{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T09:04:49Z","timestamp":1778144689427,"version":"3.51.4"},"reference-count":88,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Parkinson\u2019s disease (PD) is a progressive neurodegenerative disorder that causes abnormal movements and an array of other symptoms. An accurate PD diagnosis can be a challenging task as the signs and symptoms, particularly at an early stage, can be similar to other medical conditions or the physiological changes of normal ageing. This work aims to contribute to the PD diagnosis process by using a convolutional neural network, a type of deep neural network architecture, to differentiate between healthy controls and PD patients. Our approach focuses on discovering deviations in patient\u2019s movements with the use of drawing tasks. In addition, this work explores which of two drawing tasks, wire cube or spiral pentagon, are more effective in the discrimination process. With <jats:inline-formula><jats:alternatives><jats:tex-math>$$93.5\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>93.5<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> accuracy, our convolutional classifier, trained with images of the pentagon drawing task and augmentation techniques, can be used as an objective method to discriminate PD from healthy controls. Our compact model has the potential to be developed into an offline real-time automated single-task diagnostic tool, which can be easily deployed within a clinical setting.<\/jats:p>","DOI":"10.1007\/s00521-021-06469-7","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T18:02:36Z","timestamp":1631124156000},"page":"1433-1453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["Parkinson\u2019s disease diagnosis using convolutional neural networks and figure-copying tasks"],"prefix":"10.1007","volume":"34","author":[{"given":"Mohamad","family":"Alissa","sequence":"first","affiliation":[]},{"given":"Michael A.","family":"Lones","sequence":"additional","affiliation":[]},{"given":"Jeremy","family":"Cosgrove","sequence":"additional","affiliation":[]},{"given":"Jane E.","family":"Alty","sequence":"additional","affiliation":[]},{"given":"Stuart","family":"Jamieson","sequence":"additional","affiliation":[]},{"given":"Stephen L.","family":"Smith","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9957-954X","authenticated-orcid":false,"given":"Marta","family":"Vallejo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"key":"6469_CR1","doi-asserted-by":"crossref","unstructured":"Afonso LC, Pereira CR, Weber SA, Hook C, Falc\u00e3o AX, Papa JP (2020) Hierarchical learning using deep optimum-path forest. J Vis Commun Image Represent 71:102823","DOI":"10.1016\/j.jvcir.2020.102823"},{"key":"6469_CR2","doi-asserted-by":"crossref","unstructured":"Afonso LC, Rosa GH, Pereira CR, Weber SA, Hook C, Albuquerque VHC, Papa JP (2019) A recurrence plot-based approach for Parkinson\u2019s disease identification. Future Gener Comput Syst 94:282\u2013292","DOI":"10.1016\/j.future.2018.11.054"},{"key":"6469_CR3","doi-asserted-by":"crossref","unstructured":"Afonso LCS, Pereira CR, Weber SAT, Hook C, Papa JP (2017) Parkinson\u2019s disease identification through deep optimum-path forest clustering. In: 30th conference on graphics, patterns and images (SIBGRAPI). IEEE, pp 163\u2013169","DOI":"10.1109\/SIBGRAPI.2017.28"},{"key":"6469_CR4","doi-asserted-by":"crossref","unstructured":"Alty JE, Cosgrove J, Jamieson S, Smith SL, Possin KL (2015) Which figure copy test is more sensitive for cognitive impairment in Parkinson\u2019s disease: Wire cube or interlocking pentagons? Clin Neurol Neurosurg 139:244\u2013246","DOI":"10.1016\/j.clineuro.2015.10.019"},{"key":"6469_CR5","unstructured":"Alty JE, Cosgrove J, Lones MA, Smith SL, Possin K, Schuff N, Jamieson S (2016) Clinically \u2018slight\u2019 bradykinesia in Parkinson\u2019s disease is accurately detected using evolutionary computation analysis of finger tapping. Mov Disord 31:S184\u2013S184"},{"key":"6469_CR6","doi-asserted-by":"crossref","unstructured":"Aly N, Playfer J, Smith S, Halliday D (2007) A novel computer-based technique for the assessment of tremor in Parkinson\u2019s disease. Age Ageing 36(4):395\u2013399","DOI":"10.1093\/ageing\/afm061"},{"key":"6469_CR7","unstructured":"Bousquet O, Elisseeff A (2002) Stability and generalization. J Mach Learn Res 2:499\u2013526"},{"issue":"3","key":"6469_CR8","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1097\/WNN.0000000000000006","volume":"26","author":"XY Bu","year":"2013","unstructured":"Bu XY, Luo XG, Gao C, Feng Y, Yu HM, Ren Y, Shang H, He ZY (2013) Usefulness of cube copying in evaluating clinical profiles of patients with Parkinson disease. Cogn Behav Neurol 26(3):140\u2013145","journal-title":"Cogn Behav Neurol"},{"issue":"2","key":"6469_CR9","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1097\/00146965-200306000-00001","volume":"16","author":"DA Cahn-Weiner","year":"2003","unstructured":"Cahn-Weiner DA, Williams K, Grace J, Tremont G, Westervelt H, Stern RA (2003) Discrimination of dementia with Lewy bodies from Alzheimer disease and Parkinson disease using the clock drawing test. Cogn Behav Neurol 16(2):85\u201392","journal-title":"Cogn Behav Neurol"},{"key":"6469_CR10","doi-asserted-by":"crossref","unstructured":"Camps J, Sama A, Martin M, Rodriguez-Martin D, Perez-Lopez C, Arostegui JMM, Cabestany J, Catala A, Alcaine S, Mestre B et al (2018) Deep learning for freezing of gait detection in Parkinson\u2019s disease patients in their homes using a waist-worn inertial measurement unit. Knowl-Based Syst 139:119\u2013131","DOI":"10.1016\/j.knosys.2017.10.017"},{"key":"6469_CR11","doi-asserted-by":"crossref","unstructured":"Canturk I (2020) Fuzzy recurrence plot-based analysis of dynamic and static spiral tests of Parkinson\u2019s disease patients. Neural Comput Appl 33: 349-360","DOI":"10.1007\/s00521-020-05014-2"},{"key":"6469_CR12","doi-asserted-by":"crossref","unstructured":"Chatfield K, Simonyan K, Vedaldi A, Zisserman A (2014) Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:1405.3531","DOI":"10.5244\/C.28.6"},{"key":"6469_CR13","unstructured":"Chollet F et\u00a0al (2015) Keras: Deep learning library for Theano and Tensorflow. 7(8) https:\/\/keras.io\/k"},{"key":"6469_CR14","doi-asserted-by":"crossref","unstructured":"Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37\u201346","DOI":"10.1177\/001316446002000104"},{"key":"6469_CR15","doi-asserted-by":"crossref","unstructured":"Cormack F, Aarsland D, Ballard C, Tov\u00e9e M (2004) Pentagon drawing and neuropsychological performance in dementia with Lewy bodies, Alzheimer\u2019s disease, Parkinson\u2019s disease and Parkinson\u2019s disease with dementia. J Geriatr Psychiatry 19(4):371\u2013377","DOI":"10.1002\/gps.1094"},{"issue":"4","key":"6469_CR16","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1002\/mds.10189","volume":"17","author":"P Derkinderen","year":"2002","unstructured":"Derkinderen P, Dupont S, Vidal JS, Chedru F, Vidailhet M (2002) Micrographia secondary to lenticular lesions. Mov Disord 17(4):835\u2013837","journal-title":"Mov Disord"},{"key":"6469_CR17","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.freeradbiomed.2013.01.018","volume":"62","author":"DT Dexter","year":"2013","unstructured":"Dexter DT, Jenner P (2013) Parkinson disease: from pathology to molecular disease mechanisms. Free Radical Biol Med 62:132\u2013144","journal-title":"Free Radical Biol Med"},{"key":"6469_CR18","doi-asserted-by":"crossref","unstructured":"Diaz M, Ferrer MA, Impedovo D, Pirlo G, Vessio G (2019) Dynamically enhanced static handwriting representation for Parkinson\u2019s disease detection. Pattern Recogn Lett 128:204\u2013210","DOI":"10.1016\/j.patrec.2019.08.018"},{"issue":"5","key":"6469_CR19","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1212\/01.wnl.0000247740.47667.03","volume":"68","author":"E Dorsey","year":"2007","unstructured":"Dorsey E, Constantinescu R, Thompson J, Biglan K, Holloway R, Kieburtz K, Marshall F, Ravina B, Schifitto G et al (2007) Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030. Neurology 68(5):384\u2013386","journal-title":"Neurology"},{"key":"6469_CR20","doi-asserted-by":"crossref","unstructured":"Drot\u00e1r P, Mekyska J, Rektorov\u00e1 I, Masarov\u00e1 L, Sm\u00e9kal Z, Faundez-Zanuy M (2014) Analysis of in-air movement in handwriting: a novel marker for Parkinson\u2019s disease. Comput Methods Programs Biomed 117(3):405\u2013411","DOI":"10.1016\/j.cmpb.2014.08.007"},{"key":"6469_CR21","doi-asserted-by":"crossref","unstructured":"Drot\u00e1r P, Mekyska J, Rektorov\u00e1 I, Masarov\u00e1 L, Sm\u00e9kal Z, Faundez-Zanuy M (2016) Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson\u2019s disease. Artif Intell Med 67:39\u201346","DOI":"10.1016\/j.artmed.2016.01.004"},{"key":"6469_CR22","unstructured":"Duffy J, Keith R, Shane H, Podraza B (1976) Performance of normal (non-brain injured) adults on the porch index of communicative ability. In: Conference in clinical aphasiology, pp 32\u201342. BRK Publishers"},{"key":"6469_CR23","doi-asserted-by":"crossref","unstructured":"Efron B (1983) Estimating the error rate of a prediction rule: improvement on cross-validation. J Am Stat Assoc 78(382):316\u2013331","DOI":"10.1080\/01621459.1983.10477973"},{"issue":"1","key":"6469_CR24","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1602\/neurorx.1.1.139","volume":"1","author":"S Fahn","year":"2004","unstructured":"Fahn S, Sulzer D (2004) Neurodegeneration and neuroprotection in Parkinson disease. NeuroRX 1(1):139\u2013154","journal-title":"NeuroRX"},{"key":"6469_CR25","volume-title":"Statistical methods for rates and proportions","author":"JL Fleiss","year":"2013","unstructured":"Fleiss JL, Levin B, Paik MC (2013) Statistical methods for rates and proportions. Wiley, London"},{"key":"6469_CR26","doi-asserted-by":"crossref","unstructured":"Frid A, Manevitz LM, Mosafi O (2018) Kohonen-based topological clustering as an amplifier for multi-class classification for Parkinson\u2019s disease. In: International conference on the science of electrical engineering in Israel (ICSEE), pp 1\u20135. IEEE","DOI":"10.1109\/ICSEE.2018.8646026"},{"key":"6469_CR27","doi-asserted-by":"crossref","unstructured":"Gallicchio C, Micheli A, Pedrelli L (2018) Deep echo state networks for diagnosis of Parkinson\u2019s disease. In: 26th European symposium on artificial neural networks, pp 397\u2013402","DOI":"10.1109\/IJCNN.2018.8489464"},{"key":"6469_CR28","doi-asserted-by":"crossref","unstructured":"Gibb W, Lees A (1988) The relevance of the Lewy body to the pathogenesis of idiopathic Parkinsons disease. J Neurol Neurosurg Psychiatry 51(6):745\u2013752","DOI":"10.1136\/jnnp.51.6.745"},{"key":"6469_CR29","doi-asserted-by":"crossref","unstructured":"Gil-Mart\u00edn M, Montero JM, San-Segundo R (2019) Parkinson\u2019s disease detection from drawing movements using convolutional neural networks. Electronics 8(8):907","DOI":"10.3390\/electronics8080907"},{"key":"6469_CR30","unstructured":"Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th international conference on artificial intelligence and statistics, pp 249\u2013256"},{"key":"6469_CR31","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, vol 1. MIT Press, Cambridge"},{"key":"6469_CR32","doi-asserted-by":"crossref","unstructured":"Greenspan H, Van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. Trans Med Imaging 35(5):1153\u20131159","DOI":"10.1109\/TMI.2016.2553401"},{"key":"6469_CR33","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"6","key":"6469_CR34","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton G, Deng L, Yu D, Dahl G, Mohamed A, Jaitly N, Senior A, Vanhoucke V, Nguyen P et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82\u201397","journal-title":"IEEE Signal Process Mag"},{"key":"6469_CR35","doi-asserted-by":"publisher","unstructured":"Hollm\u00e9n J, Skubacz M, Taniguchi M (2000) Input dependent misclassification costs for cost-sensitive classifiers. WIT Trans Inform Commun Technol https:\/\/doi.org\/10.2495\/DATA000481","DOI":"10.2495\/DATA000481"},{"issue":"4","key":"6469_CR36","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1093\/brain\/awf080","volume":"125","author":"AJ Hughes","year":"2002","unstructured":"Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ (2002) The accuracy of diagnosis of Parkinsonian syndromes in a specialist movement disorder service. Brain 125(4):861\u2013870","journal-title":"Brain"},{"key":"6469_CR37","doi-asserted-by":"crossref","unstructured":"Hughes AJ, Daniel SE, Blankson S, Lees AJ (1993) A clinicopathologic study of 100 cases of Parkinson\u2019s disease. Arch Neurol 50(2):140\u2013148","DOI":"10.1001\/archneur.1993.00540020018011"},{"key":"6469_CR38","doi-asserted-by":"crossref","unstructured":"Hughes AJ, Daniel SE, Kilford L, Lees AJ (1992) Accuracy of clinical diagnosis of idiopathic Parkinson\u2019s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 55(3):181\u2013184","DOI":"10.1136\/jnnp.55.3.181"},{"key":"6469_CR39","unstructured":"Isenkul M, Sakar B, Kursun O (2014) Improved spiral test using digitized graphics tablet for monitoring Parkinson\u2019s disease. In: International conference on e-health and telemedicine, pp 171\u20135"},{"key":"6469_CR40","doi-asserted-by":"crossref","unstructured":"Kaul S, Elble R (2014) Impaired pentagon drawing is an early predictor of cognitive decline in Parkinson disease. Movem Disord 29(3):427","DOI":"10.1002\/mds.25807"},{"key":"6469_CR41","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.compbiomed.2017.01.004","volume":"82","author":"J Kawa","year":"2017","unstructured":"Kawa J, Bednorz A, Stpie P, Derejczyk J, Bugdol M (2017) Spatial and dynamical handwriting analysis in mild cognitive impairment. Comput Biol Med 82:21\u201328","journal-title":"Comput Biol Med"},{"key":"6469_CR42","doi-asserted-by":"crossref","unstructured":"Khatamino P, Cant\u00fcrk \u0130, \u00d6zy\u0131lmaz L (2018) A deep learning-CNN based system for medical diagnosis: an application on Parkinson\u2019s disease handwriting drawings. In: 6th international conference on control engineering and information technology (CEIT). IEEE, pp 1\u20136","DOI":"10.1109\/CEIT.2018.8751879"},{"key":"6469_CR43","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097\u20131105"},{"issue":"7553","key":"6469_CR44","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436","journal-title":"Nature"},{"key":"6469_CR45","doi-asserted-by":"publisher","first-page":"5858","DOI":"10.1109\/ACCESS.2017.2696121","volume":"5","author":"J Lemley","year":"2017","unstructured":"Lemley J, Bazrafkan S, Corcoran P (2017) Smart augmentation learning an optimal data augmentation strategy. IEEE Access 5:5858\u20135869","journal-title":"IEEE Access"},{"key":"6469_CR46","doi-asserted-by":"crossref","unstructured":"Lesage S, Brice A (2009) Parkinson\u2019s disease: from monogenic forms to genetic susceptibility factors. Hum Mol Genet 18(R1):R48\u2013R59","DOI":"10.1093\/hmg\/ddp012"},{"key":"6469_CR47","doi-asserted-by":"crossref","unstructured":"Letanneux A, Danna J, Velay JL, Viallet F, Pinto S (2014) From micrographia to Parkinson\u2019s disease dysgraphia. Mov Disord 29(12):1467\u20131475","DOI":"10.1002\/mds.25990"},{"issue":"1","key":"6469_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-020-09773-x","volume":"13","author":"M Mahmud","year":"2021","unstructured":"Mahmud M, Kaiser MS, McGinnity TM, Hussain A (2021) Deep learning in mining biological data. Cogn Comput 13(1):1\u201333","journal-title":"Cogn Comput"},{"issue":"12","key":"6469_CR49","doi-asserted-by":"publisher","first-page":"2947","DOI":"10.1109\/TPAMI.2018.2872043","volume":"41","author":"X Mao","year":"2018","unstructured":"Mao X, Li Q, Xie H, Lau RY, Wang Z, Smolley SP (2018) On the effectiveness of least squares generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 41(12):2947\u20132960","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2\u20133","key":"6469_CR50","doi-asserted-by":"publisher","first-page":"427","DOI":"10.1016\/j.neunet.2007.12.031","volume":"21","author":"MA Mazurowski","year":"2008","unstructured":"Mazurowski MA, Habas PA, Zurada JM, Lo JY, Baker JA, Tourassi GD (2008) Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Netw 21(2\u20133):427\u2013436","journal-title":"Neural Netw"},{"key":"6469_CR51","doi-asserted-by":"crossref","unstructured":"Michaeli S, \u00d6z G, Sorce DJ, Garwood M, Ugurbil K, Majestic S, Tuite P (2007) Assessment of brain iron and neuronal integrity in patients with Parkinson\u2019s disease using novel MRI contrasts. Move Disord 22(3):334\u2013340","DOI":"10.1002\/mds.21227"},{"key":"6469_CR52","doi-asserted-by":"crossref","unstructured":"Miko\u0142ajczyk A, Grochowski M (2018) Data augmentation for improving deep learning in image classification problem. In: International interdisciplinary PhD workshop (IIPhDW), pp 117\u2013122. IEEE","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"6469_CR53","doi-asserted-by":"crossref","unstructured":"Miller DB, O\u2019Callaghan JP (2015) Biomarkers of Parkinson\u2019s disease: present and future. Metabolism 64(3):S40\u2013S46","DOI":"10.1016\/j.metabol.2014.10.030"},{"key":"6469_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-020-04735-8","volume":"32","author":"M Moetesum","year":"2020","unstructured":"Moetesum M, Siddiqi I, Ehsan S, Vincent N (2020) Deformation modeling and classification using deep convolutional neural networks for computerized analysis of neuropsychological drawings. Neural Comput Appl 32:1\u201325","journal-title":"Neural Comput Appl"},{"key":"6469_CR55","doi-asserted-by":"crossref","unstructured":"Moetesum M, Siddiqi I, Vincent N, Cloppet F (2018) Assessing visual attributes of handwriting for prediction of neurological disorders\u2014a case study on Parkinson\u2019s disease. Pattern Recogn Lett 121:19\u201327","DOI":"10.1016\/j.patrec.2018.04.008"},{"key":"6469_CR56","doi-asserted-by":"crossref","unstructured":"Movement Disorder Society Task Force on Rating Scales for Parkinson\u2019s disease: the unified Parkinson\u2019s disease rating scale (UPDRS): status and recommendations. Move Disord 18(7):738\u2013750 (2003)","DOI":"10.1002\/mds.10473"},{"key":"6469_CR57","doi-asserted-by":"crossref","unstructured":"Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2020) Refining Parkinson\u2019s neurological disorder identification through deep transfer learning. Neural Comput Appl 32(3):839\u2013854","DOI":"10.1007\/s00521-019-04069-0"},{"issue":"4","key":"6469_CR58","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1111\/j.1532-5415.2005.53221.x","volume":"53","author":"Z Nasreddine","year":"2005","unstructured":"Nasreddine Z, Phillips N, B\u00e9dirian V, Charbonneau S, Whitehead V, Collin I, Cummings J, Chertkow H (2005) Montreal cognitive assessment MoCA brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53(4):695\u2013699","journal-title":"J Am Geriatr Soc"},{"issue":"1","key":"6469_CR59","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1111\/j.1468-1331.2011.03590.x","volume":"19","author":"Olesen J, Gustavsson A, Svensson M, Wittchen H, J\u00f6nsson B, Group CS, Council EB","year":"2012","unstructured":"Olesen J, Gustavsson A, Svensson M, Wittchen H, J\u00f6nsson B, Group CS, Council EB (2012) Economic cost of brain disorders in Europe. Eur J Neurol 19(1):155\u2013162","journal-title":"Eur J Neurol"},{"issue":"2","key":"6469_CR60","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1002\/ima.20188","volume":"19","author":"JP Papa","year":"2009","unstructured":"Papa JP, Falcao AX, Suzuki CT (2009) Supervised pattern classification based on optimum-path forest. Int J Imaging Syst Technol 19(2):120\u2013131","journal-title":"Int J Imaging Syst Technol"},{"key":"6469_CR61","unstructured":"Parkinson Society: Website of the Parkinson\u2019s disease society. http:\/\/www.parkinsons.org.uk (2018). Accessed on 23-07-2021"},{"key":"6469_CR62","doi-asserted-by":"crossref","unstructured":"Parkinson Study Group (2004) Levodopa and the progression of Parkinson\u2019s disease. N Engl J Med 351(24):2498\u20132508","DOI":"10.1056\/NEJMoa033447"},{"key":"6469_CR63","unstructured":"Parkinson\u2019s Foundation: Statistics on Parkinson\u2019s: who has Parkinson\u2019s? https:\/\/www.parkinson.org\/Understanding-Parkinsons\/Statistics (2015). Accessed on 23-07-2021"},{"key":"6469_CR64","doi-asserted-by":"crossref","unstructured":"Pereira C, Pereira D, Papa J, Rosa G, Yang X (2016) Convolutional neural networks applied for Parkinson\u2019s disease identification. In: Machine learning for health informatics. Springer, pp 377\u2013390","DOI":"10.1007\/978-3-319-50478-0_19"},{"key":"6469_CR65","doi-asserted-by":"crossref","unstructured":"Pereira C, Pereira D, Rosa G, Albuquerque V, Weber S, Hook C, Papa J (2018) Handwritten dynamics assessment through convolutional neural networks: an application to Parkinson\u2019s disease identification. Artif Intell Med 87:67\u201377","DOI":"10.1016\/j.artmed.2018.04.001"},{"key":"6469_CR66","doi-asserted-by":"crossref","unstructured":"Pereira C, Weber S, Hook C, Rosa G, Papa J (2016) Pereira C, Weber S, Hook C, Rosa G, Papa J (2016) Deep learning-aided Parkinson\u2019s disease diagnosis from handwritten dynamics. In: Conference on graphics, patterns and ages. IEEE, pp 340\u2013346","DOI":"10.1109\/SIBGRAPI.2016.054"},{"key":"6469_CR67","doi-asserted-by":"crossref","unstructured":"Pirlo G, Diaz M, Ferrer M, Impedovo D, Occhionero F, Zurlo U (2015) Early diagnosis of neurodegenerative diseases by handwritten signature analysis. In: Conference on image analysis and processing (ICIAP). Springer, pp 290\u2013297","DOI":"10.1007\/978-3-319-23222-5_36"},{"key":"6469_CR68","doi-asserted-by":"crossref","unstructured":"Post B, Merkus MP, de Bie RM, de Haan RJ, Speelman JD (2005) Unified Parkinson\u2019s disease rating scale motor examination: are ratings of nurses, residents in neurology, and movement disorders specialists interchangeable? Move Disord 20(12):1577\u20131584","DOI":"10.1002\/mds.20640"},{"key":"6469_CR69","doi-asserted-by":"crossref","unstructured":"Reeve A, Simcox E, Turnbull D (2014) Ageing and Parkinson\u2019s disease: why is advancing age the biggest risk factor? Ageing Res Rev 14:19\u201330","DOI":"10.1016\/j.arr.2014.01.004"},{"key":"6469_CR70","doi-asserted-by":"crossref","unstructured":"Ribeiro LC, Afonso LC, Papa JP (2019) Bag of samplings for computer-assisted Parkinson\u2019s disease diagnosis based on recurrent neural networks. Comput Biol Med 115:103477","DOI":"10.1016\/j.compbiomed.2019.103477"},{"key":"6469_CR71","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"6469_CR72","doi-asserted-by":"crossref","unstructured":"Samii A, Nutt JG, Ransom BR (2004) Parkinson\u2019 disease. Lancet 363(9423):1783\u20131793","DOI":"10.1016\/S0140-6736(04)16305-8"},{"key":"6469_CR73","doi-asserted-by":"crossref","unstructured":"Saunders-Pullman R, Derby C, Stanley K, Floyd A, Bressman S, Lipton RB, Deligtisch A, Severt L, Yu Q, Kurtis M et al (2008) Validity of spiral analysis in early Parkinson\u2019s disease. Offic J Move Disord Soc 23(4):531\u2013537","DOI":"10.1002\/mds.21874"},{"key":"6469_CR74","doi-asserted-by":"crossref","unstructured":"Seedat N, Aharonson V, Schlesinger I (2020) Automated machine vision enabled detection of movement disorders from hand drawn spirals. In: 2020 IEEE international conference on healthcare informatics (ICHI). IEEE, pp 1\u20135","DOI":"10.1109\/ICHI48887.2020.9374333"},{"key":"6469_CR75","unstructured":"Shenoy AA, Lones MA, Smith SL, Vallejo M (2021) Evaluation of recurrent neural network models for Parkinson\u2019s disease classification using drawing data. In: 43rd annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE"},{"issue":"2","key":"6469_CR76","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1038\/nprot.2006.115","volume":"1","author":"MS Shin","year":"2006","unstructured":"Shin MS, Park SY, Park SR, Seol SH, Kwon JS (2006) Clinical and empirical applications of the Rey\u2013Osterrieth complex figure test. Nat Protoc 1(2):892","journal-title":"Nat Protoc"},{"issue":"1","key":"6469_CR77","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar T (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60","journal-title":"J Big Data"},{"key":"6469_CR78","doi-asserted-by":"crossref","unstructured":"Smits EJ, Tolonen AJ, Cluitmans L, van Gils M, Conway BA, Zietsma RC, Leenders KL, Maurits NM (2014) Standardized handwriting to assess bradykinesia, micrographia and tremor in Parkinson\u2019s disease. PLoS ONE 9(5):e97614","DOI":"10.1371\/journal.pone.0097614"},{"key":"6469_CR79","doi-asserted-by":"crossref","unstructured":"de Souza RW, Silva DS, Passos LA, Roder M, Santana MC, Pinheiro PR, de Albuquerque VHC (2021) Computer-assisted Parkinson\u2019s disease diagnosis using fuzzy optimum-path forest and restricted Boltzmann machines. Comput Biol Med 131:104260","DOI":"10.1016\/j.compbiomed.2021.104260"},{"issue":"3","key":"6469_CR80","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.parkreldis.2009.12.007","volume":"16","author":"K Stanley","year":"2010","unstructured":"Stanley K, Hagenah J, Br\u00fcggemann N, Reetz K, Severt L, Klein C, Yu Q, Derby C, Pullman S, Saunders-Pullman R (2010) Digitized spiral analysis is a promising early motor marker for Parkinson disease. Parkin Rel Disord 16(3):233\u2013234","journal-title":"Parkin Rel Disord"},{"key":"6469_CR81","doi-asserted-by":"crossref","unstructured":"Szumilas M, Lewenstein K, \u015alubowska E, Szlufik S, Koziorowski D (2020) A multimodal approach to the quantification of kinetic tremor in Parkinson\u2019s disease. Sensors 20(1):184","DOI":"10.3390\/s20010184"},{"key":"6469_CR82","doi-asserted-by":"crossref","unstructured":"Tucha O, Mecklinger L, Thome J, Reiter A, Alders G, Sartor H, Naumann M, Lange K (2006) Kinematic analysis of dopaminergic effects on skilled handwriting movements in Parkinson\u2019s disease. J Neural Transm 113(5):609\u2013623","DOI":"10.1007\/s00702-005-0346-9"},{"issue":"6","key":"6469_CR83","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1016\/j.conb.2010.08.022","volume":"20","author":"RS Turner","year":"2010","unstructured":"Turner RS, Desmurget M (2010) Basal ganglia contributions to motor control: a vigorous tutor. Curr Opin Neurobiol 20(6):704\u2013716","journal-title":"Curr Opin Neurobiol"},{"key":"6469_CR84","doi-asserted-by":"crossref","unstructured":"\u00dcnl\u00fc A, Brause R, Krakow K (2006) Handwriting analysis for diagnosis and prognosis of Parkinson\u2019s disease. In: International symposium on biological and medical data analysis. Springer, pp 441\u2013450","DOI":"10.1007\/11946465_40"},{"key":"6469_CR85","doi-asserted-by":"crossref","unstructured":"Vallejo M, Jamieson S, Cosgrove J, Smith SL, Lones MA, Alty JE, Corne DW (2016) Exploring diagnostic models of Parkinson\u2019s disease with multi-objective regression. In: Symposium series on computational intelligence (SSCI). IEEE, pp 1\u20138","DOI":"10.1109\/SSCI.2016.7849884"},{"key":"6469_CR86","doi-asserted-by":"crossref","unstructured":"V\u00e1squez-Correa JC, Arias-Vergara T, Orozco-Arroyave JR, Eskofier B, Klucken J, N\u00f6th E (2018) Multimodal assessment of Parkinson\u2019s disease: a deep learning approach. J Biomed Health Inform 23(4):1618\u20131630","DOI":"10.1109\/JBHI.2018.2866873"},{"key":"6469_CR87","doi-asserted-by":"crossref","unstructured":"Wang Q, Hopgood JR, Finlayson N, Williams GO, Fernandes S, Williams E, Akram A, Dhaliwal K, Vallejo M (2020) Deep learning in ex-vivo lung cancer discrimination using fluorescence lifetime endomicroscopic images. In: 42nd annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 1891\u20131894","DOI":"10.1109\/EMBC44109.2020.9175598"},{"key":"6469_CR88","doi-asserted-by":"crossref","unstructured":"Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H (2018) Classification of Alzheimer\u2019s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42(5):85","DOI":"10.1007\/s10916-018-0932-7"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06469-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06469-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06469-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T15:33:53Z","timestamp":1642779233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06469-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,8]]},"references-count":88,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["6469"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06469-7","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,8]]},"assertion":[{"value":"8 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 August 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}