{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T23:53:55Z","timestamp":1791244435541,"version":"4.1.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Phys Eng Sci Med"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1007\/s13246-026-01753-z","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:29:54Z","timestamp":1780914594000},"page":"1413-1432","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning based depth of anaesthesia monitoring using EEG: a 4-layer CNN model with PSD and BSR correlation features"],"prefix":"10.1007","volume":"49","author":[{"given":"M. S.","family":"Thasneem","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. R. Remesh","family":"Babu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"issue":"4","key":"1753_CR1","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1093\/bjacepd\/mkg106","volume":"3","author":"SD Whyte","year":"2003","unstructured":"Whyte SD, Booker PD (2003) Monitoring depth of anaesthesia by EEG. BJA CEPD Reviews 3(4):106\u2013110. https:\/\/doi.org\/10.1093\/bjacepd\/mkg106","journal-title":"BJA CEPD Reviews"},{"key":"1753_CR2","unstructured":"Karunarathna I, Hapuarachchi T, Gunasena P, Aluthge P, Perera N, Gunathilake S et al (2024) Guedel\u2019s Classification and Its Relevance in Modern Anaesthesia. Uva Clin Anaesth Intensive Care ISSN 2827\u20137198. https:\/\/www.researchgate.net\/publication\/387165386"},{"key":"1753_CR3","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1111\/anae.13739","volume":"72","author":"Z Hajat","year":"2017","unstructured":"Hajat Z, Ahmad N, Andrzejowski J (2017) The role and limitations of EEG-based depth of anaesthesia monitoring in theatres and intensive care. Anaesthesia 72:38\u201347. https:\/\/doi.org\/10.1111\/anae.13739","journal-title":"Anaesthesia"},{"key":"1753_CR4","unstructured":"Giese C (2023) EEG Approaches to Measuring Depth of Anaesthesia. Sapien Labs Shap. Future Mind Healthhttps:\/\/3.234.186.46\/eeg-approaches-to-measuring-depth-of-anesthesia\/"},{"key":"1753_CR5","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/bja\/aev212","volume":"115","author":"S Hagihira","year":"2015","unstructured":"Hagihira S (2015) Changes in the electroencephalogram during anaesthesia and their physiological basis. Br J Anaesth 115:27\u201331. https:\/\/doi.org\/10.1093\/bja\/aev212","journal-title":"Br J Anaesth"},{"key":"1753_CR6","doi-asserted-by":"publisher","first-page":"2332","DOI":"10.3390\/s25072332","volume":"25","author":"M Zhu","year":"2025","unstructured":"Zhu M, Xu M, Gao M, Yu R, Bin G (2025) Robust EEG characteristics for predicting neurological recovery from coma after cardiac arrest. Sensors 25:2332. https:\/\/doi.org\/10.3390\/s25072332","journal-title":"Sensors"},{"key":"1753_CR7","doi-asserted-by":"publisher","first-page":"1313","DOI":"10.1093\/biomet\/asae033","volume":"111","author":"LC Astfalck","year":"2024","unstructured":"Astfalck LC, Sykulski AM, Cripps EJ (2024) Debiasing Welch\u2019s method for spectral density estimation. Biometrika 111:1313\u20131329. https:\/\/doi.org\/10.1093\/biomet\/asae033","journal-title":"Biometrika"},{"key":"1753_CR8","doi-asserted-by":"publisher","first-page":"5047","DOI":"10.3934\/mbe.2021257","volume":"18","author":"R Madanu","year":"2021","unstructured":"Madanu R, Rahman F, Abbod MF, Fan S-Z, Shieh J-S et al (2021) Depth of anesthesia prediction via EEG signals using convolutional neural network and ensemble empirical mode decomposition. Math Biosci En 18:5047\u20135068. https:\/\/doi.org\/10.3934\/mbe.2021257","journal-title":"Math Biosci En"},{"key":"1753_CR9","doi-asserted-by":"publisher","first-page":"102869","DOI":"10.1016\/j.artmed.2024.102869","volume":"151","author":"T Schmierer","year":"2024","unstructured":"Schmierer T, Li T, Li Y (2024) Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment. Artif Intell Med 151:102869. https:\/\/doi.org\/10.1016\/j.artmed.2024.102869","journal-title":"Artif Intell Med"},{"key":"1753_CR10","doi-asserted-by":"publisher","first-page":"793","DOI":"10.1007\/s13246-022-01145-z","volume":"45","author":"I Alsafy","year":"2022","unstructured":"Alsafy I, Diykh M (2022) Developing a robust model to predict depth of anesthesia from single channel EEG signal. Phys Eng Sci Med 45:793\u2013808. https:\/\/doi.org\/10.1007\/s13246-022-01145-z","journal-title":"Phys Eng Sci Med"},{"key":"1753_CR11","doi-asserted-by":"publisher","first-page":"19","DOI":"10.3390\/sci5020019","volume":"5","author":"RV Anand","year":"2023","unstructured":"Anand RV, Abbod MF, Fan S-Z, Shieh J-S (2023) Depth analysis of anesthesia using EEG signals via time series feature extraction and machine learning. Science 5:19. https:\/\/doi.org\/10.3390\/sci5020019","journal-title":"Science"},{"key":"1753_CR12","doi-asserted-by":"publisher","DOI":"10.1186\/s40708-024-00241-y","author":"T Li","year":"2024","unstructured":"Li T, Huang Y, Wen P et al (2024) Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features. Brain Inf. 11: 28 (2024). https:\/\/doi.org\/10.1186\/s40708-024-00241-y","journal-title":"Brain Inf"},{"key":"1753_CR13","doi-asserted-by":"publisher","first-page":"E1142","DOI":"10.1073\/pnas.1221180110","volume":"110","author":"PL Purdon","year":"2013","unstructured":"Purdon PL, Pierce ET, Mukamel EA, Prerau MJ, Walsh JL, Wong KFK et al (2013) Electroencephalogram signatures of loss and recovery of consciousness from propofol. Proc Natl Acad Sci U S A 110:E1142\u2013E1151. https:\/\/doi.org\/10.1073\/pnas.1221180110","journal-title":"Proc Natl Acad Sci U S A"},{"key":"1753_CR14","doi-asserted-by":"publisher","first-page":"740012","DOI":"10.3389\/fphar.2021.740012","volume":"12","author":"D Wang","year":"2021","unstructured":"Wang D, Guo Q, Liu D, Kong X-X, Xu Z, Zhou Y et al (2021) Association Between Burst-Suppression Latency and Burst-Suppression Ratio Under Isoflurane or Adjuvant Drugs with Isoflurane Anaesthesia in Mice. Front Pharmacol 12:740012. https:\/\/doi.org\/10.3389\/fphar.2021.740012","journal-title":"Front Pharmacol"},{"key":"1753_CR15","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970104","author":"I Daubechies","year":"1992","unstructured":"Daubechies I (1992) Ten Lectures on Wavelets. Soc Industrial Appl Math. https:\/\/doi.org\/10.1137\/1.9781611970104","journal-title":"Soc Industrial Appl Math"},{"key":"1753_CR16","doi-asserted-by":"publisher","unstructured":"Jahankhani P, Kodogiannis V, Revett K (2006) EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks. IEEE John Vincent Atanasoff, Int Symp Mod Comput JVA06. 2006. pp. 120\u20134. https:\/\/doi.org\/10.1109\/JVA.2006.17","DOI":"10.1109\/JVA.2006.17"},{"key":"1753_CR17","doi-asserted-by":"publisher","first-page":"056017","DOI":"10.1088\/1741-2560\/10\/5\/056017","volume":"10","author":"J Chemali","year":"2013","unstructured":"Chemali J, Ching S, Purdon PL, Solt K, Brown EN (2013) Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression. J Neural Eng IOP Publishing 10:056017. https:\/\/doi.org\/10.1088\/1741-2560\/10\/5\/056017","journal-title":"J Neural Eng IOP Publishing"},{"key":"1753_CR18","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1023\/A:1016393904439","volume":"17","author":"M S\u00e4rkel\u00e4","year":"2002","unstructured":"S\u00e4rkel\u00e4 M, Mustola S, Sepp\u00e4nen T et al (2002) Automatic Analysis and Monitoring of Burst Suppression in Anesthesia. J Clin Monit Comput 17:125\u2013134. https:\/\/doi.org\/10.1023\/A:1016393904439","journal-title":"J Clin Monit Comput"},{"key":"1753_CR19","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1093\/bja\/aex054","volume":"118","author":"WG Muhlhofer","year":"2017","unstructured":"Muhlhofer WG, Zak R, Kamal T, Rizvi B, Sands LP, Yuan M et al (2017) Burst-suppression ratio underestimates absolute duration of electroencephalogram suppression compared with visual analysis of intraoperative electroencephalogram. Br J Anaesth 118:755\u2013761. https:\/\/doi.org\/10.1093\/bja\/aex054","journal-title":"Br J Anaesth"},{"issue":"4","key":"1753_CR20","first-page":"145","volume":"2","author":"BE Thomas","year":"2015","unstructured":"Thomas BE, John SK, Abe S (2015) Power spectral density computation using modified Welch method. IJSTE-International J Sci Technol Eng 2(4):145\u2013152","journal-title":"IJSTE-Int J Sci Technol Eng"},{"key":"1753_CR21","doi-asserted-by":"publisher","first-page":"5391","DOI":"10.1002\/hbm.23730","volume":"38","author":"RT Schirrmeister","year":"2017","unstructured":"Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M et al (2017) Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 38:5391\u20135420. https:\/\/doi.org\/10.1002\/hbm.23730","journal-title":"Hum Brain Mapp"},{"key":"1753_CR22","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/s10877-021-00771-4","volume":"36","author":"T Sawa","year":"2022","unstructured":"Sawa T, Yamada T, Obata Y (2022) Power spectrum and spectrogram of EEG analysis during general anesthesia: Python-based computer programming analysis. J Clin Monit Comput 36:609\u2013621. https:\/\/doi.org\/10.1007\/s10877-021-00771-4","journal-title":"J Clin Monit Comput"},{"key":"1753_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3639856.3639863","volume-title":"Proc Third Int Conf AI-ML Syst","author":"NK Sharma","year":"2024","unstructured":"Sharma NK, Shahid S, Kumar S, Sharma S, Gupta RK, Kumar N (2024) Predicting Depth of Anesthesia using EEG Signals and Deep Convolution Network. Proc Third Int Conf AI-ML Syst. Association for Computing Machinery, New York, NY, USA, pp 1\u20138. https:\/\/doi.org\/10.1145\/3639856.3639863."},{"key":"1753_CR24","doi-asserted-by":"publisher","DOI":"10.6084\/m9.figshare.5589841.v1","author":"L Ma","year":"2017","unstructured":"Ma L (2017) EEG and BIS raw data figshare. Dataset. https:\/\/doi.org\/10.6084\/m9.figshare.5589841.v1","journal-title":"Dataset"},{"key":"1753_CR25","doi-asserted-by":"publisher","first-page":"6434","DOI":"10.3390\/s23146434","volume":"23","author":"A Chaddad","year":"2023","unstructured":"Chaddad A, Wu Y, Kateb R, Ahmed B (2023) Electroencephalography signal processing: a comprehensive review analysis of methods techniques. Sensors 23:6434. https:\/\/doi.org\/10.3390\/s23146434","journal-title":"Sensors"},{"issue":"6","key":"1753_CR26","doi-asserted-by":"publisher","first-page":"537","DOI":"10.4313\/JKEM.2009.22.6.537","volume":"22","author":"SW Baik","year":"2009","unstructured":"Baik SW, Ye SY, Park JM, Jeon GR (2009) Development of parameters on depth of anesthesia using power spectrum density analysis during general anesthesia. J Korean Inst Electr Electron Mater Eng 22(6):537\u2013545. https:\/\/doi.org\/10.4313\/JKEM.2009.22.6.537","journal-title":"J Korean Inst Electr Electron Mater Eng"},{"key":"1753_CR27","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1002\/ibra.12186","volume":"11","author":"X He","year":"2025","unstructured":"He X, Li T, Wang X (2025) Research progress on the depth of anesthesia monitoring based on the electroencephalogram. Ibrain 11:32\u201343. https:\/\/doi.org\/10.1002\/ibra.12186","journal-title":"Ibrain"},{"key":"1753_CR28","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1111\/anae.13739","volume":"72","author":"Z Hajat","year":"2017","unstructured":"Hajat Z, Ahmad N, Andrzejowski J (2017) The role and limitations of EEG-based depth of anaesthesia monitoring in theatres and intensive care. Anaesthesia 72:138\u2013147. https:\/\/doi.org\/10.1111\/anae.13739","journal-title":"Anaesthesia"},{"issue":"5","key":"1753_CR29","doi-asserted-by":"publisher","first-page":"056017","DOI":"10.1088\/1741-2560\/10\/5\/056017","volume":"10","author":"J Chemali","year":"2013","unstructured":"Chemali J, Ching S, Purdon PL, Solt K, Brown EN (2013) Burst suppression probability algorithms: state-space methods for tracking EEG burst suppression. J Neural Eng 10(5):056017. https:\/\/doi.org\/10.1088\/1741-2560\/10\/5\/056017","journal-title":"J Neural Eng"},{"key":"1753_CR30","doi-asserted-by":"publisher","first-page":"031001","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik A, He Y, Contreras-Vidal JL (2019) Deep learning for electroencephalogram (EEG) classification tasks: A review. J Neural Eng IOP Publishing Ltd 16:031001. https:\/\/doi.org\/10.1088\/1741-2552\/ab0ab5","journal-title":"J Neural Eng IOP Publishing Ltd"},{"key":"1753_CR31","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1511.08458","author":"K O\u2019Shea","year":"2015","unstructured":"O\u2019Shea K, Nash R (2015) An Introduction to Convolutional Neural Networks. arXiv. https:\/\/doi.org\/10.48550\/arXiv.1511.08458","journal-title":"arXiv"},{"key":"1753_CR32","doi-asserted-by":"publisher","unstructured":"Roy Y, Banville H, Albuquerque I, Gramfort A, Falk TH, Faubert J (2019) Deep learning-based electroencephalography analysis: a systematic review. arXiv; 2019. https:\/\/doi.org\/10.48550\/arXiv.1901.05498","DOI":"10.48550\/arXiv.1901.05498"}],"container-title":["Physical and Engineering Sciences in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-026-01753-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13246-026-01753-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-026-01753-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T23:07:10Z","timestamp":1791241630000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13246-026-01753-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,8]]},"references-count":32,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["1753"],"URL":"https:\/\/doi.org\/10.1007\/s13246-026-01753-z","relation":{},"ISSN":["2662-4729","2662-4737"],"issn-type":[{"value":"2662-4729","type":"print"},{"value":"2662-4737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,8]]},"assertion":[{"value":"18 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no conflict of interest in any financial or non-financial terms.","order":1,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This is an observational study. The Research Ethics Committee of the institution has confirmed that no ethical approval is required.","order":2,"name":"Ethics","label":"Ethical approval","group":{"name":"EthicsHeading","label":"Declarations"}}]}}