{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:25:46Z","timestamp":1773771946473,"version":"3.50.1"},"reference-count":82,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Comput. Healthcare"],"published-print":{"date-parts":[[2026,4,30]]},"abstract":"<jats:p>An accurate Depth of Anaesthesia (DoA) assessment is crucial for enhancing a patient\u2019s surgical experience, as it helps avoid intraoperative (anaesthesia) awareness and reduces postoperative recovery time and cognitive dysfunction. This article presents two novel research outcomes. The first is the development of a new DoA index using processed Electroencephalogram (EEG) signals and machine learning techniques. This research uniquely combines powerful features extracted by two distinct time series decomposition methods: the Fast Fourier Transform (FFT) and Variation Mode Decomposition (VMD). Permutation Entropy, Multiscale (Permutation) Lempel\u2013Ziv Complexity, Hjorth\u2019s mobility and Petrosian Fractal Dimension features trained a Support Vector Regressor producing a highly responsive DoA index of 85.8% accuracy. Secondly, a novel fixed-period Loss of Consciousness (LoC) early warning system is developed. Using the same feature set, a KNeighbors (KNN) classifier achieves an accuracy and Area Under the Receiver Operating Characteristic Curve of 82% after Synthetic Minority Oversampling Technique (SMOTE) dataset imbalance correction. The KNN model outperforms Decision Trees, Random Forests and Support Vector classification. Reliably predicting the LoC within a fixed period would greatly assist medical practitioners by ensuring an appropriate level of anaesthetic is administered to achieve the LoC, thus reducing the risk of anaesthesia awareness and preventing anaesthetic overdose.<\/jats:p>","DOI":"10.1145\/3779215","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T14:42:35Z","timestamp":1764945755000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Loss of Consciousness Early Warning Modelling with EEG Signals"],"prefix":"10.1145","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0962-3182","authenticated-orcid":false,"given":"Les","family":"Fish","sequence":"first","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5142-8654","authenticated-orcid":false,"given":"Tianning","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4694-4926","authenticated-orcid":false,"given":"Yan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0020-077X","authenticated-orcid":false,"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, Queensland, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2980370"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13755-022-00178-8"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106480"},{"key":"e_1_3_1_5_2","first-page":"1","article-title":"Monitoring depth of anesthesia based on hybrid features and recurrent neural network","volume":"14","author":"Li R.","year":"2020","unstructured":"R. Li, Q. Wu, J. Liu, Q. Wu, C. Li, and Q. Zhao. 2020. Monitoring depth of anesthesia based on hybrid features and recurrent neural network. Frontiers in Neuroscience 14, Article 26 (2020), 1\u201315.","journal-title":"Frontiers in Neuroscience"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBCAS.2020.2998172"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2021.3058047"},{"key":"e_1_3_1_8_2","first-page":"1","article-title":"Nonlinear analysis of electroencephalogram variability as a measure of the depth of anesthesia","volume":"71","author":"Chen Y. F.","year":"2022","unstructured":"Y. F. Chen, S. Z. Fan, M. F. Abbod, J. S. Shieh, and M. Zhang. 2022. Nonlinear analysis of electroencephalogram variability as a measure of the depth of anesthesia. IEEE Transactions on Instrumentation and Measurement 71 (2022), 1\u201313.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bjae.2020.12.001"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.3934\/mbe.2021257"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.4817"},{"issue":"5","key":"e_1_3_1_12_2","doi-asserted-by":"crossref","first-page":"1450","DOI":"10.1109\/TBME.2021.3053019","article-title":"Real-time EEG signal classification for monitoring and predicting the transition between different anaesthetic states","volume":"68","author":"Nguyen-Ky T.","year":"2021","unstructured":"T. Nguyen-Ky, H. D. Tuan, A. Savkin, M. N. Do, and N. T. T. Van. 2021. Real-time EEG signal classification for monitoring and predicting the transition between different anaesthetic states. IEEE Transactions on Bio-Medical Engineering 68, 5 (2021), 1450\u20131458.","journal-title":"IEEE Transactions on Bio-Medical Engineering"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12871-020-00964-5"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3068481"},{"key":"e_1_3_1_15_2","first-page":"1","article-title":"Real-time depth of anaesthesia assessment based on hybrid statistical features of EEG","volume":"22","author":"Huang Y.","year":"2022","unstructured":"Y. Huang, P. Wen, B. Song, and Y. Li. 2022. Real-time depth of anaesthesia assessment based on hybrid statistical features of EEG. Sensors 22, Article 6099 (2022), 1\u201311.","journal-title":"Sensors"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0246165"},{"issue":"6","key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1109\/TBME.2012.2236649","article-title":"Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals","volume":"60","author":"Nguyen-Ky T.","year":"2013","unstructured":"T. Nguyen-Ky, P. Wen, and Y. Li. 2013. Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals. IEEE Transactions on Bio-Medical Engineering 60, 6 (2013), 1488\u20131498.","journal-title":"IEEE Transactions on Bio-Medical Engineering"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-spr.2015.0114"},{"key":"e_1_3_1_19_2","first-page":"2011","article-title":"A new approach to denoising EEG signals-merger of translation invariant wavelet and ICA","volume":"5","author":"Li Y.","year":"2011","unstructured":"Y. Li and J. Williams. 2011. A new approach to denoising EEG signals-merger of translation invariant wavelet and ICA. International Journal of Biometrics and Bioinformatics 5 (2011), 2011\u20132130.","journal-title":"International Journal of Biometrics and Bioinformatics"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.jvca.2022.07.004"},{"issue":"6","key":"e_1_3_1_21_2","doi-asserted-by":"crossref","first-page":"465","DOI":"10.4097\/kja.21349","article-title":"Raw and processed electroencephalography in modern anesthesia practice: A brief primer on select clinical applications","volume":"74","author":"Lee K. H.","year":"2021","unstructured":"K. H. Lee, T. D. Egan, and K. B. Johnson. 2021. Raw and processed electroencephalography in modern anesthesia practice: A brief primer on select clinical applications. Korean Journal of Anesthesiology 74, 6 (2021), 465\u2013477.","journal-title":"Korean Journal of Anesthesiology"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bja.2019.07.004"},{"issue":"5","key":"e_1_3_1_23_2","doi-asserted-by":"crossref","first-page":"1336","DOI":"10.1213\/01.ANE.0000105867.17108.B6","article-title":"The relationship between bispectral index and electroencephalographic parameters during isoflurane anesthesia","volume":"98","author":"Morimoto Y.","year":"2004","unstructured":"Y. Morimoto, S. Hagihira, Y. Koizumi, K. Ishida, M. Matsumoto, and T. Sakabe. 2004. The relationship between bispectral index and electroencephalographic parameters during isoflurane anesthesia. Anesthesia and Analgesia 98, 5 (2004), 1336\u20131340.","journal-title":"Anesthesia and Analgesia"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0238249"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10877-023-01037-x"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11357-022-00710-4"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1049\/iet-spr.2017.0140"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105116"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01411-5"},{"issue":"3","key":"e_1_3_1_30_2","doi-asserted-by":"crossref","first-page":"192","DOI":"10.4103\/2228-7477.161495","article-title":"Noise estimation in electroencephalogram signal by using volterra series coefficients","volume":"5","author":"Hassani M.","year":"2015","unstructured":"M. Hassani and M. R. Karami. 2015. Noise estimation in electroencephalogram signal by using volterra series coefficients. Journal of Medical Signals and Sensors 5, 3 (2015), 192\u2013200.","journal-title":"Journal of Medical Signals and Sensors"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/frai.2022.1072801","article-title":"Trends in EEG signal feature extraction applications","volume":"5","author":"Singh A. K.","year":"2023","unstructured":"A. K. Singh and S. Krishnan. 2023. Trends in EEG signal feature extraction applications. Frontiers in Artificial Intelligence 5, Article 1072801 (2023), 1\u201317.","journal-title":"Frontiers in Artificial Intelligence"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fncom.2019.00065","article-title":"Selection of the best electroencephalogram channel to predict the depth of anesthesia","volume":"13","author":"Dubost C.","year":"2019","unstructured":"C. Dubost, P. Humbert, A. Benizri, J.-P. Tourtier, N. Vayatis, and P.-P. Vidal. 2019. Selection of the best electroencephalogram channel to predict the depth of anesthesia. Frontiers in Computational Neuroscience 13, Article 65 (2019), 1\u201313.","journal-title":"Frontiers in Computational Neuroscience"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13246-022-01145-z"},{"issue":"3","key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1007\/s13246-016-0459-5","article-title":"Depth of anaesthesia assessment based on adult electroencephalograph beta frequency band","volume":"39","author":"Li T.","year":"2016","unstructured":"T. Li and P. Wen. 2016. Depth of anaesthesia assessment based on adult electroencephalograph beta frequency band. Australasian Physical and Engineering Sciences in Medicine 39, 3 (2016), 773\u2013781.","journal-title":"Australasian Physical and Engineering Sciences in Medicine"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1093\/bjacepd\/mkg106"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-50391-x"},{"key":"e_1_3_1_37_2","unstructured":"The Pandas Development Team. 2024. pandas-dev\/pandas: Pandas. Zenodo."},{"key":"e_1_3_1_38_2","doi-asserted-by":"crossref","unstructured":"S. Siuly Y. Li and P. Wen. 2010. Analysis and classification of EEG signals using a hybrid clustering technique. IEEE\/ICME International Conference on Complex Medical Engineering (2010) 34\u201339. DOI: https:\/\/dx.doi.org\/10.1109\/ICCME.2010.5558875","DOI":"10.1109\/ICCME.2010.5558875"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1097\/00000542-199810000-00023"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-022-07313-3"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bja.2021.04.023"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bja.2022.01.010"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab98da"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2013.2288675"},{"key":"e_1_3_1_46_2","first-page":"1","article-title":"Use of multiple EEG features and artificial neural network to monitor the depth of anesthesia","volume":"19","author":"Gu Y.","year":"2019","unstructured":"Y. Gu, Z. Liang, and S. Hagihira. 2019. Use of multiple EEG features and artificial neural network to monitor the depth of anesthesia. Sensors 19, Article 2499 (2019), 1\u201312.","journal-title":"Sensors"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104644"},{"key":"e_1_3_1_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3323988"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3204076"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00034-023-02496-y"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11760-022-02343-8"},{"key":"e_1_3_1_52_2","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/978-981-13-9113-2_6","volume-title":"EEG Signal Processing and Feature Extraction","author":"Zhang Z.","year":"2019","unstructured":"Z. Zhang. 2019. Spectral and time-frequency analysis. In EEG Signal Processing and Feature Extraction. L. Hu and Z. Zhang (Eds.), Springer Singapore, Singapore, 89\u2013116."},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2980370"},{"issue":"5","key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1002\/bimj.19710130517","article-title":"R\u00e9nyi, A.: Probability theory. Akad\u00e9miai kiad\u00f3, budapest 1970. 666 S","volume":"13","author":"Toutenburg H.","year":"1971","unstructured":"H. Toutenburg. 1971. R\u00e9nyi, A.: Probability theory. Akad\u00e9miai kiad\u00f3, budapest 1970. 666 S. Biometrische Zeitschrift 13, 5 (1971), 366\u2013366.","journal-title":"Biometrische Zeitschrift"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2008.07.004"},{"key":"e_1_3_1_57_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/fncom.2015.00016","article-title":"EEG entropy measures in anesthesia","volume":"9","author":"Liang Z.","year":"2015","unstructured":"Z. Liang, Y. Wang, X. Sun, D. Li, L. J. Voss, J. W. Sleigh, S. Hagihira, and X. Li. 2015. EEG entropy measures in anesthesia. Frontiers in Computational Neuroscience 9, Article 16 (2015), 1\u201317.","journal-title":"Frontiers in Computational Neuroscience"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2016.2542881"},{"key":"e_1_3_1_59_2","first-page":"260","volume-title":"14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC \u201921)","author":"Nieto-del-Amor F.","year":"2021","unstructured":"F. Nieto-del-Amor, Y. Ye-Lin, J. Garcia-Casado, A. Diaz-Martinez, M. G. Mart\u00ednez, R. Monfort-Ortiz, and G. Prats-Boluda. 2021. Dispersion entropy: A measure of electrohysterographic complexity for preterm labor discrimination. In 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC \u201921), 260\u2013267."},{"issue":"3","key":"e_1_3_1_60_2","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1097\/ALN.0000000000004293","article-title":"Brain complexities and anesthesia: Their meaning and measurement","volume":"137","author":"Li D.","year":"2022","unstructured":"D. Li, M. S. Fabus, and J. W. Sleigh. 2022. Brain complexities and anesthesia: Their meaning and measurement. Anesthesiology 137, 3 (2022), 290\u2013302.","journal-title":"Anesthesiology"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1976.1055501"},{"key":"e_1_3_1_62_2","first-page":"1","article-title":"The effect of age on electroencephalogram measures of anesthesia hypnosis: A comparison of BIS, alpha power, Lempel-Ziv complexity and permutation entropy during propofol induction","volume":"14","author":"Biggs D.","year":"2022","unstructured":"D. Biggs, G. Boncompte, J. C. Pedemonte, C. Fuentes, and L. I. Cortinez. 2022. The effect of age on electroencephalogram measures of anesthesia hypnosis: A comparison of BIS, alpha power, Lempel-Ziv complexity and permutation entropy during propofol induction. Aging Neuroscience 14, Article 910886 (2022), 1\u20139.","journal-title":"Aging Neuroscience"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1097\/ALN.0000000000004293"},{"issue":"12","key":"e_1_3_1_64_2","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1109\/10.966601","article-title":"EEG complexity as a measure of depth of anesthesia for patients","volume":"48","author":"Zhang X. S.","year":"2001","unstructured":"X. S. Zhang, R. J. Roy, and E. W. Jensen. 2001. EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Bio-Medical Engineering 48, 12 (2001), 1424\u20131433.","journal-title":"IEEE Transactions on Bio-Medical Engineering"},{"issue":"4","key":"e_1_3_1_65_2","doi-asserted-by":"crossref","first-page":"zsaa226","DOI":"10.1093\/sleep\/zsaa226","article-title":"Changes in EEG permutation entropy in the evening and in the transition from wake to sleep","volume":"44","author":"Hou F.","year":"2021","unstructured":"F. Hou, L. Zhang, B. Qin, G. Gaggioni, X. Liu, and G. Vandewalle. 2021. Changes in EEG permutation entropy in the evening and in the transition from wake to sleep. Sleep 44, 4 (2021), zsaa226.","journal-title":"Sleep"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/0167-2789(88)90081-4"},{"key":"e_1_3_1_67_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.cmpb.2016.05.014","article-title":"Application of Higuchi\u2019s fractal dimension from basic to clinical neurophysiology: A review","volume":"133","author":"Kesi\u0107 S.","year":"2016","unstructured":"S. Kesi\u0107 and S. Z. Spasi\u0107. 2016. Application of Higuchi\u2019s fractal dimension from basic to clinical neurophysiology: A review. Computer Methods and Programs in Biomedicine 133 (2016), 55\u201370.","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"e_1_3_1_68_2","doi-asserted-by":"publisher","DOI":"10.21037\/qims-21-430"},{"issue":"6","key":"e_1_3_1_69_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1523\/ENEURO.0192-20.2020","article-title":"Electrophysiological frequency band ratio measures conflate periodic and aperiodic neural activity","volume":"7","author":"Donoghue T.","year":"2020","unstructured":"T. Donoghue, J. Dominguez, and B. Voytek. 2020. Electrophysiological frequency band ratio measures conflate periodic and aperiodic neural activity. eNeuro 7, 6 (2020), 1\u201314.","journal-title":"eNeuro"},{"key":"e_1_3_1_70_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-020-00744-x"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.88.174102"},{"key":"e_1_3_1_73_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/e23070832","article-title":"Multiscale permutation Lempel\u2013Ziv complexity measure for biomedical signal analysis: Interpretation and application to focal EEG signals","volume":"23","author":"Borowska M.","year":"2021","unstructured":"M. Borowska. 2021. Multiscale permutation Lempel\u2013Ziv complexity measure for biomedical signal analysis: Interpretation and application to focal EEG signals. Entropy 23, Article 832 (2021), 1\u201317.","journal-title":"Entropy"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ad12dc"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1016\/0013-4694(70)90143-4"},{"key":"e_1_3_1_76_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3390\/app12147251","article-title":"Classification of EEG signals for prediction of epileptic seizures","volume":"12","author":"Aslam M. H.","year":"2022","unstructured":"M. H. Aslam, S. M. Usman, S. Khalid, A. Anwar, R. Alroobaea, S. Hussain, J. Almotiri, S. S. Ullah, and A. Yasin. 2022. Classification of EEG signals for prediction of epileptic seizures. Applied Sciences 12, Article 7251 (2022), 1\u201315.","journal-title":"Applied Sciences"},{"key":"e_1_3_1_77_2","doi-asserted-by":"crossref","unstructured":"A. Petrosian. 1995. Kolmogorov complexity of finite sequences and recognition of different preictal EEG patterns. In Proceedings Eighth IEEE Symposium on Computer-Based Medical Systems 212\u2013217. DOI: https:\/\/dx.doi.org\/10.1109\/CBMS.1995.465426","DOI":"10.1109\/CBMS.1995.465426"},{"key":"e_1_3_1_78_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"e_1_3_1_79_2","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/978-1-4302-5990-9_4","volume-title":"Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers","author":"Awad M.","year":"2015","unstructured":"M. Awad and R. Khanna. 2015. Support vector regression. In Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers. M. Awad and R. Khanna (Eds.), Apress, Berkeley, CA, 67\u201380."},{"issue":"3","key":"e_1_3_1_80_2","first-page":"307","article-title":"Measurement in medicine: The analysis of method comparison studies","volume":"32","author":"Altman D. G.","year":"1983","unstructured":"D. G. Altman and J. M. Bland. 1983. Measurement in medicine: The analysis of method comparison studies. Journal of the Royal Statistical Society. Series D (The Statistician) 32, 3 (1983), 307\u2013317.","journal-title":"Journal of the Royal Statistical Society. Series D (The Statistician)"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/8430565"},{"key":"e_1_3_1_82_2","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin et al. 2015. TensorFlow: Large-scale machine learning on heterogeneous systems. Retrieved from https:\/\/www.tensorflow.org\/"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"}],"container-title":["ACM Transactions on Computing for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3779215","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T15:11:42Z","timestamp":1773760302000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3779215"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,17]]},"references-count":82,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4,30]]}},"alternative-id":["10.1145\/3779215"],"URL":"https:\/\/doi.org\/10.1145\/3779215","relation":{},"ISSN":["2691-1957","2637-8051"],"issn-type":[{"value":"2691-1957","type":"print"},{"value":"2637-8051","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,17]]},"assertion":[{"value":"2024-11-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}