Research Article Spectral Gini Index for Quantifying the Depth of Consciousness Kyung-Jin You, 1 Gyu-Jeong Noh, 2,3 and Hyun-Chool Shin 1 1 Department of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea 2 Department of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05535, Republic of Korea 3 Department of Clinical Pharmacology and herapeutics, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05535, Republic of Korea Correspondence should be addressed to Hyun-Chool Shin; shinhc@ssu.ac.kr Received 14 June 2016; Revised 13 September 2016; Accepted 26 September 2016 Academic Editor: Saeid Sanei Copyright © 2016 Kyung-Jin You et al. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We propose indices that describe the depth of consciousness (DOC) based on electroencephalograms (EEGs) acquired during anesthesia. he spectral Gini index (SpG) is a novel index utilizing the inequality in the powers of the EEG spectral components; a similar index is the binarized spectral Gini index (BSpG), which has low computational complexity. A set of EEG data from 15 subjects was obtained during the induction and recovery periods of general anesthesia with propofol. he eicacy of the indices as indicators of the DOC was demonstrated by examining Spearman’s correlation coeicients between the indices and the efect-site concentration of propofol. A higher correlation was observed for SpG and BSpG (0.633 and 0.770, resp.,  < 0.001) compared to the conventional indices. hese results show that the proposed indices can achieve a reliable quantiication of the DOC with simpliied calculations. 1. Introduction he depth of anesthesia (DOA) must be precisely and appro- priately controlled according to the surgical procedure and the patient’s medical condition. For example, inadequate anesthesia may provoke stress responses of the body such as hypertension, tachycardia, sweating, lacrimation, increased skeletal muscle tone, and spontaneous movement [1]. Tachy- cardia and hypertension can lead to various side efects such as a cardiovascular event. In contrast, an anesthetic agent overdose can cause hypotension, which can lead to hypoperfusion of the heart and brain in susceptible patients. Owing to the interpatient variability of the dose-response efect of anesthetic agents, the administration of an adequate amount of anesthetics and the maintenance of an appropriate DOA are challenging. herefore, an objective and reliable method of evaluating the DOA is needed to maintain a stable level of anesthesia. General anesthesia (GA) includes two independent com- ponents: hypnosis and analgesia [2]. Several methods of measuring the DOA are based on the changes in the autonomic nervous system, such as the degree of muscle relaxation, hemodynamics, sweating, and lacrimation [3, 4]. Methods using the heart rate variability relect the changes in brainstem function [5, 6]. However, these parameters are poorly correlated with the cerebral cortex functions, are closely related to consciousness, and constitute poor indicators of the depth of consciousness (DOC) [7, 8]. Intraoperative awareness can occur without monitoring the DOC. Intraoperative awareness is the unexpected explicit recall of sensory perceptions during GA [9] and may occur in 0.1–0.2% of patients receiving GA [10]. Such awareness can lead to mental sequelae and posttraumatic syndrome [11]. herefore, the parameters that monitor the DOC must focus on the electroencephalogram (EEG), which relects the action of the cerebral cortex, of the thalamus, and of the brainstem. Many studies have attempted to develop indices for a quantitative, immediate, and continuous indicator of the DOC based on (sub)cortical electrical activities. Information theoretical approaches, such as the spec- tral entropy [12–14], permutation entropy (PE) [15], and approximate entropy (AE) [16] methods, consider that the irregularity of the EEG change during anesthesia is expressed Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2016, Article ID 2304356, 12 pages http://dx.doi.org/10.1155/2016/2304356