PURPOSE: The identification of the epileptogenic zone (EZ) is crucial for planning epilepsy surgery in patients with drug-resistant partial epilepsy. This task may require intracerebral encephalography (EEG) monitoring, the results of which are usually interpreted by visual presurgical inspection. A computer-assisted method for rapidly identifying reproducible ictal patterns based on the analysis of time, frequency, and spatial domains of stereo-EEG (SEEG) signals is described here. METHODS: A new method for EZ detection was tested on SEEG recordings performed by intracerebral electrodes in eight patients with pharmacoresistant partial epilepsy. SEEG data were exported to a program developed in LabView. KEY FINDINGS: Prevalent frequencies during seizure events were evaluated by Fourier transform and further integral algorithms. Different frequencies and the relative powers were simultaneously evaluated in all recording leads. Patterns characterized by specific and prevalent frequencies were identified in a subset of recording sites during both seizure onset and seizure development. Three-dimensional (3D) maps of the measurements obtained from each recording channel were reconstructed on magnetic resonance coordinates to visualize the spatial distribution of the EZ. With this method, the reproducibility of ictal patterns in the same patient was characterized. The boundaries of the EZ identified with this algorithm correlated well with the EZ recognized with the traditional approach (n = 8). The spatial distribution of specific SEEG signals associated with different types of seizures was also analyzed in two patients. SIGNIFICANCE: We describe a computer-assisted method to acquire information on EZ boundaries and to verify reproducibility of seizure patterns from intracerebral recordings performed in patients with pharmacoresistant partial epilepsies. Wiley Periodicals, Inc.
PURPOSE: The identification of the epileptogenic zone (EZ) is crucial for planning epilepsy surgery in patients with drug-resistant partial epilepsy. This task may require intracerebral encephalography (EEG) monitoring, the results of which are usually interpreted by visual presurgical inspection. A computer-assisted method for rapidly identifying reproducible ictal patterns based on the analysis of time, frequency, and spatial domains of stereo-EEG (SEEG) signals is described here. METHODS: A new method for EZ detection was tested on SEEG recordings performed by intracerebral electrodes in eight patients with pharmacoresistant partial epilepsy. SEEG data were exported to a program developed in LabView. KEY FINDINGS: Prevalent frequencies during seizure events were evaluated by Fourier transform and further integral algorithms. Different frequencies and the relative powers were simultaneously evaluated in all recording leads. Patterns characterized by specific and prevalent frequencies were identified in a subset of recording sites during both seizure onset and seizure development. Three-dimensional (3D) maps of the measurements obtained from each recording channel were reconstructed on magnetic resonance coordinates to visualize the spatial distribution of the EZ. With this method, the reproducibility of ictal patterns in the same patient was characterized. The boundaries of the EZ identified with this algorithm correlated well with the EZ recognized with the traditional approach (n = 8). The spatial distribution of specific SEEG signals associated with different types of seizures was also analyzed in two patients. SIGNIFICANCE: We describe a computer-assisted method to acquire information on EZ boundaries and to verify reproducibility of seizure patterns from intracerebral recordings performed in patients with pharmacoresistant partial epilepsies. Wiley Periodicals, Inc.
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