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Extraction of multivariate components in brain signals obtained during general anesthesia

Mariia Fedotenkova 1
1 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Nowadays, surgical operations are impossible to imagine without general anesthesia, which involves loss of consciousness, immobility, amnesia and analgesia. Understanding mechanisms underlying each of these effects guarantees well-controlled medical treatment. This thesis focuses on analgesia effect of general anesthesia, more specifically, on patients reaction to nociceptive stimuli. We also study differences in the reaction between different anesthetic drugs. The study was conducted on dataset consisting of 230 EEG signals: pre- and post-incision recordings obtained form 115 patients, who received desflurane and propofol. The first stage of the study comprise power spectral analysis, which is a widespread approach in signal processing. Spectral information was described by fitting the background activity, that exposes $1/f$ behavior, to power spectral density estimates of the EEG signals and measuring power contained in delta and alpha bands relatively to the power of background activity. A further improvement was done by expanding spectra with time information due to observed non-stationary nature of EEG signals. To obtain time-frequency representations of the signals we apply three different methods: scalogram (based on continuous wavelet transform), conventional spectrogram, and spectrogram reassignment. The latter allows to ameliorate readability of a time-frequency representation by reassigning energy contained in spectrogram to more precise positions. Subsequently, obtained spectrograms were used as phase space reconstruction in recurrence analysis and its quantification by complexity measure. Recurrence analysis allows to describe and visualize recurrent dynamics of a system and discover structural patterns contained in the data. Here, recurrence plots were used as rewriting grammar to turn an original signal into a symbolic sequence, where each symbol represents a certain state of the system. After computing three different complexity measures of resulting symbolic sequences they are used as features for classification. Finally, combining features obtained with power spectral analysis and recurrence symbolic analysis, we perform classification of the data using two classification methods: linear discriminant analysis and support vector machines. Classification was carried out on two-class problem, distinguishing between pre-/post-incision EEG signals, as well as between two different anesthetic drugs, desflurane and propofol.
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Submitted on : Tuesday, December 13, 2016 - 7:38:12 PM
Last modification on : Tuesday, December 18, 2018 - 4:40:21 PM
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  • HAL Id : tel-01527901, version 2

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Mariia Fedotenkova. Extraction of multivariate components in brain signals obtained during general anesthesia. Computer Science [cs]. Université de Lorraine, 2016. English. ⟨NNT : 2016LORR0189⟩. ⟨tel-01527901v2⟩

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