{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T16:44:46Z","timestamp":1791564286290,"version":"4.3.4"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T00:00:00Z","timestamp":1683244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2021YFE0203400"],"award-info":[{"award-number":["2021YFE0203400"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation and Technology Commission under Mainland-Hong Kong Joint Funding Scheme","award":["MHP\/043\/20"],"award-info":[{"award-number":["MHP\/043\/20"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Neurodyn"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s11571-023-09973-9","type":"journal-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T10:02:16Z","timestamp":1683280936000},"page":"1153-1166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Diagnosis of neurodegenerative diseases with a refined Lempel\u2013Ziv complexity"],"prefix":"10.1007","volume":"18","author":[{"given":"Huan","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junxiao","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangquan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5025-7838","authenticated-orcid":false,"given":"Junyi","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Hsin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongmei","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,5]]},"reference":[{"issue":"1","key":"9973_CR1","first-page":"1391","volume":"16","author":"K Al-Daffaie","year":"2020","unstructured":"Al-Daffaie K, Al-Ghayab HR (2020) Transient artifact reduction and statistical method based classification of neurodegenerative diseases. Int J Agric Stat Sci 16(1):1391\u20131399","journal-title":"Int J Agric Stat Sci"},{"key":"9973_CR2","doi-asserted-by":"publisher","first-page":"107579","DOI":"10.1016\/j.measurement.2020.107579","volume":"156","author":"SMG Beyrami","year":"2020","unstructured":"Beyrami SMG, Ghaderyan P (2020) A robust, cost-effective and non-invasive computer-aided method for diagnosis three types of neurodegenerative diseases with gait signal analysis. Measurement 156:107579. https:\/\/doi.org\/10.1016\/j.measurement.2020.107579","journal-title":"Measurement"},{"key":"9973_CR3","doi-asserted-by":"publisher","first-page":"832","DOI":"10.3390\/e23070832","volume":"23","author":"M Borowska","year":"2021","unstructured":"Borowska M (2021) Multiscale permutation LempeL\u2013Ziv complexity measure for biomedical signal analysis: Interpretation and Application to Focal EEG Signals. Entropy 23:832. https:\/\/doi.org\/10.3390\/e23070832","journal-title":"Entropy"},{"key":"9973_CR4","doi-asserted-by":"publisher","first-page":"102891","DOI":"10.1016\/j.humov.2021.102891","volume":"81","author":"HA Carvajal-Casta\u00f1o","year":"2022","unstructured":"Carvajal-Casta\u00f1o HA, Lemos-Duque JD, Orozco-Arroyave JR (2022) Effective detection of abnormal gait patterns in Parkinson\u2019s disease patients using kinematics, nonlinear, and stability gait features. Hum Mov Sci 81:102891. https:\/\/doi.org\/10.1016\/j.humov.2021.102891","journal-title":"Hum Mov Sci"},{"key":"9973_CR5","doi-asserted-by":"publisher","first-page":"123154","DOI":"10.1016\/j.physa.2019.123154","volume":"540","author":"S Chatterjee","year":"2020","unstructured":"Chatterjee S (2020) Analysis of the human gait rhythm in Neurodegenerative disease: a multifractal approach using Multifractal detrended cross correlation analysis. Phys A Stat Mech Appl 540:123154. https:\/\/doi.org\/10.1016\/j.physa.2019.123154","journal-title":"Phys A Stat Mech Appl"},{"issue":"1","key":"9973_CR6","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1109\/JBHI.2021.3092875","volume":"26","author":"G Cicirelli","year":"2021","unstructured":"Cicirelli G, Impedovo D, Dentamaro V, Marani R, Pirlo G, D\u2019Orazio TR (2021) Human gait analysis in neurodegenerative diseases: a review. IEEE J Biomed Health 26(1):229\u2013242. https:\/\/doi.org\/10.1109\/JBHI.2021.3092875","journal-title":"IEEE J Biomed Health"},{"issue":"7","key":"9973_CR7","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.1016\/j.measurement.2012.04.013","volume":"45","author":"MR Daliri","year":"2012","unstructured":"Daliri MR (2012) Automatic diagnosis of neuro-degenerative diseases using gait dynamics. Measurement 45(7):1729\u20131734. https:\/\/doi.org\/10.1016\/j.measurement.2012.04.013","journal-title":"Measurement"},{"key":"9973_CR8","doi-asserted-by":"publisher","first-page":"85929","DOI":"10.3389\/fnins.2022.859298","volume":"16","author":"R Das","year":"2022","unstructured":"Das R, Paul S, Mourya GK, Kumar N, Hussain M (2022) Recent trends and practices toward assessment and rehabilitation of neurodegenerative disorders: insights from human gait. Front Neurosci 16:85929. https:\/\/doi.org\/10.3389\/fnins.2022.859298","journal-title":"Front Neurosci"},{"key":"9973_CR9","doi-asserted-by":"publisher","first-page":"102741","DOI":"10.1016\/j.humov.2020.102741","volume":"75","author":"F Dierick","year":"2021","unstructured":"Dierick F, Vandevoorde C, Chantraine F, White O, Buisseret F (2021) Benefits of nonlinear analysis indices of walking stride interval in the evaluation of neurodegenerative diseases. Hum Movement Sci 75:102741. https:\/\/doi.org\/10.1016\/j.humov.2020.102741","journal-title":"Hum Movement Sci"},{"key":"9973_CR10","doi-asserted-by":"publisher","first-page":"103069","DOI":"10.1016\/j.bspc.2021.103069","volume":"70","author":"\u00c7B Erda\u015f","year":"2021","unstructured":"Erda\u015f \u00c7B, S\u00fcmer E, Kibaro\u011flu S (2021) Neurodegenerative disease detection and severity prediction using deep learning approaches. Biomed Signal Proces 70:103069. https:\/\/doi.org\/10.1016\/j.bspc.2021.103069","journal-title":"Biomed Signal Proces"},{"key":"9973_CR11","doi-asserted-by":"crossref","unstructured":"Faisal MAA, Chowdhury ME, Mahbub ZB, Pedersen S, Ahmed MU, Khandakar A, AbdulMoniem M (2023) NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern. Appl Intell 1\u201313","DOI":"10.1007\/s10489-023-04557-w"},{"key":"9973_CR12","doi-asserted-by":"publisher","first-page":"103736","DOI":"10.1016\/j.compbiomed.2020.103736","volume":"120","author":"P Ghaderyan","year":"2020","unstructured":"Ghaderyan P, Beyrami SMG (2020) Neurodegenerative diseases detection using distance metrics and sparse coding: a new perspective on gait symmetric features. Comput Biol Med 120:103736. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103736","journal-title":"Comput Biol Med"},{"issue":"23","key":"9973_CR13","doi-asserted-by":"publisher","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, Stanley HE (2000) PhysioBank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215\u2013e220. https:\/\/doi.org\/10.1161\/01.CIR.101.23.e215","journal-title":"Circulation"},{"key":"9973_CR52","doi-asserted-by":"publisher","unstructured":"Grassberger, P. (2007). Grassberger-Procaccia algorithm. Scholarpedia, 2(5), 3043. https:\/\/doi.org\/10.4249\/scholarpedia.3043","DOI":"10.4249\/scholarpedia.3043"},{"key":"9973_CR14","doi-asserted-by":"publisher","first-page":"547","DOI":"10.1007\/s12553-018-0274-y","volume":"9","author":"K Gupta","year":"2019","unstructured":"Gupta K, Khajuria A, Chatterjee N, Joshi P, Joshi D (2019) Rule based classification of neurodegenerative diseases using data driven gait features. Heal Technol 9:547\u2013560. https:\/\/doi.org\/10.1007\/s12553-018-0274-y","journal-title":"Heal Technol"},{"issue":"2","key":"9973_CR15","doi-asserted-by":"publisher","first-page":"026113","DOI":"10.1063\/1.3147408","volume":"19","author":"JM Hausdorff","year":"2009","unstructured":"Hausdorff JM (2009) Gait dynamics in Parkinson\u2019s disease: common and distinct behavior among stride length, gait variability, and fractal-like scaling. Chaos Interdiscip J Nonlinear Sci 19(2):026113. https:\/\/doi.org\/10.1063\/1.3147408","journal-title":"Chaos Interdiscip J Nonlinear Sci"},{"key":"9973_CR16","doi-asserted-by":"publisher","first-page":"2045","DOI":"10.1152\/jappl.2000.88.6.2045","volume":"88","author":"JM Hausdorff","year":"2000","unstructured":"Hausdorff JM, Lertratanakul A, Cudkowicz ME, Peterson AL, Kaliton D, Goldberger AL (2000) Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J Appl Physiol 88:2045\u20132053. https:\/\/doi.org\/10.1152\/jappl.2000.88.6.2045","journal-title":"J Appl Physiol"},{"key":"9973_CR17","doi-asserted-by":"publisher","first-page":"3186","DOI":"10.1109\/EMBC.2017.8037534","volume":"2017","author":"M Heydarzadeh","year":"2017","unstructured":"Heydarzadeh M, Tan CT, Nourani M, Ostadabbas S (2017) Gait variability assessment in neuro-degenerative patients by measuring complexity of independent sources. Annu Int Conf IEEE Eng Med Biol Soc 2017:3186\u20133189. https:\/\/doi.org\/10.1109\/EMBC.2017.8037534","journal-title":"Annu Int Conf IEEE Eng Med Biol Soc"},{"issue":"10","key":"9973_CR18","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1038\/s41582-019-0244-7","volume":"15","author":"Y Hou","year":"2019","unstructured":"Hou Y, Dan X, Babbar M, Wei Y, Hasselbalch SG, Croteau DL, Bohr VA (2019) Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol 15(10):565\u2013581. https:\/\/doi.org\/10.1038\/s41582-019-0244-7","journal-title":"Nat Rev Neurol"},{"issue":"3","key":"9973_CR19","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1016\/j.clinph.2014.07.012","volume":"126","author":"AJ Ib\u00e1\u00f1ez-Molina","year":"2015","unstructured":"Ib\u00e1\u00f1ez-Molina AJ, Iglesias-Parro S, Soriano MF, Aznarte JI (2015) Multiscale LempeL\u2013Ziv complexity for EEG measures. Clin Neurophysiol 126(3):541\u2013548. https:\/\/doi.org\/10.1016\/j.clinph.2014.07.012","journal-title":"Clin Neurophysiol"},{"key":"9973_CR20","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1088\/1674-1056\/17\/3\/021","volume":"17","author":"Z Jian-Jun","year":"2008","unstructured":"Jian-Jun Z, Xin-Bao N, Xiao-Dong Y, Feng-Zhen H, Cheng-Yu H (2008) Decrease in Hurst exponent of human gait with aging and neurodegenerative diseases. Chin Phys B 17:852\u2013856. https:\/\/doi.org\/10.1088\/1674-1056\/17\/3\/021","journal-title":"Chin Phys B"},{"issue":"1","key":"9973_CR21","doi-asserted-by":"publisher","first-page":"1177924","DOI":"10.1080\/23311916.2016.1177924","volume":"3","author":"C Kamath","year":"2016","unstructured":"Kamath C (2016) Analysis of altered complexity of gait dynamics with aging and Parkinson\u2019s disease using ternary LempeL\u2013Ziv complexity. Cogent Eng 3(1):1177924. https:\/\/doi.org\/10.1080\/23311916.2016.1177924","journal-title":"Cogent Eng"},{"key":"9973_CR22","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/s11062-018-9715-5","volume":"50","author":"A Khajuria","year":"2018","unstructured":"Khajuria A, Joshi P, Joshi D (2018) Comprehensive statistical analysis of the gait parameters in neurodegenerative diseases. Neurophysiology 50:38\u201351. https:\/\/doi.org\/10.1007\/s11062-018-9715-5","journal-title":"Neurophysiology"},{"key":"9973_CR23","doi-asserted-by":"publisher","first-page":"111610","DOI":"10.1016\/j.chaos.2021.111610","volume":"154","author":"S Lahmiri","year":"2022","unstructured":"Lahmiri S, Bekiros S (2022) Complexity measures of high oscillations in phonocardiogram as biomarkers to distinguish between normal heart sound and pathological murmur. Chaos Solitons Fract 154:111610. https:\/\/doi.org\/10.1016\/j.chaos.2021.111610","journal-title":"Chaos Solitons Fract"},{"issue":"1","key":"9973_CR24","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1109\/TIT.1976.1055501","volume":"22","author":"A Lempel","year":"1976","unstructured":"Lempel A, Ziv J (1976) On the complexity of finite sequences. IEEE T Inform Theory 22(1):75\u201381. https:\/\/doi.org\/10.1109\/TIT.1976.1055501","journal-title":"IEEE T Inform Theory"},{"key":"9973_CR25","doi-asserted-by":"publisher","first-page":"3857","DOI":"10.3390\/s20143857","volume":"20","author":"CW Lin","year":"2020","unstructured":"Lin CW, Wen TC, Setiawan F (2020) Evaluation of vertical ground reaction forces pattern visualization in neurodegenerative diseases identification using deep learning and recurrence plot image feature extraction. Sensors-Basel 20:3857. https:\/\/doi.org\/10.3390\/s20143857","journal-title":"Sensors-Basel"},{"key":"9973_CR26","doi-asserted-by":"publisher","first-page":"934","DOI":"10.3390\/e21100934","volume":"21","author":"AB Liu","year":"2019","unstructured":"Liu AB, Lin CW (2019) Multiscale approximate entropy for gait analysis in patients with neurodegenerative diseases. Entropy 21:934. https:\/\/doi.org\/10.3390\/e21100934","journal-title":"Entropy"},{"key":"9973_CR27","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1109\/TNSRE.2016.2584923","volume":"25","author":"H Ma","year":"2016","unstructured":"Ma H, Liao WH (2016) Human gait modeling and analysis using a semi-Markov process with ground reaction forces. Ieee T Neur Sys Reh 25:597\u2013607. https:\/\/doi.org\/10.1109\/TNSRE.2016.2584923","journal-title":"Ieee T Neur Sys Reh"},{"issue":"5","key":"9973_CR28","doi-asserted-by":"publisher","first-page":"967","DOI":"10.1029\/WR005i005p00967","volume":"5","author":"BB Mandelbrot","year":"1969","unstructured":"Mandelbrot BB, Wallis JR (1969) Robustness of the rescaled range R\/S in the measurement of noncyclic long run statistical dependence. Water Resour Res 5(5):967\u2013988. https:\/\/doi.org\/10.1029\/WR005i005p00967","journal-title":"Water Resour Res"},{"issue":"12","key":"9973_CR29","doi-asserted-by":"publisher","first-page":"5974","DOI":"10.1109\/JBHI.2022.3205058","volume":"26","author":"A Mengarelli","year":"2022","unstructured":"Mengarelli A, Tigrini A, Fioretti S, Verdini F (2022) Identification of neurodegenerative diseases from gait rhythm through time domain and time-dependent spectral descriptors. IEEE J Biomed Health Inform 26(12):5974\u20135982. https:\/\/doi.org\/10.1109\/JBHI.2022.3205058","journal-title":"IEEE J Biomed Health Inform"},{"key":"9973_CR30","doi-asserted-by":"crossref","unstructured":"Mengarelli A, Tigrini A, Fioretti S, et al. (2021) Recurrence quantification analysis of gait rhythm in patients affected by Parkinson\u2019s Disease. In: 2021 IEEE EMBS international conference on biomedical and health informatics (BHI). IEEE, pp 1\u20134","DOI":"10.1109\/BHI50953.2021.9508550"},{"issue":"1","key":"9973_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12984-019-0560-6","volume":"16","author":"C O\u2019Keeffe","year":"2019","unstructured":"O\u2019Keeffe C, Taboada LP, Feerick N, Gallagher L, Lynch T, Reilly RB (2019) Complexity based measures of postural stability provide novel evidence of functional decline in fragile X premutation carriers. J Neuroeng Rehabil 16(1):1\u20138. https:\/\/doi.org\/10.1186\/s12984-019-0560-6","journal-title":"J Neuroeng Rehabil"},{"key":"9973_CR32","doi-asserted-by":"publisher","first-page":"3253","DOI":"10.1007\/s11071-020-05691-7","volume":"100","author":"PA P\u00e9rez-Toro","year":"2020","unstructured":"P\u00e9rez-Toro PA, V\u00e1squez-Correa JC, Arias-Vergara T, N\u00f6th E, Orozco-Arroyave JR (2020) Nonlinear dynamics and Poincar\u00e9 sections to model gait impairments in different stages of Parkinson\u2019s disease. Nonlinear Dyn 100:3253\u20133276. https:\/\/doi.org\/10.1007\/s11071-020-05691-7","journal-title":"Nonlinear Dyn"},{"issue":"1","key":"9973_CR33","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1109\/TNSRE.2017.2732448","volume":"26","author":"TD Pham","year":"2017","unstructured":"Pham TD (2017) Texture classification and visualization of time series of gait dynamics in patients with neuro-degenerative diseases. IEEE Trans Neural Syst Rehabil Eng 26(1):188\u2013196. https:\/\/doi.org\/10.1109\/TNSRE.2017.2732448","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"9973_CR34","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.patrec.2018.05.006","volume":"139","author":"P Prabhu","year":"2020","unstructured":"Prabhu P, Karunakar AK, Anitha H, Pradhan N (2020) Classification of gait signals into different neurodegenerative diseases using statistical analysis and recurrence quantification analysis. Pattern Recogn Lett 139:10\u201316. https:\/\/doi.org\/10.1016\/j.patrec.2018.05.006","journal-title":"Pattern Recogn Lett"},{"key":"9973_CR35","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TNSRE.2015.2477325","volume":"24","author":"P Ren","year":"2015","unstructured":"Ren P, Zhao W, Zhao Z, Bringas-Vega ML, Valdes-Sosa PA, Kendrick KM (2015) Analysis of gait Rhythm Fluctuations for neurodegenerative diseases by phase synchronization and conditional entropy. IEEE T Neur Sys Reh 24:291\u2013299. https:\/\/doi.org\/10.1109\/TNSRE.2015.2477325","journal-title":"IEEE T Neur Sys Reh"},{"key":"9973_CR36","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1109\/TBME.2016.2536438","volume":"64","author":"P Ren","year":"2016","unstructured":"Ren P, Tang S, Fang F, Luo L, Xu L, Bringas-Vega ML, Valdes-Sosa PA (2016) Gait rhythm fluctuation analysis for neurodegenerative diseases by empirical mode decomposition. IEEE T Bio-Med Eng 64:52\u201360. https:\/\/doi.org\/10.1109\/TBME.2016.2536438","journal-title":"IEEE T Bio-Med Eng"},{"key":"9973_CR37","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1109\/TNSRE.2018.2860971","volume":"26","author":"H Ren","year":"2018","unstructured":"Ren H, Yang Y, Gu C, Weng T, Yang H (2018a) A patient suffering from neurodegenerative disease may have a strengthened fractal gait rhythm. IEEE T Neur Sys Reh 26:1765\u20131772. https:\/\/doi.org\/10.1109\/TNSRE.2018.2860971","journal-title":"IEEE T Neur Sys Reh"},{"issue":"1","key":"9973_CR38","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1109\/TBME.2018.2829749","volume":"66","author":"P Ren","year":"2018","unstructured":"Ren P, Hu S, Han Z, Wang Q, Yao S, Gao Z, Valdes-Sosa PA (2018b) Movement symmetry assessment by bilateral motion data fusion. IEEE T Bio-Med Eng 66(1):225\u2013236. https:\/\/doi.org\/10.1109\/TBME.2018.2829749","journal-title":"IEEE T Bio-Med Eng"},{"key":"9973_CR53","doi-asserted-by":"publisher","unstructured":"Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65(1\u20132), 117\u2013134. https:\/\/doi.org\/10.1016\/0167-2789(93)90009-P","DOI":"10.1016\/0167-2789(93)90009-P"},{"key":"9973_CR39","doi-asserted-by":"publisher","first-page":"136107","DOI":"10.1016\/j.neulet.2021.136107","volume":"761","author":"M Saljuqi","year":"2021","unstructured":"Saljuqi M, Ghaderyan P (2021) A novel method based on matching pursuit decomposition of gait signals for Parkinson\u2019s disease, Amyotrophic lateral sclerosis and Huntington\u2019s disease detection. Neurosci Lett 761:136107. https:\/\/doi.org\/10.1016\/j.neulet.2021.136107","journal-title":"Neurosci Lett"},{"key":"9973_CR40","doi-asserted-by":"publisher","first-page":"110703","DOI":"10.1016\/j.chaos.2021.110703","volume":"146","author":"M \u0160apina","year":"2021","unstructured":"\u0160apina M, Karmakar CK, Kramari\u0107 K, Ko\u015bmider M, Garcin M, Brdari\u0107 D, Yearwood J (2021) (2021), LempeL\u2013Ziv complexity of the pNNx statistics\u2013an application to neonatal stress. Chaos Solitons Fract 146:110703. https:\/\/doi.org\/10.1016\/j.chaos.2021.110703","journal-title":"Chaos Solitons Fract"},{"issue":"2","key":"9973_CR41","doi-asserted-by":"publisher","first-page":"026108","DOI":"10.1063\/1.3143035","volume":"19","author":"N Scafetta","year":"2009","unstructured":"Scafetta N, Marchi D, West BJ (2009) Understanding the complexity of human gait dynamics. Chaos Interdiscip J Nonlinear Sci 19(2):026108. https:\/\/doi.org\/10.1063\/1.3143035","journal-title":"Chaos Interdiscip J Nonlinear Sci"},{"issue":"7","key":"9973_CR42","doi-asserted-by":"publisher","first-page":"902","DOI":"10.3390\/brainsci11070902","volume":"11","author":"F Setiawan","year":"2021","unstructured":"Setiawan F, Lin CW (2021) Identification of neurodegenerative diseases based on vertical ground reaction force classification using time-frequency spectrogram and deep learning neural network features. Brain Sci 11(7):902. https:\/\/doi.org\/10.3390\/brainsci11070902","journal-title":"Brain Sci"},{"issue":"2","key":"9973_CR43","doi-asserted-by":"publisher","first-page":"239","DOI":"10.3390\/e22020239","volume":"22","author":"J Sun","year":"2020","unstructured":"Sun J, Wang B, Niu Y, Tan Y, Fan C, Zhang N, Xiang J (2020) Complexity analysis of EEG, MEG, and fMRI in mild cognitive impairment and Alzheimer\u2019s disease: a review. Entropy 22(2):239. https:\/\/doi.org\/10.3390\/e22020239","journal-title":"Entropy"},{"issue":"3","key":"9973_CR44","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1093\/gerona\/glab352","volume":"77","author":"S Tanabe","year":"2022","unstructured":"Tanabe S, Parker M, Lennertz R et al (2022) Reduced electroencephalogram complexity in postoperative delirium. J Gerontol Ser A 77(3):502\u2013506. https:\/\/doi.org\/10.1093\/gerona\/glab352","journal-title":"J Gerontol Ser A"},{"issue":"6","key":"9973_CR45","doi-asserted-by":"publisher","first-page":"065001","DOI":"10.1088\/2057-1976\/ac8c9a","volume":"8","author":"C Tobar","year":"2022","unstructured":"Tobar C, Rengifo C, Mu\u00f1oz M (2022) Petri net transition times as training features for multiclass models to support the detection of neurodegenerative diseases. Biomed Phys Eng Express 8(6):065001. https:\/\/doi.org\/10.1088\/2057-1976\/ac8c9a","journal-title":"Biomed Phys Eng Express"},{"key":"9973_CR46","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/j.bspc.2015.02.002","volume":"18","author":"Y Xia","year":"2015","unstructured":"Xia Y, Gao Q, Ye Q (2015) Classification of gait rhythm signals between patients with neuro-degenerative diseases and normal subjects: Experiments with statistical features and different classification models. Biomed Signal Process Control 18:254\u2013262. https:\/\/doi.org\/10.1016\/j.bspc.2015.02.002","journal-title":"Biomed Signal Process Control"},{"key":"9973_CR47","doi-asserted-by":"publisher","first-page":"96363","DOI":"10.1109\/ACCESS.2020.2996667","volume":"8","author":"Y Yan","year":"2020","unstructured":"Yan Y, Omisore OM, Xue YC, Li HH, Liu QH, Nie ZD, Wang L (2020) Classification of neurodegenerative diseases via topological motion analysis\u2014a comparison study for multiple gait fluctuations. Ieee Access 8:96363\u201396377. https:\/\/doi.org\/10.1109\/ACCESS.2020.2996667","journal-title":"Ieee Access"},{"key":"9973_CR48","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.ins.2015.04.047","volume":"317","author":"W Zeng","year":"2015","unstructured":"Zeng W, Wang C (2015) Classification of neurodegenerative diseases using gait dynamics via deterministic learning. Inf Sci 317:246\u2013258. https:\/\/doi.org\/10.1016\/j.ins.2015.04.047","journal-title":"Inf Sci"},{"key":"9973_CR49","doi-asserted-by":"publisher","first-page":"102860","DOI":"10.1016\/j.bspc.2021.102860","volume":"69","author":"H Zhao","year":"2021","unstructured":"Zhao H, Cao J, Wang R, Lei Y, Liao WH, Cao H (2021) Accurate identification of Parkinson\u2019s disease by distinctive features and ensemble decision trees. Biomed Signal Proces 69:102860. https:\/\/doi.org\/10.1016\/j.bspc.2021.102860","journal-title":"Biomed Signal Proces"},{"key":"9973_CR50","doi-asserted-by":"publisher","first-page":"116113","DOI":"10.1016\/j.eswa.2021.116113","volume":"189","author":"H Zhao","year":"2022","unstructured":"Zhao H, Wang R, Lei Y, Liao WH, Cao H, Cao J (2022) Severity level diagnosis of Parkinson\u2019s disease by ensemble K-nearest neighbor under imbalanced data. Expert Syst Appl 189:116113. https:\/\/doi.org\/10.1016\/j.eswa.2021.116113","journal-title":"Expert Syst Appl"},{"key":"9973_CR51","doi-asserted-by":"crossref","unstructured":"Zhao H, Yu J, Cao J, Liao WH (2020) Refined weighted-permutation entropy: a complexity measure for human gait and physiologic signals with outliers and noise, Springer International Publishing, Cham, pp 223\u2013231","DOI":"10.1007\/978-3-030-34724-6_23"}],"container-title":["Cognitive Neurodynamics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-023-09973-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11571-023-09973-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11571-023-09973-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T10:42:48Z","timestamp":1717152168000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11571-023-09973-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,5]]},"references-count":53,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["9973"],"URL":"https:\/\/doi.org\/10.1007\/s11571-023-09973-9","relation":{},"ISSN":["1871-4080","1871-4099"],"issn-type":[{"value":"1871-4080","type":"print"},{"value":"1871-4099","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,5]]},"assertion":[{"value":"6 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}