Towards a novel monitor of intraoperative awareness: Selecting paradigm settings for a movement-based brain-computer interface

Contains fulltext : 103039.pdf (publisher's version ) (Open Access)During 0.1-0.2% of operations with general anesthesia, patients become aware during surgery. Unfortunately, pharmacologically paralyzed patients cannot seek attention by moving. Their attempted movements may however induce detectable EEG changes over the motor cortex. Here, methods from the area of movement-based brain-computer interfacing are proposed as a novel direction in anesthesia monitoring. Optimal settings for development of such a paradigm are studied to allow for a clinically feasible system. A classifier was trained on recorded EEG data of ten healthy non-anesthetized participants executing 3-second movement tasks. Extensive analysis was performed on this data to obtain an optimal EEG channel set and optimal features for use in a movement detection paradigm. EEG during movement could be distinguished from EEG during non-movement with very high accuracy. After a short calibration session, an average classification rate of 92% was obtained using nine EEG channels over the motor cortex, combined movement and post-movement signals, a frequency resolution of 4 Hz and a frequency range of 8-24 Hz. Using Monte Carlo simulation and a simple decision making paradigm, this translated into a probability of 99% of true positive movement detection within the first two and a half minutes after movement onset. A very low mean false positive rate of <0.01% was obtained. The current results corroborate the feasibility of detecting movement-related EEG signals, bearing in mind the clinical demands for use during surgery. Based on these results further clinical testing can be initiated.9 p

Pojistná smlouva

Import 20/04/2006Prezenční výpůjčkaVŠB - Technická univerzita Ostrava. Ekonomická fakulta. Katedra (119) práv

Relationship Between Postoperative Pain and Overall 30-Day Complications in a Broad Surgical Population: An Observational Study

OBJECTIVE: The aim of this study was to establish the relationship between postoperative pain and 30-day postoperative complications. BACKGROUND: Only scarce data are available on the association between postoperative pain and a broad range of postoperative complications in a large heterogeneous surgical population. METHODS: Having postoperative pain was assessed in 2 ways: the movement-evoked pain score on the Numerical Rating Scale (NRS-MEP) and the patients' opinion whether the pain was acceptable or not. Outcome was the presence of a complication within 30 days after surgery. We used binary logistic regression for the total population and homogeneous subgroups to control for case complexity. Results for homogeneous subgroups were summarized in a meta-analysis using inverse variance weighting. RESULTS: In 1014 patients, 55% experienced moderate-to-severe pain on the first postoperative day. The overall complication rate was 34%. The proportion of patients experiencing postoperative complications increased from 0.25 [95% confidence interval (CI) = 0.21-0.31] for NRS-MEP = 0 to 0.45 (95% CI = 0.36-0.55) for NRS-MEP = 10. Patients who found their pain unacceptable had more complications (adjusted odds ratio = 2.17 (95% CI = 1.51-3.10; P < 0.001)). Summary effect sizes obtained with homogeneous groups were similar to those obtained from the total population who underwent very different types of surgery. CONCLUSIONS: Higher actual postoperative pain scores and unacceptable pain, even on the first postoperative day, are associated with more postoperative complications. Our findings provide important support for the centrality of personalized analgesia in modern perioperative care

Classification rates using ten-fold cross-validation (10-fold) versus using only the 1^st experimental block (1^st block) for training.

<p>Classification rates using ten-fold cross-validation (10-fold) versus using only the 1^st experimental block (1^st block) for training.</p

Grand average time-frequency plot of all 64 channels.

<p>Spatial downsampling was performed with a Surface Laplacian; the period from t = −1.5 to t = −0.5 s was used as the baseline period. Blue colouring represents ERD; red represents ERS. The motor cortex is situated in the central regions (C3–C4). An enlargement of channel C3 is shown in the right-hand corner, with the dashed lines indicating the onset and offset of the auditory cue (task period).</p

Stimulus sequence.

<p>Each sequence started with an auditory sequence instruction, i.e. ‘No Movement’ or ‘Both Arms Movement’. Following the instruction were nine trials consisting of a cue (task) and their corresponding baseline periods used for analysis.</p

Decrease in average classification rate with corresponding standard errors when reducing the number of trials.

<p>Information was used from either t = 0–4 s (ERD), t = 4–6 s (ERS) or both (ERD+ERS). Channel sets correspond to the channel sets in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0044336#pone-0044336-g002" target="_blank">Figure 2</a>. To clearly show the rates for all time periods, data points are slightly shifted to either left or right. The dashed line represents the binomial confidence interval, i.e. all classification rates above this line are significantly better than chance (p = 0.01).</p

EEG Electrode positions used for analysis.

<p>The full set of 64 electrodes was used for computation of the grand average time-frequency plot. The remaining channel sets consisted of 18, 12, 9 and 6 channels. The 4-channel set denotes the Laplacian C3.</p

Cumulative probability of true positive monitor output after start of movement.

<p>Movement was assumed to start at trial 1 (t = 0 s) with a trial duration of 8 seconds. As in this paradigm 4 positive classifications in a row are needed for a positive monitor output, the first possible positive monitor output is at trial 4 i.e. 32 seconds after movement onset. For each subject plus the average of all subjects, the solid line shows the output for the recorded sequences. The dashed lines show the interpolation of this output for another 9 trials using a Monte Carlo simulation.</p

Single trial classification rates per subject using different frequency ranges.

<p>Calculations were done using 9 channels and the combined ERD and ERS periods.</p