{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:21:38Z","timestamp":1786123298897,"version":"build-2736575974"},"reference-count":44,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"4","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>BACKGROUND:<\/jats:title>\n                    <jats:p>Precise monitoring of anesthetic depth in children receiving propofol anesthesia is crucial. Commercial depth of anesthesia monitoring devices do not account for age-related changes in brain states and provide misleading information regarding the actual depth in young children. Entropy analysis, a typical complexity methodology, has been demonstrated to be a simple and robust tool for monitoring consciousness levels during anesthesia in adults. The validity of entropic measures for depth of anesthesia monitoring in children receiving general anesthesia remains largely unexplored. The age-related entropic feature dynamics during propofol anesthesia are still not clear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>METHODS:<\/jats:title>\n                    <jats:p>We prospectively studied frontal electroencephalogram (EEG) recordings from subjects aged 1 to 18 years receiving propofol anesthesia. We calculated spectral power, permutation entropy (PeEn), sample entropy (SampEn), beta ratio, and bispectral index (BIS) from EEG segments obtained during wakefulness, maintenance, and recovery. PeEn quantifies the randomness of a time series and SampEn quantifies its unpredictability. Both measures convey complexity information on local connectivity within neural circuits for an EEG signal. The accuracy of these EEG measures to distinguish between propofol-induced unresponsiveness and clinical recovery was assessed. The changes in entropic feature dynamics with age during propofol anesthesia were investigated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS:<\/jats:title>\n                    <jats:p>\n                      Seventy-seven subjects were included for analysis. Propofol induced a significant decrease in frontal PeEn (from a median [interquartile range] of 0.75 [0.71\u20130.78] during wakefulness to 0.61 [0.57\u20130.63] during maintenance,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001), which returned to wakefulness levels during recovery (0.75 [0.71\u20130.79]), contrasting with BIS, which remained lower. A significant increase in SampEn was noted from wakefulness to maintenance (0.04 [0.04\u20130.06] vs 0.25 [0.20\u20130.28],\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      &lt; .001). PeEn provided excellent performance for distinguishing between unresponsiveness and clinical recovery at an optimal classification threshold of 0.67 with the accuracy of 96.6%. The distinguishing capability of PeEn appeared superior in toddlers compared to BIS (accuracy: 94.7% vs 88.9%). SampEn also exhibited good distinguishing accuracy of 81.1% at an optimal threshold of 0.18. Frontal PeEn and SampEn, indicating information amount of intracortical neural circuits connectivity, decreased with age during propofol maintenance (\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      = .017 and .026, respectively). The adolescents exhibited significantly lower frontal power, PeEn, and SampEn values during propofol administration.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS:<\/jats:title>\n                    <jats:p>The frontal PeEn served as an excellent indicator for distinguishing propofol-induced unresponsiveness from recovery in children. Frontal complexity, represented by PeEn and SampEn, decreased with age during propofol maintenance, which was hypothesized to reflect sequential neurophysiological development in frontal cortex, particularly its maturation during adolescence.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1213\/ane.0000000000007608","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T17:00:33Z","timestamp":1752166833000},"page":"769-781","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Age-Dependent Entropic Features During Propofol Anesthesia in Developing Brain"],"prefix":"10.1213","volume":"142","author":[{"given":"Yue","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Anesthesiology, Peking University First Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen-Hu","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Yanshan University, Qinhuangdao, China"},{"name":"Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province, Yanshan University, Qinhuangdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Yanshan University, Qinhuangdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Yanshan University, Qinhuangdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui-Ting","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Peking University First Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong-Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Peking University First Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang-Yang","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Peking University Third Hospital, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Li","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou, China"},{"name":"Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Guangzhou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin-Lin","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Peking University First Hospital, Beijing, 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