, San Carlos de Bariloche, Río Negro, Argentina
2
Neurosciences and Complex Systems Unit (ENyS), Consejo Nacional de Investigaciones Científicas y Técnicas, Hospital El Cruce “Néstor Kirchner”, Universidad Nacional Arturo Jauretche, Florencio Varela, Argentina
The last decades have witnessed an explosion in computational techniques aimed at automatically detecting and characterizing epileptic seizures Tzallas et al. (2012), Orosco et al. (2013), Alotaiby et al. (2014), Ulate-Campos et al. (2016), Boubchir et al. (2017). Some a
Data Loading...
