The aim of this paper is to show that the brain activity of patients with acute respiratory failure hospitalized in Intensive Care Units (ICUs) can provide useful medical information, which is directly related to neurological rehabilitation. It also aims to show that the entropy and kurtosis, widely used indices of the electroencephalographic (EEG) signals, are able to identify EEG changes associated with cerebral hypoxia. EEG signals were recorded from eight adult patients with acute respiratory failure admitted to the ICU. The measurements were recorded in five stages, with FiO2 at 40%, 100%, 60%, 20% and 0% (T-piece) respectively. Total time of recordings was 50min (10 min. for each stage). The EEG signals were filtered and further cleaned from ocular and muscular artifacts as well as from the artifacts introduced by other external devices, electrodes movements and electrode’s bad tangencies. Afterwards the 10-min EEG signals of each stage were segmented in ten epochs with one minute fixed length. Then Kurtosis and Shannon’s Entropy were calculated in each segment. One-Way ANOVA verified the assumption that there are statistically significant differences between the various stages of our protocol, while the Scheffe Post-Hoc tests revealed the homogeneous subsets compiled by the aforementioned stage. The results suggest that the EEG is directly connected with the mechanical ventilator’s changes, so in the future, clinicians could probably use the EEG as particularly useful and time-critical information, especially during the weaning procedure from the mechanical ventilator.
|Title of host publication||XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010|
|Number of pages||4|
|Publication status||Published - 2010|
Peranonti, E. G., Klados, M. A., Papadelis, C. L., Kontotasiou, D. G., Kourtidou-Papadeli, C., & Bamidis, P. D. (2010). Can the EEG Indicate the FiO2 Flow of a Mechanical Ventilator in ICU Patients with Respiratory Failure? In XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010 (pp. 827-830). Springer. https://doi.org/10.1007/978-3-642-13039-7_209