Assessment of postoperative sedation level with spectral EEG parameters

• Published in: Clinical neurophysiology Vol. 113; no. 10; pp. 1633 - 1639
• Main Authors: Yppärilä, Heidi, Korhonen, Ilkka, Westerén-Punnonen, Susanna, Musialowicz, Tadeusz, Partanen, Juhani, Ruokonen, Esko
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier Ireland Ltd 01.10.2002; Elsevier Science
• Subjects: Adult; Aged; Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy; Biological and medical sciences; Brain - physiology; Conscious Sedation - methods; Coronary Artery Bypass; Electroencephalogram; Electroencephalography; Emergency and intensive care: techniques, logistics; Female; Humans; Intensive care medicine; Male; Medical sciences; Middle Aged; Monitoring; Postoperative Period; Prediction probability; Principal component analysis; Probability; Propofol; Regression Analysis; Sedation; Prediction probability; Sedation; Propofol; Regression analysis; Electroencephalogram; Principal component analysis; Human; Heart; Postoperative; Intensive care; Electroencephalography; Spectral analysis; Depth; Electrodiagnosis; General anesthetic; Surveillance; Surgery; Adult; Circulatory system; Technique; Monitoring
• ISSN: 1388-2457; 1872-8952
• DOI: 10.1016/S1388-2457(02)00217-1

Objectives: Several sedation scores have been developed, but still a need exists for an objective method to monitor sedation level during intensive care. Our study presents a procedure for finding a combination of electroencephalogram (EEG) characteristics, which could be used in estimating sedation level. Methods: We measured EEG in 29 cardiac surgical patients prior to and after the cardiac bypass grafting operation at different sedation levels. The clinical assessment of sedation levels was evaluated with the Ramsay Score. Spectral EEG parameters were computed and a linear model to predict postoperative sedation level was constructed by using principal component analysis and regression analysis. Results: Sedation levels modified all computed spectral EEG parameters. The model based on optimal combination of EEG parameters predicted the observed Ramsay Score value with a prediction probability of 88%. Conclusions: This study suggests that a combination of spectral EEG parameters may discriminate between 3 sedation levels: awake, moderate sedation and deep sedation.