{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T19:04:50Z","timestamp":1782846290607,"version":"3.54.5"},"reference-count":46,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n General anesthesia comprises 3 essential components\u2014hypnosis, analgesia, and immobility. Among these, maintaining an appropriate hypnotic state, or anesthetic depth, is crucial for patient safety. Excessively deep anesthesia may lead to hemodynamic instability and postoperative cognitive dysfunction, whereas inadequate anesthesia increases the risk of intraoperative awareness. Electroencephalography (EEG)-based monitoring has therefore become a cornerstone for evaluating anesthetic depth. However, processed electroencephalography (pEEG) indices remain vulnerable to various sources of interference, including electromyographic activity, interindividual variability, and anesthetic drug effects, which can yield inaccurate numerical outputs.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n With recent advances in machine learning, particularly unsupervised learning, data-driven methods that classify signals according to inherent patterns offer new possibilities for anesthetic depth analysis. This study aimed to establish a methodology for automatically identifying anesthesia depth using an unsupervised, machine learning\u2013based clustering approach applied to pEEG data.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n Standard frontal EEG data from participants undergoing elective lumbar spine surgery were retrospectively analyzed, yielding more than 16,000 data points. The signals were filtered with a fourth-order Butterworth bandpass filter and transformed using the fast Fourier transform to estimate power spectral density. Normalized band power ratios for delta, high-theta, alpha, and beta frequencies were extracted as input features. Fuzzy C-Means (FCM) clustering (\n c<\/jats:italic>\n =3,\n m<\/jats:italic>\n =2) was applied to categorize anesthetic depth into slight, proper, and deep clusters.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n FCM clustering successfully identified 3 physiologically interpretable clusters consistent with EEG dynamics during progressive anesthesia. As anesthesia deepened, frontal alpha oscillations became more prominent within a delta-dominant background, while beta activity decreased with loss of consciousness. The fuzzy membership values quantified transitional states and captured the continuum of anesthetic depth. Visualization confirmed strong correspondence among cluster transitions, Patient State Index trends, and spectral density patterns.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study demonstrates the feasibility of using unsupervised machine learning to enhance anesthetic depth assessment. By applying FCM clustering to pEEG data, this approach improves the understanding of anesthesia depth and integrates effectively with existing monitoring modalities. The proposed FCM-based method complements current EEG indices and may assist anesthesia practitioners and even nonanesthesia professionals in assessing anesthetic depth to enhance patient safety.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/77830","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T19:21:02Z","timestamp":1770405662000},"page":"e77830-e77830","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3355-4046","authenticated-orcid":false,"given":"Po-Yu","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2059-4100","authenticated-orcid":false,"given":"Wei-Lun","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9375-0884","authenticated-orcid":false,"given":"Hui-Zen","family":"Hee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4125-8584","authenticated-orcid":false,"given":"Wen-Kuei","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3081-362X","authenticated-orcid":false,"given":"Ching-Hung","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3156-8139","authenticated-orcid":false,"given":"Chien-Kun","family":"Ting","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"issue":"27","key":"R1","doi-asserted-by":"publisher","first-page":"2638","DOI":"10.1056\/NEJMra0808281","article-title":"General anesthesia, sleep, and coma","volume":"363","author":"Brown","journal-title":"N Engl J Med"},{"issue":"5","key":"R2","doi-asserted-by":"publisher","first-page":"1104","DOI":"10.1213\/ANE.0000000000003743","article-title":"Multimodal general anesthesia: a principled approach to producing the drug-induced, reversible coma of anesthesia","volume":"127","author":"Egan","journal-title":"Anesth Analg"},{"issue":"2","key":"R3","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1213\/ane.0b013e318193c634","article-title":"Awareness during anesthesia: risk factors, causes and sequelae: a review of reported cases in the literature","volume":"108","author":"Ghoneim","journal-title":"Anesth Analg"},{"issue":"4","key":"R4","doi-asserted-by":"publisher","DOI":"10.1111\/jnc.70070","article-title":"Molecular mechanisms of propofol-induced cognitive impairment: suppression of critical hippocampal pathways","volume":"169","author":"Zhou","journal-title":"J Neurochem"},{"issue":"2","key":"R5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1097\/EJA.0000000000001876","article-title":"Update of the European Society of Anaesthesiology and Intensive Care Medicine evidence-based and consensus-based guideline on postoperative delirium in adult patients","volume":"41","author":"Aldecoa","journal-title":"Eur J Anaesthesiol"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2025.1638282","article-title":"Effect of electroencephalogram-guided anesthesia on postoperative delirium in older adults after surgery: a systematic review and meta-analysis","volume":"16","author":"Da","journal-title":"Front Neurol"},{"issue":"2","key":"R7","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1213\/ane.0b013e3181a9fc38","article-title":"Practical use of the raw electroencephalogram waveform during general anesthesia: the art and science","volume":"109","author":"Bennett","journal-title":"Anesth Analg"},{"issue":"4","key":"R8","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1097\/ALN.0000000000000841","article-title":"Clinical electroencephalography for anesthesiologists: Part I: background and basic signatures","volume":"123","author":"Purdon","journal-title":"Anesthesiology"},{"issue":"5","key":"R9","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1213\/ANE.0000000000004502","article-title":"American Society for Enhanced Recovery and Perioperative Quality Initiative Joint Consensus Statement on the role of neuromonitoring in perioperative outcomes: electroencephalography","volume":"130","author":"Chan","journal-title":"Anesth Analg"},{"issue":"2","key":"R10","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1097\/ALN.0000000000002960","article-title":"Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations","volume":"132","author":"Hashimoto","journal-title":"Anesthesiology"},{"issue":"2","key":"R11","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.9.2.14","article-title":"Introduction to machine learning, neural networks, and deep learning","volume":"9","author":"Choi","journal-title":"Transl Vis Sci Technol"},{"issue":"3","key":"R12","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1515\/BMT.2010.009","article-title":"On the analysis of BIS stage epochs via fuzzy clustering","volume":"55","author":"Nasibov","journal-title":"Biomed Tech (Berl)"},{"issue":"8","key":"R13","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1016\/j.compbiomed.2006.10.010","article-title":"A study on fuzzy C-means clustering-based systems in automatic spike detection","volume":"37","author":"Inan","journal-title":"Comput Biol Med"},{"issue":"1-2","key":"R14","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/0020-7101(91)90028-d","article-title":"Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering","volume":"28","author":"Krajca","journal-title":"Int J Biomed Comput"},{"issue":"3","key":"R15","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1213\/ANE.0000000000006398","article-title":"A pilot investigation evaluating relative changes in fronto-occipital alpha and beta spectral power as measurement of anesthesia hypnotic depth","volume":"137","author":"Fehrlin","journal-title":"Anesth Analg"},{"issue":"1","key":"R16","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1097\/ANA.0000000000000733","article-title":"Electroencephalographic alpha and delta oscillation dynamics in response to increasing doses of propofol","volume":"34","author":"Guti\u00e9rrez","journal-title":"J Neurosurg Anesthesiol"},{"issue":"10","key":"R17","doi-asserted-by":"publisher","first-page":"2014","DOI":"10.1016\/j.clinph.2017.07.417","article-title":"Electroencephalogram dynamics in children during different levels of anaesthetic depth","volume":"128","author":"Koch","journal-title":"Clin Neurophysiol"},{"issue":"2","key":"R18","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1088\/1361-6579\/38\/2\/116","article-title":"Improved spectrum analysis in EEG for measure of depth of anesthesia based on phase-rectified signal averaging","volume":"38","author":"Liu","journal-title":"Physiol Meas"},{"issue":"11","key":"R19","doi-asserted-by":"publisher","DOI":"10.3390\/s19112499","article-title":"Use of multiple EEG features and artificial neural network to monitor the depth of anesthesia","volume":"19","author":"Gu","journal-title":"Sensors (Basel)"},{"issue":"6","key":"R20","doi-asserted-by":"publisher","first-page":"066030","DOI":"10.1088\/1741-2552\/ac3316","article-title":"Electroencephalogram variability analysis for monitoring depth of anesthesia","volume":"18","author":"Chen","journal-title":"J Neural Eng"},{"issue":"3","key":"R21","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1097\/00000542-200003000-00016","article-title":"Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia","volume":"92","author":"Bruhn","journal-title":"Anesthesiology"},{"issue":"1","key":"R22","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1097\/00000542-200107000-00010","article-title":"Shannon entropy applied to the measurement of the electroencephalographic effects of desflurane","volume":"95","author":"Bruhn","journal-title":"Anesthesiology"},{"issue":"3","key":"R23","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1097\/00000542-200103000-00006","article-title":"Surgical stimulation shifts EEG concentration-response relationship of desflurane","volume":"94","author":"R\u00f6pcke","journal-title":"Anesthesiology"},{"issue":"Suppl 1","key":"R24","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1111\/anae.13739","article-title":"The role and limitations of EEG-based depth of anaesthesia monitoring in theatres and intensive care","volume":"72 Suppl 1","author":"Hajat","journal-title":"Anaesthesia"},{"issue":"5","key":"R25","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1111\/anae.12177","article-title":"The ability of bispectral index to detect intra-operative wakefulness during total intravenous anaesthesia compared with the isolated forearm technique","volume":"68","author":"Russell","journal-title":"Anaesthesia"},{"issue":"3","key":"R26","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1093\/bja\/aeh082","article-title":"The Patient State Index as an indicator of the level of hypnosis under general anaesthesia","volume":"92","author":"Prichep","journal-title":"Br J Anaesth"},{"issue":"7","key":"R27","doi-asserted-by":"publisher","first-page":"774","DOI":"10.23736\/S0375-9393.21.14929-6","article-title":"Update on the SedLine\u00ae algorithm for calculating the Patient State Index of older individuals during general anesthesia: a randomized controlled trial","volume":"87","author":"Obara","journal-title":"Minerva Anestesiol"},{"key":"R28","unstructured":"Operator\u2019s manual: sedline sedation monitor. Masimo Corporation. 2018. URL: https:\/\/techdocs.masimo.com\/globalassets\/techdocs\/pdf\/lab-7373f_master.pdf [Accessed 16-01-2026]"},{"issue":"16","key":"R29","doi-asserted-by":"publisher","first-page":"1127","DOI":"10.1097\/BRS.0000000000004722","article-title":"Duration of surgery and intraoperative blood pressure management are modifiable risk factors for postoperative neurocognitive disorders after spine surgery: results of the Prospective CONFESS Study","volume":"48","author":"M\u00fcller","journal-title":"Spine (Phila Pa 1976)"},{"issue":"1","key":"R30","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3340\/jkns.2014.56.1.28","article-title":"Risk factors for delirium after spine surgery in elderly patients","volume":"56","author":"Seo","journal-title":"J Korean Neurosurg Soc"},{"issue":"5","key":"R31","doi-asserted-by":"publisher","first-page":"975","DOI":"10.1016\/j.bja.2020.12.042","article-title":"Offline comparison of processed electroencephalogram monitors for anaesthetic-induced electroencephalogram changes in older adults","volume":"126","author":"Eagleman","journal-title":"Br J Anaesth"},{"issue":"2","key":"R32","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1016\/j.bja.2020.10.036","article-title":"Effects of noxious stimulation on the electroencephalogram during general anaesthesia: a narrative review and approach to analgesic titration","volume":"126","author":"Garc\u00eda","journal-title":"Br J Anaesth"},{"issue":"Suppl 1","key":"R33","doi-asserted-by":"publisher","first-page":"i27","DOI":"10.1093\/bja\/aev212","article-title":"Changes in the electroencephalogram during anaesthesia and their physiological basis","volume":"115 Suppl 1","author":"Hagihira","journal-title":"Br J Anaesth"},{"issue":"12","key":"R34","doi-asserted-by":"publisher","first-page":"1858","DOI":"10.1007\/s12630-020-01820-3","article-title":"Continuing professional development module: an updated introduction to electroencephalogram-based brain monitoring during intended general anesthesia","volume":"67","author":"Hight","journal-title":"Can J Anaesth"},{"key":"R35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.120426","article-title":"EEG spectral slope: a reliable indicator for continuous evaluation of consciousness levels during propofol anesthesia","volume":"283","author":"Zhang","journal-title":"Neuroimage"},{"issue":"4","key":"R36","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1213\/ANE.0000000000006369","article-title":"Performance of the SEDLine monitor: age dependency and time delay","volume":"137","author":"Obert","journal-title":"Anesth Analg"},{"key":"R37","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2019.00031","article-title":"The application of unsupervised clustering methods to Alzheimer\u2019s disease","volume":"13","author":"Alashwal","journal-title":"Front Comput Neurosci"},{"issue":"1","key":"R38","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s10741-023-10357-8","article-title":"Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review","volume":"29","author":"Nazar","journal-title":"Heart Fail Rev"},{"key":"R39","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102588","article-title":"Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: a systematic review","volume":"142","author":"Tavazzi","journal-title":"Artif Intell Med"},{"issue":"6","key":"R40","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1213\/ANE.0000000000006652","article-title":"The influence of electromyographic on electroencephalogram-based monitoring: putting the forearm on the forehead","volume":"138","author":"Lichtenfeld","journal-title":"Anesth Analg"},{"issue":"3","key":"R41","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1097\/WNP","article-title":"Artifact: recording EEG in special care units","volume":"28","author":"Tatum","journal-title":"J Clin Neurophysiol"},{"issue":"6","key":"R42","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1213\/ANE.0000000000005704","article-title":"The strength of alpha oscillations in the electroencephalogram differently affects algorithms used for anesthesia monitoring","volume":"133","author":"Weyer","journal-title":"Anesth Analg"},{"issue":"2","key":"R43","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1213\/ANE.0000000000001372","article-title":"Clinical performance and safety of closed-loop systems: a systematic review and meta-analysis of randomized controlled trials","volume":"124","author":"Brogi","journal-title":"Anesth Analg"},{"issue":"4","key":"R44","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000023930","article-title":"Bispectral index monitoring of the clinical effects of propofol closed-loop target-controlled infusion: systematic review and meta-analysis of randomized controlled trials","volume":"100","author":"Wang","journal-title":"Medicine (Baltimore)"},{"issue":"1","key":"R45","doi-asserted-by":"publisher","DOI":"10.1186\/s13741-019-0130-2","article-title":"Technical and clinical evaluation of a closed loop TIVA system with SEDLine spectral density monitoring: multicentric prospective cohort study","volume":"9","author":"Castellanos Pe\u00f1aranda","journal-title":"Perioper Med (Lond)"},{"key":"R46","doi-asserted-by":"crossref","unstructured":"Nayak J Naik B Behera HS . Fuzzy c-means (FCM) clustering algorithm: a decade review from 2000 to 2014. In: Computational Intelligence in Data Mining. Vol 2. New Delhi: Springer; 2015:978-981. [doi: 10.1007\/978-81-322-2208-8_14] ISBN: 978-81-322-2208-8_14","DOI":"10.1007\/978-81-322-2208-8_14"}],"container-title":["JMIR Medical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e77830","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T18:10:10Z","timestamp":1782843010000},"score":1,"resource":{"primary":{"URL":"https:\/\/medinform.jmir.org\/2026\/1\/e77830"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,6]]},"references-count":46,"URL":"https:\/\/doi.org\/10.2196\/77830","relation":{},"ISSN":["2291-9694"],"issn-type":[{"value":"2291-9694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,6]]},"article-number":"v14i27e77830"}}