[Eeglablist] CEEG Processing and Quantitative EEG Parameter Calculation in ICU Patients With Noisy Data

[Johnson, Thomas]

Hi there,

First of all, I want to thank the EEGLab community in advance for being so generous with their limited time in helping beginner users such as myself. EEG analysis is a rich and complex world, and having a community to help get one started makes it much less intimidating.

Now to my problems:

Setup:
We are a lab studying various neurophysiologic measures in ICU patients that require mechanical circulatory support (e.g. ECMO). As part of this, we have continuous EEG data for extended periods (i.e. 24 hours per day, 3-7 days per study). This data is noisy due to the poor electrical isolation of the ICU environment and the extra machinery an ICU patient requires (e.g. automatically inflating mattress to reduce pressure sores, intermittent pneumatic compression devices to prevent DVT, infusion pumps, ECMO machine, etc.). We have a limited number (typically 21 or 22) of leads in a 10-20 system and collect data at 512 Hz via XLTek before being down-sampled to 256 Hz and saved on disk. We also concurrently measure relative cerebral blood flow (rCBF) for approximately two hours per day with a diffuse correlation spectroscopy (DCS) system that uses near-infrared spectroscopy (NIRS).

For my current project, we are interested in qEEG measures during these 2 hours of EEG and DCS co-measurement. The patients are typically comatose, so there are no experimental ‘events’. The data is purely observational. We are scaling up our project after recently receiving grant funding, and I am trying to make a semi or fully automated data processing pipeline for our group. As the first step in this process, I am comparing the results of a semi-automated pipeline to manually selected segments of the time series that an expert reviewer (i.e. an epileptologist) has deemed to have minimal artifact. My reasoning is as follows: if the qEEG measures are similar between the two methods, the semi-automated pipeline can be used, thus saving valuable time for the epileptologist.

To begin this analysis I have established the following pre-processing workflow after reading the tutorials on the EEGLab website, as well as Makato’s pipeline:


  1.  Import Data with BIOSIG
  2.  Narrow Into DCS Time Range
  3.  Populate Channel Locations Using Known Labels
  4.  Manually Exclude Obviously Bad Channels
  5.  clean_rawdata() to reject bad channels
  6.  Duplicate Dataset Narrowed To Expert Reviewer Time Segments
  7.  1 Hz High-Pass Filter
  8.  Zapline 60 Hz
  9.  Interpolate Removed Channels
  10. Re-reference To Average
  11. Calculate ICA Components
  12. ICLabel
  13. Review & Reject ICA Components

I have a few questions that have arisen after working through this pipeline for the first time.


  1.  I initially tried to exclude channels by only using clean_rawdata, but found that it was missing channels with very high amplitude sinusoidal artifacts (not 60 Hz, but certainly from a machine). I tried adjusting the 'ChannelCriterion' parameter, but this did not work well either at 0.70 or 0.90. I ended up manually excluding these channels so I could move forward. Would this type of artifact be better removed using ICA?
  2.  In a similar vein, is there any benefit to removing line noise prior to doing ICA?
     *   My initial thought is that if we have limited leads and thus limited components to use for analysis, we might as well remove one source of noise of which we have a priori knowledge, and ‘save’ the components for other noise.
     *   However, the idea of removing all noise sources with a single non-destructive technique is appealing.
  3.  Is there a benefit to using epochs in this type of continuous EEG data, i.e. where there are no ‘events’?
     *   My initial thought is that this might be helpful in calculating alpha/delta ratio and other qEEG parameters, as I can leverage the epoch infrastructure to calculate parameters more easily per each epoch.
  4.  Some of the ‘expert-reviewed’ time segments are brief (e.g. 3-5 seconds). If I chose a 5-second epoch length, how does it handle data that contains boundaries within the epoch?
  5.  Is anyone aware of a plugin that will calculate some of these qEEG parameters? It seems common enough in the literature that someone has created the scripts at some point, but I didn’t see any plugin that matched this description on the website.
     *   We are interested in parameters similar to Tjepkema-Cloostermans et al. (2017), https://urldefense.com/v3/__https://pubmed.ncbi.nlm.nih.gov/28430695/__;!!Mih3wA!CO_ifNmKaFvV5SxJrxhMu3ASeZffudYh2CK5B3JUL18YRuXSl4_jREAO7b88pEFwzViEIA1GOqP04VZcG-9lDW3jIbtUFvVZCaM5m60$ . This includes ADR, power, Shannon entropy, delta coherence, regularity.

Thank you in advance for any help you can provide.


Thomas W. Johnson, MD, PhD
Resident Physician, PGY-4
Department of Neurology
University of Rochester Medical Center