A Novel Complexity Algorithm for Quantification of Consciousness Levels

The level of human consciousness is a critical indicator in medicine, essential for the accurate diagnosis of specific conditions such as brain death. This study aims to provide an objective and visual consciousness indicator for medical applications by proposing a new algorithm to quantify consciou...

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Veröffentlicht in:IEEE Access Jg. 12; S. 118665 - 118672
Hauptverfasser: Shen, Chengyuan, Mitsukura, Yasue, Cao, Jianting
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2024
Institute of Electrical and Electronics Engineers (IEEE)
Schlagworte:
Anesthesia
Approximation algorithms
Complexity theory
Consciousness assessment
D-MaxApEn
Electrical engineering. Electronics. Nuclear engineering
Electrodes
Electroencephalography
Glasgow coma scale
Heuristic algorithms
TK1-9971
White noise
ISSN:2169-3536, 2169-3536
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Zusammenfassung:The level of human consciousness is a critical indicator in medicine, essential for the accurate diagnosis of specific conditions such as brain death. This study aims to provide an objective and visual consciousness indicator for medical applications by proposing a new algorithm to quantify consciousness. Traditionally, consciousness levels have been qualitatively assessed using scales like the Glasgow Coma Scale (GCS). In this study, we introduce a novel algorithm, termed D-MaxApEn, designed to quantify consciousness. This algorithm was applied to EEG data from four different states: wakefulness, anesthesia, coma, and brain death. Through calculation and analysis, we objectively quantified these four levels of consciousness and validated the effectiveness of this algorithm in a real-time brain death diagnosis system. The results demonstrate the significant potential of this algorithm for the future diagnosis of the level of consciousness. Its use could revolutionize early intervention, improve patient outcomes, and reduce the burden on individuals and healthcare systems.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3445467