1. PCI & Auditory ERPs for the diagnosis of disorders of consciousness
Hello everyone,
My name is Leandro Sanz I will introduce you to our research which compares two EEG-based methods for the diagnosis of patients with disorders of consciousness
Leandro Sanz, MD
Coma Science Group
GIGA Consciousness
University & Hospital of Liège, Belgium
4th Congress of the EAN
Lisbon
June 17th 2018 1

2. Disorders of consciousness Differential diagnosis
Introduction
Disorders of consciousness
Wakefulness
Variable awareness & communication
Unresponsive Wakefulness Syndrome
Only reflexive behavior
Minimally Conscious State
Reproducible non-reflexive behaviors
Emergence from the Minimally Conscious State
Functional communication or use of object
After coma, patients can develop DOC where they are awake but show variable awareness & communication skills. While unresponsive patients only show reflexive behaviours, MCS patients exhibit reproducible behaviours such as command following. They emerge from the MCS when they are able to functionally communicate or use an object. The clinical assessment of these patients yields up to 40% of misdiagnosis, even though an accurate diagnosis is crucial to predict prognosis, to offer adequate treatment and guide end-of-life decisions. Therefore, other non-invasive tools such as EEG-based methods are needed to complement the behavioral evaluation.
Differential diagnosis
Clinical misdiagnosis up to 40%
Crucial for prognosis, treatment, end-of-life decisions
EEG-based methods as non-invasive tools to detect consciousness

3. Methods 2 EEG-based methods
1. Transcranial magnetic stimulation combined with EEG
The Perturbational Complexity Index (PCI)
The first approach we used is transcranial magnetic stimulation, a technique where we measure the EEG response of the cortex to a magnetic focal perturbation through the scalp. By integrating the complexity of the response into a single metric, the PCI, we can predict the patient’s level of consciousness.
PCI=0.31

4. Methods 2 EEG-based methods
1. Transcranial magnetic stimulation combined with EEG
The Perturbational Complexity Index (PCI)
We see here an unresponsive patient with local, short, simple response, which yields a PCI under the threshold of consciousness.
PCI=0.20
Unresponsive

5. Methods 2 EEG-based methods Minimally conscious
1. Transcranial magnetic stimulation combined with EEG
The Perturbational Complexity Index (PCI)
Whereas this minimally conscious patient exhibits a far more complex, widespread and long-lasting response which classifies him above the threshold of consciousness.
PCI=0.41
Minimally conscious

6. 2. Auditory evoked potentials
Methods
2 EEG-based methods
2. Auditory evoked potentials
Local-Global oddball paradigm 
Machine-Learning approach
120 EEG markers
Evoked potentials
Spectral measures
Information
Connectivity
 Multivariate classifier calibrated on a dataset of 130 patients (68 unresponsive; 62 minimally conscious)
Regularity 1)
.....
Local Standard Global Standard
Local Deviant Global Deviant
Regularity
For the second method, EEG was recorded while during a standardized auditory oddball paradigm, and 120 EEG markers were computed using a machine-learning approach. These markers reflected either evoked potentials, spectral measures, information or connectivity. From these markers, a multivariate classifier was used to evaluate the level the consciousness, which was calibrated on a dataset of 130 previous patients. 2)
.....
Local Deviant Global Standard
Local Standard Global Deviant

7. Results
25 patients (4 unresponsive; 16 minimally conscious; 5 emergent)
PCI correlates with some individual EEG markers
25 patients with a clinical diagnosis of disorders of consciousness underwent both assessments. On the x axis, you can see individual markers from the machine learning approach, while the y axis shows the PCI value from transcranial magnetic stimulation. The color code indicates the clinical diagnosis. We see that some markers correlate better than others with the PCI value, but there is still some overlap between the different colors.
Kolmogorov complexity
Permutational entropy (θ)
Weighted symbolic mutual information (θ)

8. Results & Discussion
The probability from the multivariate classifier correlates best with the PCI
Congruent results for 19 patients (76%)
Positive correlation between 2 methods
Correct diagnosis for all unresponsive and emergent patients
EEG signatures of consciousness 
reliable predictive models for diagnosis
Using the multivariate classifier with the 120 markers, we observe a better correlation with the PCI, as well as a clear distinction between unresponsive and emergent patients. However, minimally conscious ones are still spread across the graph which might indicate greater variability in their capacity for consciousness.
The correlation between these two independent measures provides evidence that EEG-based methods can be reliably used to detect signatures of consciousness and that further tools should be developed to increase the diagnosis of disorders of consciousness. Thank you for your attention.