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Assessing the state of consciousness is a concern for neuroscientists, psychologists, and anesthesiologists.
However, in clinical practice, there is still no effective tool or "consciousness scale" to distinguish these two dimensions of consciousness.
Many studies use nonlinear analysis methods to describe EEG.
The study subjects were divided into the following groups: (1) healthy volunteers during awake period (2) MCS patients (3) healthy volunteers during general anesthesia; (4) coma patients.
Algorithm flowchart
This study explored the differences in the power spectrum and nonlinear dynamic characteristics of NWC, MCS, coma and general anesthesia.
EEG recording and its multi-dimensional measurement
Non-linear dynamics is an important perspective for studying the nervous system.
In short, the combination of the non-linear measures PE, SampEn, PLZC and DFA with the GA-SVM classifier is the best way to distinguish the state of consciousness.
Z.
Z.
Lianget al .
, " Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine ," inConstructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine IEEE Transactions on Neural Systems and Rehabilitation Engineering , vol.
28, no.
2, pp.
399-408, Feb.
2020, doi: 10.
1109/TNSRE.
2020.
2964819.
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