Detecting temporal lobe seizures from scalp EEG recordings: A comparison of various features
Introduction
Although various bodily functions are measured continuously and on a routine basis in the critical care or stroke unit, the brain is typically still monitored by means of routine bedside clinical testing. Even under the best circumstances, this is a discontinuous and subjective process. Furthermore, when patients are sedated, in coma, or medically paralyzed, this method becomes quite uninformative.
There is increasing awareness that several derangements of brain function may occur in critically ill patients, that are difficult, if not impossible, to detect by clinical examination (Jordan, 1999, Vespa et al., 1999, van Putten, 2003a). Furthermore, in various circumstances changes in brain function may occur several hours before clinical signs become apparent. Examples include progressive brain ischaemia, as may occur in particular stroke patients (van Putten and Tavy, 2004b), or in patients suffering from vasospasm after a subarachnoid hemorrhage.
Recently, Jordan, 1999, Vespa et al., 1999 discussed the influence that continuous EEG monitoring (cEEG) in Intensive Care Units (ICUs) has on management decisions. They suggest that in 58% of the cases cEEG had an important influence on the decisions taken within the ICU. Various other studies stress the importance of cEEG in the ICU as well, see e.g. (Vespa et al., 1997, Jordan, 1993, Kay, 1998, Nuwer, 1999, Scheuer, 2002, Claassen et al., 2004, Vespa, 2005).
In the application of cEEG, there is a need for (semi-) automatic EEG analysis (Agarwal et al., 1998, Kull and Emerson, 2005), for instance for the detection of epileptic activity (Gotman, 1999, Jerger et al., 2001, van Putten, 2003a; Faul et al., 2005, Harrison et al., 2005, Vespa, 2005) or ischaemia (van Putten et al., 2004a, van Putten and Tavy, 2004b). This paper discusses various features and their (triplet) combinations to detect seizure activity from scalp EEG recordings. As performance measures, the sensitivity and the false alarm rate (FAR) are used, as discussed in Section 2.
Section snippets
EEG recordings
EEGs were recorded with a Brainlab digital EEG system (OSG bvba, Belgium) using a sampling frequency fs=250 Hz. Analog filter settings were 0.16–70 Hz. The EEGs were recorded with Ag/AgCl electrodes placed at the Fp2, Fp1, F8, F7, F4, F3, T4, T3, C4, C3, T7, T5, P4, P3, O1, O2, Fz, Cz and Pz loci of the 10–20 International System. Impedance was kept below 50 kΩ to avoid polarization effects. All EEGs were band-filtered between 0.5 and 30 Hz before further analysis. Bipolar montages were applied
Results
Patient characteristics are presented in Table 2. In approximately half of the recordings, epileptiform discharges were unilateral, in the remaining recordings, bilateral discharges were present.
In Fig. 2 we show an example of two EEG recordings. The top recording shows a strong EEG asymmetry during seizure activity. In the lower recording, this is less pronounced, and bilateral epileptiform discharges are present. The corresponding ROC curves are shown in Fig. 3, illustrating the differences
Discussion and conclusions
In the application of cEEG, there is a need for (semi-) automatic EEG analysis (Agarwal et al., 1998), for instance for the detection of epileptic activity (Gotman, 1999, Jerger et al., 2001, van Putten, 2003a) or ischaemia (van Putten et al., 2004a, van Putten and Tavy, 2004b).
In this study, we present a detailed comparison of various features and the potential contribution of combining various features in their ability to detect seizure activity in scalp EEG recordings from patients suffering
Acknowledgments
We thank Prof W. Paeschen and Dr L. Lagae from the Universitair Ziekenhuis Gasthuisberg, Leuven, Belgium, for kindly providing the EEG data and OSG bvba, Belgium, for developing the software tool for conversion of the EEG Brainlab data to the MatLab environment. We also thank the anonymous referees for their useful suggestions and comments.
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2017, Journal of Neuroscience MethodsCitation Excerpt :The selection of features to be extracted was based on the existing literature in an attempt to include features that have been widely used for the analysis of EEG signals. In order to provide an insight into the extracted features we refer to the corresponding studies (Greene et al., 2007; Pippa et al., 2015; van Putten et al., 2005; Petrantonakis and Hadjileontiadis, 2010; Valderrama et al., 2010; Le Van Quyen et al., 1999). Statistical features such as minimum, maximum, mean, variance, standard deviation, percentiles, interquartile range, mean absolute deviation, range, skewness, kyrtosis and energy are used to capture the variations in the amplitude of the EEG signals that accompany the electroencephalographic seizure activity (van Putten et al., 2005; Petrantonakis and Hadjileontiadis, 2010; Valderrama et al., 2010).