Untitled

Abstract

An automatic EEG pattern detection unit was developed and tested for the recognition of burst-suppression periods and for the separation of burst from suppression patterns. The median, standard deviation and the 95% edge frequency were computed from single channels of the EEG within a moving window and completed by the continuous computation of frequency band power via an adapted Hilbert resonance filter. These parameters were given to the inputs of two hierarchically arranged artificial neural networks (NNs). The output signals of NNs indicate the suppression and burst phases. The burst recognition was focused on the precise recognition of the burst onset. In subsequent processing steps the time course of percentages of burst patterns within their corresponding burst-suppression-phases was calculated and the time locations of burst onsets can be used to trigger an averaging for a burst-related analysis. The data for our investigations were derived from the routine EEG derivations of 12 patients with various neurosurgical diseases. A group-related training of the NNs was realized. For the group-related trained NNs EEG data for 6 patients were used for training and the data of 6 other patients for testing the classification performance of the pattern recognition units. Additionally, the reliability of the detection algorithm was tested with data of two patients with convulsive state, resistant to treatment, and burst-suppression like pattern EEC.

Real-time segmentation of burst suppression patterns in critical care EEG monitoring

“…At least eight previous publications have presented methods for automated segmentation of burst suppression EEG patterns, based on a variety of EEG features and classification methods, including the nonlinear energy operator, spectral feature clustering, and neural-network classification of adaptive Hilbert-transformed EEG features (Thomsen et al, 1991; Lipping et al, 1995; Arnold et al, 1996; Griessbach et al, 1997; Sherman et al, 1997; Leistritz et al, 1999; Atit et al, 1999). These prior efforts are summarized and improved on in Särkelä et al (2002), the method most closely related to our method.…”

“…Consequently, the results may not generalize to burst suppression EEG patterns of critically ill adults. Furthermore, validation of these methods against human expert clinical EEG segmentation has been limited (Thomsen et al, 1991; Lipping et al, 1995; Arnold et al, 1996; Griessbach et al, 1997; Sherman et al, 1997; Leistritz et al, 1999; Atit et al, 1999; Särkelä et al, 2002). …”

Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression

“…Quantification of burst suppression begins by thresholding and segmenting the EEG [18][19][20][21][22][23]. Thresholding sets a voltage level to separate burst and suppression events.…”

Presence of electroencephalogram burst suppression in sedated, critically ill patients is associated with increased mortality

“…1). This pattern, when induced by sedatives, is considered to be a reliable indicator of very deep drug-induced coma (7, 8). With deepening sedation, the proportion of isoelectric (flat) periods to “bursts” increases until eventually the EEG becomes completely isoelectric (7).…”

“…With deepening sedation, the proportion of isoelectric (flat) periods to “bursts” increases until eventually the EEG becomes completely isoelectric (7). In some clinical settings such as the presence of intracranial hypertension associated with cerebral injury or persistent status epilepticus, deep sedation is clinically beneficial and sedatives are intentionally titrated to achieve EEG burst suppression (8). Burst suppression may also be seen as a result of anoxic brain injury.…”