{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T16:52:42Z","timestamp":1776963162698,"version":"3.51.4"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61836011"],"award-info":[{"award-number":["61836011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"crossref","award":["N2104001"],"award-info":[{"award-number":["N2104001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s10489-024-05669-7","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T09:04:42Z","timestamp":1720602282000},"page":"9105-9135","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Better electrobiological markers and a improved automated diagnostic classifier for schizophrenia\u2014based on a new EEG effective information estimation framework"],"prefix":"10.1007","volume":"54","author":[{"given":"Tianyu","family":"Jing","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3131-7740","authenticated-orcid":false,"given":"Jiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifen","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengbin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xindong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longyue","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"5669_CR1","doi-asserted-by":"crossref","unstructured":"Green MF, Horan WP, Lee JJNRN (2015) Social cognition in schizophrenia 16(10):620\u2013631","DOI":"10.1038\/nrn4005"},{"key":"5669_CR2","doi-asserted-by":"publisher","unstructured":"Balasubramanian K, Ramya K, Gayathri Devi K (2022) Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from eeg signals. Cogn Neurodyn 19. https:\/\/doi.org\/10.1007\/s11571-022-09817-y","DOI":"10.1007\/s11571-022-09817-y"},{"key":"5669_CR3","doi-asserted-by":"crossref","unstructured":"Cohen MX (2014) Analyzing Neural Time Series Data: Theory and Practice. MIT press, ???","DOI":"10.7551\/mitpress\/9609.001.0001"},{"key":"5669_CR4","doi-asserted-by":"crossref","unstructured":"Sharma M, Deb D, Acharya UR (2018) A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic eeg signals. Appl Int 48:1368\u20131378","DOI":"10.1007\/s10489-017-1042-9"},{"key":"5669_CR5","doi-asserted-by":"crossref","unstructured":"Sadiq MT, Akbari H, Siuly S, Li Y, Wen P (2022) Alcoholic eeg signals recognition based on phase space dynamic and geometrical features. Chaos, Soliton Fract 158:112036","DOI":"10.1016\/j.chaos.2022.112036"},{"key":"5669_CR6","doi-asserted-by":"crossref","unstructured":"Seal A, Bajpai R, Karnati M, Agnihotri J, Yazidi A, Herrera-Viedma E, Krejcar O (2023) Benchmarks for machine learning in depression discrimination using electroencephalography signals. Appl Int 53(10):12666\u201312683","DOI":"10.1007\/s10489-022-04159-y"},{"key":"5669_CR7","doi-asserted-by":"crossref","unstructured":"Zhong X, Gu Y, Luo Y, Zeng X, Liu G (2023) Bi-hemisphere asymmetric attention network: recognizing emotion from eeg signals based on the transformer. Appl Intell 53(12):15278\u201315294","DOI":"10.1007\/s10489-022-04228-2"},{"key":"5669_CR8","doi-asserted-by":"crossref","unstructured":"Barros C, Silva CA, Pinheiro AP (2021) Advanced eeg-based learning approaches to predict schizophrenia: Promises and pitfalls. Artif Intell Med 114:13. https:\/\/doi.org\/10.1016\/j.artmed.2021.102039","DOI":"10.1016\/j.artmed.2021.102039"},{"key":"5669_CR9","doi-asserted-by":"crossref","unstructured":"Khare SK, March S, Barua PD, Gadre VM, Acharya UR (2023) Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade. Inf Fusion, 101898","DOI":"10.1016\/j.inffus.2023.101898"},{"key":"5669_CR10","doi-asserted-by":"crossref","unstructured":"Ranjan R, Sahana BC, Bhandari AK (2024) Deep learning models for diagnosis of schizophrenia using eeg signals: Emerging trends, challenges, and prospects. Arch Comput Methods Eng 1\u201340","DOI":"10.1007\/s11831-023-10047-6"},{"key":"5669_CR11","doi-asserted-by":"crossref","unstructured":"Jiang X, Bian GB, Tian Z (2019) Removal of artifacts from eeg signals: A review. Sensors (Basel) 19(5):18. https:\/\/doi.org\/10.3390\/s19050987","DOI":"10.3390\/s19050987"},{"key":"5669_CR12","doi-asserted-by":"crossref","unstructured":"Manis G, Aktaruzzaman M, Sassi R (2017) Bubble entropy: An entropy almost free of parameters. IEEE Trans Biomed Eng 64(11):2711\u20132718","DOI":"10.1109\/TBME.2017.2664105"},{"key":"5669_CR13","doi-asserted-by":"crossref","unstructured":"Bigdely-Shamlo N, Mullen T, Kothe C, Su KM, Robbins KA (2015) The prep pipeline: standardized preprocessing for large-scale eeg analysis. Front neuroinform 9:16","DOI":"10.3389\/fninf.2015.00016"},{"key":"5669_CR14","doi-asserted-by":"crossref","unstructured":"Di Flumeri G, Arico P, Borghini G, Colosimo A, Babiloni F (2016) A new regression-based method for the eye blinks artifacts correction in the eeg signal, without using any\u00a0eog channel. In: 38th Annual International Conference of the IEEE-Engineering-in-Medicineand- Biology-Society (EMBC). IEEE Eng Med Biol Soc Conf Proceed, pp 3187\u20133190. Ieee, NEW YORK.<GotoISI>:\/\/WOS:000399823503135","DOI":"10.1109\/EMBC.2016.7591406"},{"key":"5669_CR15","doi-asserted-by":"crossref","unstructured":"Yang BH, Zhang T, Zhang YY, Liu WQ, Wang JG, Duan KW (2017) Removal of electrooculogram artifacts from electroencephalogram using canonical correlation analysis with ensemble empirical mode decomposition. Cogn Comput 9(5):626\u2013633. https:\/\/doi.org\/10.1007\/s12559-017-9478-0","DOI":"10.1007\/s12559-017-9478-0"},{"key":"5669_CR16","doi-asserted-by":"crossref","unstructured":"Rabcan J, Levashenko V, Zaitseva E, Kvassay M (2020) Review of methods for eeg signal classification and development of new fuzzy classification-based approach. Ieee Access 8:189720\u2013189734. https:\/\/doi.org\/10.1109\/access.2020.3031447","DOI":"10.1109\/ACCESS.2020.3031447"},{"key":"5669_CR17","doi-asserted-by":"crossref","unstructured":"Geiger BC, Kubin G (2018) Information Loss in Deterministic Signal Processing Systems. Springer, ???","DOI":"10.1007\/978-3-319-59533-7"},{"key":"5669_CR18","doi-asserted-by":"crossref","unstructured":"Oh SL, Vicnesh J, Ciaccio EJ, Yuvaraj R, Acharya UR (2019) Deep convolutional neural network model for automated diagnosis of schizophrenia using eeg signals. Appl Sci Basel 9(14):13. https:\/\/doi.org\/10.3390\/app9142870","DOI":"10.3390\/app9142870"},{"key":"5669_CR19","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang Z, McCane B, Neo P (2018) Emotionet: A 3-d convolutional neural network for eeg-based emotion recognition. In: 2018 Int Joint Conf Neural Netw (IJCNN), pp 1\u20137. IEEE","DOI":"10.1109\/IJCNN.2018.8489715"},{"key":"5669_CR20","doi-asserted-by":"crossref","unstructured":"Rad\u00fcntz T, Scouten J, Hochmuth O, Meffert B (2017) Automated eeg artifact elimination by applying machine learning algorithms to ica-based features. J Neural Eng 14(4):046004","DOI":"10.1088\/1741-2552\/aa69d1"},{"key":"5669_CR21","doi-asserted-by":"crossref","unstructured":"Sweeney KT, Ward TE, McLoone SF (2012) Artifact removal in physiological signals\u2013practices and possibilities. IEEE Trans Inf Technol Biomed 16(3):488\u2013500","DOI":"10.1109\/TITB.2012.2188536"},{"key":"5669_CR22","doi-asserted-by":"crossref","unstructured":"Sadiq MT, Yu X, Yuan Z, Aziz MZ (2020) Motor imagery bci classification based on novel two-dimensional modelling in empirical wavelet transform. Electron Lett 56(25):1367\u20131369","DOI":"10.1049\/el.2020.2509"},{"key":"5669_CR23","doi-asserted-by":"crossref","unstructured":"Zangeneh Soroush M, Tahvilian P, Nasirpour MH, Maghooli K, Sadeghniiat Haghighi K, Vahid Harandi S, Abdollahi Z, Ghazizadeh A, Jafarnia Dabanloo N (2022) Eeg artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms. Front Physiol, 1572","DOI":"10.3389\/fphys.2022.910368"},{"key":"5669_CR24","doi-asserted-by":"crossref","unstructured":"Sabeti M, Katebi S, Boostani R (2009) Entropy and complexity measures for eeg signal classification of schizophrenic and control participants. Artif Intell Med 47(3):263\u2013274","DOI":"10.1016\/j.artmed.2009.03.003"},{"key":"5669_CR25","doi-asserted-by":"crossref","unstructured":"Parvinnia E, Sabeti M, Jahromi MZ, Boostani R (2014) Classification of eeg signals using adaptive weighted distance nearest neighbor algorithm. J King Saud Univ-Comput Inf Sci 26(1):1\u20136","DOI":"10.1016\/j.jksuci.2013.01.001"},{"key":"5669_CR26","doi-asserted-by":"crossref","unstructured":"Murphy JR, Rawdon C, Kelleher I, Twomey D, Markey PS, Cannon M, Roche RA (2013) Reduced duration mismatch negativity in adolescents with psychotic symptoms: further evidence for mismatch negativity as a possible biomarker for vulnerability to psychosis. BMC psychiatry 13:1\u20137","DOI":"10.1186\/1471-244X-13-45"},{"key":"5669_CR27","doi-asserted-by":"crossref","unstructured":"Jahmunah V, Oh SL, Rajinikanth V, Ciaccio EJ, Cheong KH, Arunkumar N, Acharya UR (2019) Automated detection of schizophrenia using nonlinear signal processing methods. Artif Intell Med 100:101698","DOI":"10.1016\/j.artmed.2019.07.006"},{"key":"5669_CR28","doi-asserted-by":"crossref","unstructured":"Prabhakar SK, Rajaguru H, Sun Hee K (2020) Schizophrenia eeg signal classification based on swarm intelligence computing. Comput Intell Neurosci : CIN 2020","DOI":"10.1155\/2020\/8853835"},{"key":"5669_CR29","doi-asserted-by":"crossref","unstructured":"Sadiq MT, Yu X, Yuan Z, Zeming F, Rehman AU, Ullah I, Li G, Xiao G (2019) Motor imagery eeg signals decoding by multivariate empirical wavelet transform-based framework for robust brain\u2013computer interfaces. IEEE access 7:171431\u2013171451","DOI":"10.1109\/ACCESS.2019.2956018"},{"key":"5669_CR30","doi-asserted-by":"crossref","unstructured":"Sadiq MT, Yu X, Yuan Z, Aziz MZ, Rehman N, Ding W, Xiao G (2022) Motor imagery bci classification based on multivariate variational mode decomposition. IEEE Trans Emerg Top Comput Intell 6(5):1177\u20131189","DOI":"10.1109\/TETCI.2022.3147030"},{"key":"5669_CR31","doi-asserted-by":"crossref","unstructured":"Krishnan PT, Raj ANJ, Balasubramanian P, Chen Y (2020) Schizophrenia detection using multivariateempirical mode decomposition and entropy measures from multichannel eeg signal. Biocybern Biomed Eng 40(3):1124\u20131139","DOI":"10.1016\/j.bbe.2020.05.008"},{"key":"5669_CR32","doi-asserted-by":"crossref","unstructured":"Baygin M (2021) An accurate automated schizophrenia detection using tqwt and statistical moment based feature extraction. Biomed Signal Process Control 68:102777","DOI":"10.1016\/j.bspc.2021.102777"},{"key":"5669_CR33","doi-asserted-by":"crossref","unstructured":"Khare SK, Bajaj V (2021) A self-learned decomposition and classification model for schizophrenia diagnosis. Comput Methods Prog Biomed 211:106450","DOI":"10.1016\/j.cmpb.2021.106450"},{"key":"5669_CR34","doi-asserted-by":"crossref","unstructured":"Sharma G, Joshi AM (2022) Szhnn: a novel and scalable deep convolution hybrid neural network framework for schizophrenia detection using multichannel eeg. IEEE Trans Instrum Meas 71:1\u20139","DOI":"10.1109\/TIM.2022.3212040"},{"key":"5669_CR35","doi-asserted-by":"crossref","unstructured":"Shen M, Wen P, Song B, Li Y (2024) 3d convolutional neural network for schizophrenia detection using as eeg-based functional brain network. Biomed Signal Process Control 89:105815","DOI":"10.1016\/j.bspc.2023.105815"},{"key":"5669_CR36","doi-asserted-by":"crossref","unstructured":"Khare SK, Bajaj V, Acharya UR (2023) Schizonet: a robust and accurate margenau\u2013 hill time-frequency distribution based deep neural network model for schizophrenia detection using eeg signals. Physiol Meas 44(3):035005","DOI":"10.1088\/1361-6579\/acbc06"},{"key":"5669_CR37","doi-asserted-by":"crossref","unstructured":"Z\u00fclfikar A, Mehmet A (2022) Empirical mode decomposition and convolutional neural network-based approach for diagnosing psychotic disorders from eeg signals. Appl Intell 52(11):12103\u201312115","DOI":"10.1007\/s10489-022-03252-6"},{"key":"5669_CR38","doi-asserted-by":"crossref","unstructured":"Jakubovitz D, Giryes R, Rodrigues MRD (2019) In: Boche H, Caire G, Calderbank R, Kutyniok G, Mathar R, Petersen P (eds.) Generalization Error in Deep Learning, Springer, Cham, pp 153\u2013193. https:\/\/doi.org\/10.1007\/978-3-319-73074-5_5. https:\/\/doi.org\/10.1007\/978-3-319-73074-5_5","DOI":"10.1007\/978-3-319-73074-5_5"},{"key":"5669_CR39","doi-asserted-by":"crossref","unstructured":"Olejarczyk E, Jernajczyk W (2017) Graph-based analysis of brain connectivity in schizophrenia. PloS one 12(11):0188629","DOI":"10.1371\/journal.pone.0188629"},{"key":"5669_CR40","doi-asserted-by":"crossref","unstructured":"Sadiq MT, Yu X, Yuan Z, Aziz MZ, Siuly S, Ding W (2021) Toward the development of versatile brain\u2013computer interfaces. IEEE Trans Artif Intell 2(4):314\u2013328. https:\/\/doi.org\/10.1109\/TAI.2021.3097307","DOI":"10.1109\/TAI.2021.3097307"},{"key":"5669_CR41","doi-asserted-by":"crossref","unstructured":"Borisov S, Kaplan AY, Gorbachevskaya N, Kozlova I (2005) Analysis of eeg structural synchrony in adolescents with schizophrenic disorders. Hum Physiol 31:255\u2013261","DOI":"10.1007\/s10747-005-0042-z"},{"key":"5669_CR42","doi-asserted-by":"crossref","unstructured":"Mitra P (2007) Observed Brain Dynamics. Oxford University Press, ???","DOI":"10.1093\/acprof:oso\/9780195178081.001.0001"},{"key":"5669_CR43","doi-asserted-by":"crossref","unstructured":"Mullen T, Kothe C, Chi YM, Ojeda A, Kerth T, Makeig S, Cauwenberghs G, Jung TP (2013) : Ieee: Real-time modeling and 3d visualization of source dynamics and connectivity using wearable eeg. In: 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and- Biology-Society (EMBC). IEEE Eng Med Biol Soc Conf Proceed, pp. 2184\u20132187. Ieee,NEW YORK.<GotoISI>:\/\/WOS:000341702102164","DOI":"10.1109\/EMBC.2013.6609968"},{"key":"5669_CR44","doi-asserted-by":"crossref","unstructured":"Hyv\u00e4rinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4):411\u2013430. https:\/\/doi.org\/10.1016\/S0893-6080(00)00026-5","DOI":"10.1016\/S0893-6080(00)00026-5"},{"key":"5669_CR45","doi-asserted-by":"crossref","unstructured":"Hyvarinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. Ieee Trans Neural Netw 10(3):626\u2013634. https:\/\/doi.org\/10.1109\/72.761722","DOI":"10.1109\/72.761722"},{"key":"5669_CR46","doi-asserted-by":"crossref","unstructured":"Buettner R, Beil D, Scholtz S, Djemai A (2020) Development of a machine learning based algorithm to accurately detect schizophrenia based on one-minute eeg recordings","DOI":"10.24251\/HICSS.2020.393"},{"key":"5669_CR47","doi-asserted-by":"crossref","unstructured":"Buettner R, Hirschmiller M, Schlosser K, R\u00f6ssle M, Fernandes M, Timm IJ (2019) Highperformance exclusion of schizophrenia using a novel machine learning method on eeg data. In: 2019 IEEE Int Conf E-Health Netw Appl Serv (HealthCom) pp 1\u20136. IEEE","DOI":"10.1109\/HealthCom46333.2019.9009437"},{"key":"5669_CR48","doi-asserted-by":"crossref","unstructured":"Singh K, Singh S, Malhotra J (2021) Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients. Proceedings of the Institution of Mechanical Engineers, Part H: J Eng Med 235(2):167\u2013184","DOI":"10.1177\/0954411920966937"},{"key":"5669_CR49","doi-asserted-by":"crossref","unstructured":"Aslan Z, Akin M (2020) Automatic detection of schizophrenia by applying deep learning over spectrogram images of eeg signals. Trait Signal 37(2)","DOI":"10.18280\/ts.370209"},{"key":"5669_CR50","doi-asserted-by":"crossref","unstructured":"Aslan Z, Akin M (2022) A deep learning approach in automated detection of schizophrenia using scalogram images of eeg signals. Phys Eng Sci Med 45(1):83\u201396","DOI":"10.1007\/s13246-021-01083-2"},{"key":"5669_CR51","doi-asserted-by":"crossref","unstructured":"Ilakiyaselvan N, Khan AN, Shahina A (2022) Reconstructed phase space portraits for detecting brain diseases using deep learning. Biomed Signal Process Control 71:103278","DOI":"10.1016\/j.bspc.2021.103278"},{"key":"5669_CR52","doi-asserted-by":"crossref","unstructured":"Bagherzadeh S, Shahabi MS, Shalbaf A (2022) Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal. Comput Biol Med 146:105570","DOI":"10.1016\/j.compbiomed.2022.105570"},{"key":"5669_CR53","doi-asserted-by":"crossref","unstructured":"Wu Y, Xia M, Wang X, Zhang Y (2022) Schizophrenia detection based on eeg using recurrent auto-encoder framework. In: Int Conf Neural Inf Process, pp 62\u201373. Springer","DOI":"10.1007\/978-3-031-30108-7_6"},{"key":"5669_CR54","doi-asserted-by":"crossref","unstructured":"Lillo E, Mora M, Lucero B (2022) Automated diagnosis of schizophrenia using eeg microstates and deep convolutional neural network. Expert Syst Appl 209:118236","DOI":"10.1016\/j.eswa.2022.118236"},{"key":"5669_CR55","doi-asserted-by":"crossref","unstructured":"Naira T, Alberto C (2020) Classification of people who suffer schizophrenia and healthy people by eeg signals using deep learning","DOI":"10.14569\/IJACSA.2019.0101067"},{"key":"5669_CR56","doi-asserted-by":"crossref","unstructured":"Phang CR, Ting CM, Samdin SB, Ombao H (2019) Classification of eeg-based effective brain connectivity in schizophrenia using deep neural networks. In: 2019 9th Int IEEE\/EMBS Conf Neural Eng (NER), pp. 401\u2013406. IEEE","DOI":"10.1109\/NER.2019.8717087"},{"key":"5669_CR57","doi-asserted-by":"crossref","unstructured":"Calhas D, Romero E, Henriques R (2020) On the use of pairwise distance learning for brain signal classification with limited observations. Artif intell med 105:101852","DOI":"10.1016\/j.artmed.2020.101852"},{"key":"5669_CR58","doi-asserted-by":"crossref","unstructured":"Supakar R, Satvaya P, Chakrabarti P (2022) A deep learning based model using rnn-lstm for the detection of schizophrenia from eeg data. Comput Biol Med 151:106225","DOI":"10.1016\/j.compbiomed.2022.106225"},{"key":"5669_CR59","doi-asserted-by":"crossref","unstructured":"Phang CR, Noman F, Hussain H, Ting CM, Ombao H (2019) A multi-domain connectome convolutional neural network for identifying schizophrenia from eeg connectivity patterns. IEEE J Biomed Health Inf 24(5):1333\u20131343","DOI":"10.1109\/JBHI.2019.2941222"},{"key":"5669_CR60","doi-asserted-by":"crossref","unstructured":"Shen M, Wen P, Song B, Li Y (2023) Automatic identification of schizophrenia based on eeg signals using dynamic functional connectivity analysis and 3d convolutional neural network. Comput Biol Med 160:107022","DOI":"10.1016\/j.compbiomed.2023.107022"},{"key":"5669_CR61","doi-asserted-by":"crossref","unstructured":"Amer NS, Belhaouari SB (2023) Eeg signal processing for medical diagnosis, healthcare, and monitoring: A comprehensive review. IEEE Access 11:143116\u2013143142. https:\/\/doi.org\/10.1109\/ACCESS.2023.3341419","DOI":"10.1109\/ACCESS.2023.3341419"},{"key":"5669_CR62","doi-asserted-by":"crossref","unstructured":"Akbari H, Sadiq MT, Jafari N, Too J, Mikaeilvand N, Cicone A, Serra Capizzano S (2023) Recognizing seizure using poincar\u00e9 plot of eeg signals and graphical features in dwt domain. Bratisl Med J","DOI":"10.4149\/BLL_2023_002"},{"key":"5669_CR63","unstructured":"Yang J, Choudhary GI, Rahardja S, Franti P (2020) Classification of interbeat interval time-series using attention entropy. IEEE Trans Affect Comput"},{"key":"5669_CR64","doi-asserted-by":"crossref","unstructured":"Henry M, Judge G (2019) Permutation entropy and information recovery in nonlinear dynamic economic time series. Econ 7(1):10","DOI":"10.3390\/econometrics7010010"},{"key":"5669_CR65","unstructured":"Esteller R, Echauz J, Tcheng T, Litt B, Pless B (2001) Line length: an efficient feature for seizure onset detection. In: 2001 Conf Proceed 23rd Ann Int Conf IEEE Eng Med Biol Soc, vol. 2, pp 1707\u20131710. IEEE"},{"key":"5669_CR66","doi-asserted-by":"crossref","unstructured":"Rostaghi M, Azami H (2016) Dispersion entropy: A measure for time-series analysis. IEEE Signal Process Lett 23(5):610\u2013614","DOI":"10.1109\/LSP.2016.2542881"},{"key":"5669_CR67","doi-asserted-by":"crossref","unstructured":"Li P, Liu C, Li K, Zheng D, Liu C, Hou Y (2015) Assessing the complexity of short term heartbeat interval series by distribution entropy. Med Biol Eng Comput 53(1):77\u201387","DOI":"10.1007\/s11517-014-1216-0"},{"key":"5669_CR68","doi-asserted-by":"crossref","unstructured":"Liu X, Jiang A, Xu N, Xue J (2016) Increment entropy as a measure of complexity for time series. Entropy 18(1):22","DOI":"10.3390\/e18010022"},{"key":"5669_CR69","doi-asserted-by":"crossref","unstructured":"Hsu CF, Wei SY, Huang HP, Hsu L, Chi S, Peng CK (2017) Entropy of entropy: Measurement of dynamical complexity for biological systems. Entropy 19(10):550","DOI":"10.3390\/e19100550"},{"key":"5669_CR70","doi-asserted-by":"crossref","unstructured":"Guignard F, Laib M, Amato F, Kanevski M (2020) Advanced analysis of temporal data using fisher-shannon information: theoretical development and application in geosciences. Front Earth Sci 8:255","DOI":"10.3389\/feart.2020.00255"},{"key":"5669_CR71","doi-asserted-by":"crossref","unstructured":"Omidvarnia A, Mesbah M, Pedersen M, Jackson G (2018) Range entropy: A bridge between signal complexity and self-similarity. Entropy 20(12):962","DOI":"10.3390\/e20120962"},{"key":"5669_CR72","doi-asserted-by":"crossref","unstructured":"Cuesta-Frau D (2019) Slope entropy: A new time series complexity estimator based on both symbolic patterns and amplitude information. Entropy 21(12):1167","DOI":"10.3390\/e21121167"},{"key":"5669_CR73","doi-asserted-by":"crossref","unstructured":"Li Y, Yang Y, Li G, Xu M, Huang W (2017) A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mrmr feature selection. Mech Syst Signal Process 91:295\u2013312","DOI":"10.1016\/j.ymssp.2016.12.040"},{"key":"5669_CR74","unstructured":"Tsallis C (2009) Introduction to nonextensive statistical mechanics: approaching a complex world. Springer 1(1):2\u20131"},{"key":"5669_CR75","doi-asserted-by":"crossref","unstructured":"Vignat C, Bercher JF (2003) Analysis of signals in the fisher\u2013shannon information plane. Phys Lett A 312(1-2):27\u201333","DOI":"10.1016\/S0375-9601(03)00570-X"},{"key":"5669_CR76","doi-asserted-by":"crossref","unstructured":"Kantelhardt JW, Zschiegner SA, Koscielny Bunde E, Havlin S, Bunde A, Stanley HE (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Stat Mech Appl 316(1-4):87\u2013114","DOI":"10.1016\/S0378-4371(02)01383-3"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05669-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05669-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05669-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T13:12:45Z","timestamp":1723727565000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05669-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,10]]},"references-count":76,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["5669"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05669-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,10]]},"assertion":[{"value":"30 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors read and approved the final manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}