This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
J. Dumurgier and C. Tzourio, “Epidemiology of neurological diseases in older adults,” Revue Neurologique, vol. 176, no. 9. Elsevier Masson s.r.l., pp. 642–648, Nov. 01, 2020. doi: 10.1016/j.neurol.2020.01.356.DumurgierJ.TzourioC.“Epidemiology of neurological diseases in older adults,”1769Elsevier Masson s.r.l.642648Nov.01202010.1016/j.neurol.2020.01.356Open DOISearch in Google Scholar
M. Fayyad et al., “Parkinson's disease biomarkers based on α-synuclein,” Journal of Neurochemistry, vol. 150, no. 5. Blackwell Publishing Ltd, pp. 626–636, 2019. doi: 10.1111/jnc.14809.FayyadM.“Parkinson's disease biomarkers based on α-synuclein,”1505Blackwell Publishing Ltd,626636201910.1111/jnc.14809Open DOISearch in Google Scholar
G. E. Alexander, “Biology of Parkinson's disease: Pathogenesis and pathophysiology of a multisystem neurodegenerative disorder,” Dialogues in Clinical Neuroscience, vol. 6, no. 3. pp. 259–280, 2004. doi: 10.31887/dcns.2004.6.3/galexander.AlexanderG. E.“Biology of Parkinson's disease: Pathogenesis and pathophysiology of a multisystem neurodegenerative disorder,”63259280200410.31887/dcns.2004.6.3/galexanderOpen DOISearch in Google Scholar
M. Lelos, “Overview of Alzheimer's and Parkinson's diseases and the role of protein aggregation in these neurodegenerative diseases,” in Handbook of Innovations in Central Nervous System Regenerative Medicine, Elsevier, 2020, pp. 29–53. doi: 10.1016/B978-0-12-818084-6.00002-7.LelosM.“Overview of Alzheimer's and Parkinson's diseases and the role of protein aggregation in these neurodegenerative diseases,”inElsevier2020295310.1016/B978-0-12-818084-6.00002-7Open DOISearch in Google Scholar
T. Vyas, R. Yadav, C. Solanki, R. Darji, S. Desai, and S. Tanwar, “Deep learning-based scheme to diagnose Parkinson's disease,” Expert Syst., vol. 39, no. 3, Mar. 2022, doi: 10.1111/exsy.12739.VyasT.YadavR.SolankiC.DarjiR.DesaiS.TanwarS.“Deep learning-based scheme to diagnose Parkinson's disease,”393Mar.202210.1111/exsy.12739Open DOISearch in Google Scholar
N. Van Den Berge and A. Ulusoy, “Animal models of brain-first and body-first Parkinson's disease,” Neurobiol. Dis., vol. 163, Feb. 2022, doi: 10.1016/j.nbd.2021.105599.Van Den BergeN.UlusoyA.“Animal models of brain-first and body-first Parkinson's disease,”163Feb.202210.1016/j.nbd.2021.105599Open DOISearch in Google Scholar
World Health Organisation, “Launch of WHO's Parkinson disease technical brief,” Who.Int, 2022. https://www.who.int/news/item/14-06-2022-launch-ofwho-s-parkinson-disease-technical-briefWorld Health Organisation“Launch of WHO's Parkinson disease technical brief,”2022https://www.who.int/news/item/14-06-2022-launch-ofwho-s-parkinson-disease-technical-briefSearch in Google Scholar
C. Jatoth, E. Neelima, A. V. R. Mayuri, and S. R. Annaluri, “Effective monitoring and prediction of Parkinson disease in Smart Cities using intelligent health care system,” Microprocess. Microsyst., vol. 92, no. May, p. 104547, 2022, doi: 10.1016/j.micpro.2022.104547.JatothC.NeelimaE.MayuriA. V. R.AnnaluriS. R.“Effective monitoring and prediction of Parkinson disease in Smart Cities using intelligent health care system,”92May104547202210.1016/j.micpro.2022.104547Open DOISearch in Google Scholar
P. D. and M. D. Society and I. Mumbai, “The PDMDS Story – Parkinson's Disease and Movement.” https://www.parkinsonssocietyindia.com/the-pdmds-story/P. D. and M. D. Society and I. Mumbaihttps://www.parkinsonssocietyindia.com/the-pdmds-story/Search in Google Scholar
T. John, “The Rights of Persons with Disabilities Act 2016 and Psychiatric Care,” Kerala J. Psychiatry, vol. 33, no. 1, 2020, doi: 10.30834/kjp.33.1.2020.183.JohnT.“The Rights of Persons with Disabilities Act 2016 and Psychiatric Care,”331202010.30834/kjp.33.1.2020.183Open DOISearch in Google Scholar
D. G. N. RAJU and Secretary to the Govt. of India, THE RIGHTS OF PERSONS WITH DISABILITIES ACT, 2016. 2016. [Online]. Available: https://deoc.in/wp-content/uploads/2018/10/Rights-of-Persons-with-Disabilities-RPWD-Act-2016.pdfD. G. N. RAJU and Secretary to the Govt. of India2016[Online]. Available: https://deoc.in/wp-content/uploads/2018/10/Rights-of-Persons-with-Disabilities-RPWD-Act-2016.pdfSearch in Google Scholar
H. Modi, J. Hathaliya, M. S. Obaidiat, R. Gupta, and S. Tanwar, “Deep Learning-based Parkinson disease Classification using PET Scan Imaging Data,” in 2021 IEEE 6th International Conference on Computing, Communication and Automation, ICCCA 2021, Institute of Electrical and Electronics Engineers Inc., 2021, pp. 837–841. doi: 10.1109/ICCCA52192.2021.9666251.ModiH.HathaliyaJ.ObaidiatM. S.GuptaR.TanwarS.in2021 IEEE 6th International Conference on Computing, Communication and Automation, ICCCA 2021Institute of Electrical and Electronics Engineers Inc.202183784110.1109/ICCCA52192.2021.9666251Open DOISearch in Google Scholar
M. M. McGregor and A. B. Nelson, “Circuit Mechanisms of Parkinson's Disease,” Neuron, vol. 101, no. 6. Cell Press, pp. 1042–1056, Mar. 20, 2019. doi: 10.1016/j.neuron.2019.03.004.McGregorM. M.NelsonA. B.“Circuit Mechanisms of Parkinson's Disease,”1016Cell Press,10421056Mar.20201910.1016/j.neuron.2019.03.004Open DOISearch in Google Scholar
H. Kour and M. K. Gupta, An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM, vol. 81, no. 17. Multimedia Tools and Applications, 2022. doi: 10.1007/s11042-022-12648-y.KourH.GuptaM. K.An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM8117202210.1007/s11042-022-12648-yOpen DOISearch in Google Scholar
“The Good Star t Program 03/03/2022,” PARKINSON'S ASSOCIATION OF SAN DIEGO, 2022. WWW.PARKINSONSASSOCIATION.ORG“The Good Star t Program 03/03/2022,”2022WWW.PARKINSONSASSOCIATION.ORGSearch in Google Scholar
F. J. R. Eccles, N. Sowter, T. Spokes, N. Zarotti, and J. Simpson, “Stigma, self-compassion, and psychological distress among people with Parkinson's,” Disabil. Rehabil., vol. 0, no. 0, pp. 1–9, 2022, doi: 10.1080/09638288.2022.2037743.EcclesF. J. R.SowterN.SpokesT.ZarottiN.SimpsonJ.“Stigma, self-compassion, and psychological distress among people with Parkinson's,”0019202210.1080/09638288.2022.2037743Open DOISearch in Google Scholar
H. Braak and E. Braak, “Pathoanatomy of Parkinson's disease,” J Neurol, vol. 247 [Suppl, 2000, doi: 10.1007/PL00007758.BraakH.BraakE.“Pathoanatomy of Parkinson's disease,”247Suppl,200010.1007/PL00007758Open DOISearch in Google Scholar
A. P. Valenti, M. Chita-Tegmark, L. Tickle-Degnen, A. W. Bock, and M. J. Scheutz, “Using topic modeling to infer the emotional state of people living with Parkinson's disease,” Assist. Technol. Taylor Fr., vol. 33, no. 3, pp. 136–145, 2021, doi: 10.1080/10400435.2019.1623342.ValentiA. P.Chita-TegmarkM.Tickle-DegnenL.BockA. W.ScheutzM. J.“Using topic modeling to infer the emotional state of people living with Parkinson's disease,”333136145202110.1080/10400435.2019.1623342Open DOISearch in Google Scholar
M. N. Dar, M. U. Akram, R. Yuvaraj, S. Gul Khawaja, and M. Murugappan, “EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning,” Comput. Biol. Med., vol. 144, May 2022, doi: 10.1016/j.compbiomed.2022.105327.DarM. N.AkramM. U.YuvarajR.Gul KhawajaS.MurugappanM.“EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning,”144May202210.1016/j.compbiomed.2022.105327Open DOISearch in Google Scholar
A. Wootton, N. J. Starkey, and C. C. Barber, “Unmoving and unmoved: experiences and consequences of impaired non-verbal expressivity in Parkinson's patients and their spouses,” Disabil. Rehabil., vol. 41, no. 21, pp. 2516–2527, 2019, doi: 10.1080/09638288.2018.1471166.WoottonA.StarkeyN. J.BarberC. C.“Unmoving and unmoved: experiences and consequences of impaired non-verbal expressivity in Parkinson's patients and their spouses,”412125162527201910.1080/09638288.2018.1471166Open DOISearch in Google Scholar
S. Justyna and R. Burget, “Parkinson's Disease Detection based on Changes of Emotions during Speech,” in 2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2020, pp. 119–123.JustynaS.BurgetR.in2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)2020119123Search in Google Scholar
R. Hoegen, J. Gratch, B. Parkinson, and D. Shore, “Signals of Emotion Regulation in a Social Dilemma: Detection from Face and Context,” in International Conference on Affective Computing and Intelligent Interaction,HoegenR.GratchJ.ParkinsonB.ShoreD.inInternational Conference on Affective Computing and Intelligent InteractionSearch in Google Scholar
Lucia Pepa, M. Capecci, and M. G. Ceravolo, “Smartwatch based emotion recognition in Parkinson's disease Lucia,” in IEEE International Symposium on Consumer Technologies (ISCT),PepaLuciaCapecciM.CeravoloM. G.inIEEE International Symposium on Consumer Technologies (ISCT)Search in Google Scholar
T. Lencioni et al., “The effect of music-induced emotion on visual-spatial learning in people with Parkinson's disease_A pilot study,” Parkinsonism Relat. Disord.LencioniT.“The effect of music-induced emotion on visual-spatial learning in people with Parkinson's disease_A pilot study,”Search in Google Scholar
M. T. M. Prenger, R. Madray, K. Van Hedger, M. Anello, and P. A. Macdonald, “Social Symptoms of Parkinson's Disease,” Parkinsons. Dis., vol. 2020, 2020, doi: 10.1155/2020/8846544.PrengerM. T. M.MadrayR.Van HedgerK.AnelloM.MacdonaldP. A.“Social Symptoms of Parkinson's Disease,”2020202010.1155/2020/8846544Open DOISearch in Google Scholar
M. Mengi and D. Malhotra, Artificial Intelligence Based Techniques for the Detection of Socio-Behavioral Disorders: A Systematic Review, vol. 29, no. 5. Springer Netherlands, 2022. doi: 10.1007/s11831-021-09682-8.MengiM.MalhotraD.295Springer Netherlands202210.1007/s11831-021-09682-8Open DOISearch in Google Scholar
S. Yadav, M. Kumar, and P. Saurabh, “Artificial Intelligence Model for Parkinson Disease Detection Using Machine Learning Algorithms,” Biomed. Mater. Devices, no. 0123456789, 2023, doi: 10.1007/s44174-023-00068-x.YadavS.KumarM.SaurabhP.“Artificial Intelligence Model for Parkinson Disease Detection Using Machine Learning Algorithms,”0123456789202310.1007/s44174-023-00068-xOpen DOISearch in Google Scholar
M. Gazda, M. Hires, and P. Drotar, “Multiple-Fine-Tuned Convolutional Neural Networks for Parkinson's Disease Diagnosis from Offline Handwriting,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 52, no. 1, pp. 78–89, 2022, doi: 10.1109/TSMC.2020.3048892.GazdaM.HiresM.DrotarP.“Multiple-Fine-Tuned Convolutional Neural Networks for Parkinson's Disease Diagnosis from Offline Handwriting,”5217889202210.1109/TSMC.2020.3048892Open DOISearch in Google Scholar
J. E. Perkins et al., “Saccade, Pupil, and Blink Responses in Rapid Eye Movement Sleep Behavior Disorder,” Mov. Disord., vol. 36, no. 7, pp. 1720–1726, 2021, doi: 10.1002/mds.28585.PerkinsJ. E.“Saccade, Pupil, and Blink Responses in Rapid Eye Movement Sleep Behavior Disorder,”36717201726202110.1002/mds.28585Open DOISearch in Google Scholar
H. Kathuria et al., “Utility of Imaging of Nigrosome-1 on 3T MRI and Its Comparison with 18F-DOPA PET in the Diagnosis of Idiopathic Parkinson Disease and Atypical Parkinsonism,” Mov. Disord. Clin. Pract., vol. 8, no. 2, pp. 224–230, Feb. 2021, doi: 10.1002/mdc3.13091.KathuriaH.“Utility of Imaging of Nigrosome-1 on 3T MRI and Its Comparison with 18F-DOPA PET in the Diagnosis of Idiopathic Parkinson Disease and Atypical Parkinsonism,”82224230Feb.202110.1002/mdc3.13091Open DOISearch in Google Scholar
M. P. Adams, A. Rahmim, and J. Tang, “Improved motor outcome prediction in Parkinson's disease applying deep learning to DaTscan SPECT images,” Comput. Biol. Med., vol. 132, May 2021, doi: 10.1016/j.compbiomed.2021.104312.AdamsM. P.RahmimA.TangJ.“Improved motor outcome prediction in Parkinson's disease applying deep learning to DaTscan SPECT images,”132May202110.1016/j.compbiomed.2021.104312Open DOISearch in Google Scholar
N. Salari, M. Kazeminia, H. Sagha, A. Daneshkhah, A. Ahmadi, and M. Mohammadi, “The performance of various machine learning methods for Parkinson's disease recognition: a systematic review,” Curr. Psychol., vol. 42, no. 20, pp. 16637–16660, 2023, doi: 10.1007/s12144-022-02949-8.SalariN.KazeminiaM.SaghaH.DaneshkhahA.AhmadiA.MohammadiM.“The performance of various machine learning methods for Parkinson's disease recognition: a systematic review,”42201663716660202310.1007/s12144-022-02949-8Open DOISearch in Google Scholar
Z. Ayaz, S. Naz, N. H. Khan, I. Razzak, and M. Imran, Automated methods for diagnosis of Parkinson's disease and predicting severity level, vol. 35, no. 20. Springer London, 2022. doi: 10.1007/s00521-021-06626-y.AyazZ.NazS.KhanN. H.RazzakI.ImranM.3520Springer London,202210.1007/s00521-021-06626-yOpen DOISearch in Google Scholar
H. Kour and M. K. Gupta, “AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19,” Neural Process. Lett., 2022, doi: 10.1007/s11063-022-11112-0.KourH.GuptaM. K.“AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19,”202210.1007/s11063-022-11112-0Open DOISearch in Google Scholar
A. Laar, A. L. Silva de Lima, B. R. Maas, B. R. Bloem, and N. M. de Vries, “Successful implementation of technology in the management of Parkinson's disease: Barriers and facilitators,” Clin. Park. Relat. Disord., vol. 8, no. November 2022, p. 100188, 2023, doi: 10.1016/j.prdoa.2023.100188.LaarA.Silva de LimaA. L.MaasB. R.BloemB. R.de VriesN. M.“Successful implementation of technology in the management of Parkinson's disease: Barriers and facilitators,”8no. November 2022,100188202310.1016/j.prdoa.2023.100188Open DOISearch in Google Scholar
H. Kour and M. K. Gupta, “Predicting the language of depression from multivariate twitter data using a feature-rich hybrid deep learning model,” Concurr. Comput. Pract. Exp., vol. 34, no. 24, pp. 1–21, 2022, doi: 10.1002/cpe.7224.KourH.GuptaM. K.“Predicting the language of depression from multivariate twitter data using a feature-rich hybrid deep learning model,”3424121202210.1002/cpe.7224Open DOISearch in Google Scholar
M. G. Krokidis et al., “A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes,” Sensors, 2022, doi: https://doi.org/10.3390/s22020409.KrokidisM. G.“A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes,”2022doi: https://doi.org/10.3390/s22020409.Search in Google Scholar
S. Shafiq, M. S. Kaiser, M. Mahmud, M. S. Hossain, and K. Andersson, “Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson's Disease Diagnosis,” IEEE Access, vol. 11, pp. 479–488, 2023, doi: 10.1016/B978-0-323-46294-5.00028-5.ShafiqS.KaiserM. S.MahmudM.HossainM. S.AnderssonK.“Comprehensive Analysis of Nature-Inspired Algorithms for Parkinson's Disease Diagnosis,”11479488202310.1016/B978-0-323-46294-5.00028-5Open DOISearch in Google Scholar
M. Shaban, “Deep Learning for Parkinson's Disease Diagnosis: A Short Survey,” Comput. Spec. Issue Futur. Syst. Based Healthc. 5.0 Pandemic Prep., 2023, doi: https://doi.org/10.3390/computers12030058.ShabanM.“Deep Learning for Parkinson's Disease Diagnosis: A Short Survey,”2023doi: https://doi.org/10.3390/computers12030058.Search in Google Scholar
S. Kumar, B. Basumatary, R. Bansal, and A. Kumar, “Techniques for the detection and management of freezing of gait in Parkinson's disease – A systematic review and future perspectives,” MethodsX, vol. 10, no. December 2022, p. 102106, 2023, doi: 10.1016/j.mex.2023.102106.KumarS.BasumataryB.BansalR.KumarA.“Techniques for the detection and management of freezing of gait in Parkinson's disease – A systematic review and future perspectives,”10no. December 2022,102106202310.1016/j.mex.2023.102106Open DOISearch in Google Scholar
S. Dixit et al., “A Comprehensive Review on AI-Enabled Models for Parkinson's Disease Diagnosis,” Electron., vol. 12, no. 4, pp. 1–50, 2023, doi: 10.3390/electronics12040783.DixitS.“A Comprehensive Review on AI-Enabled Models for Parkinson's Disease Diagnosis,”124150202310.3390/electronics12040783Open DOISearch in Google Scholar
K. Khanna, S. Gambhir, and M. Gambhir, “Comparative analysis of machine learning techniques for Parkinson's detection: A review,” Multimed. Tools Appl., no. 0123456789, 2023, doi: 10.1007/s11042-023-15414-w.KhannaK.GambhirS.GambhirM.“Comparative analysis of machine learning techniques for Parkinson's detection: A review,”0123456789202310.1007/s11042-023-15414-wOpen DOISearch in Google Scholar
J. Zhang, “Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease,” npj Park. Dis., vol. 8, no. 1, 2022, doi: 10.1038/s41531-021-00266-8.ZhangJ.“Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease,”81202210.1038/s41531-021-00266-8Open DOISearch in Google Scholar
A. S. Chandrabhatla, I. J. Pomeraniec, and A. Ksendzovsky, “Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms,” npj Digit. Med., vol. 5, no. 1, pp. 1–18, 2022, doi: 10.1038/s41746-022-00568-y.ChandrabhatlaA. S.PomeraniecI. J.KsendzovskyA.“Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms,”51118202210.1038/s41746-022-00568-yOpen DOISearch in Google Scholar
K. Giannakopoulou and I. Roussaki, “Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review,” Sensors, 2022, doi: https://doi.org/10.3390/s22051799.GiannakopoulouK.RoussakiI.“Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review,”2022doi: https://doi.org/10.3390/s22051799.Search in Google Scholar
A. Rana, A. Dumka, R. Singh, M. K. Panda, and N. Priyadarshi, “A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives,” Diagnostics, vol. 12, no. 11, 2022, doi: 10.3390/diagnostics12112708.RanaA.DumkaA.SinghR.PandaM. K.PriyadarshiN.“A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives,”1211202210.3390/diagnostics12112708Open DOISearch in Google Scholar
M. Tanveer, A. H. Rashid, R. Kumar, and R. Balasubramanian, “Parkinson's disease diagnosis using neural networks: Survey and comprehensive evaluation,” Inf. Process. Manag., vol. 59, no. 3, p. 102909, 2022, doi: 10.1016/j.ipm.2022.102909.TanveerM.RashidA. H.KumarR.BalasubramanianR.“Parkinson's disease diagnosis using neural networks: Survey and comprehensive evaluation,”593102909202210.1016/j.ipm.2022.102909Open DOISearch in Google Scholar
A. ul Haq et al., “A survey of deep learning techniques based Parkinson's disease recognition methods employing clinical data,” Expert Syst. Appl., vol. 208, no. July, p. 118045, 2022, doi: 10.1016/j.eswa.2022.118045.ul HaqA.“A survey of deep learning techniques based Parkinson's disease recognition methods employing clinical data,”208July118045202210.1016/j.eswa.2022.118045Open DOISearch in Google Scholar
A. Rana, A. Dumka, R. Singh, M. K. Panda, N. Priyadarshi, and B. Twala, “Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations,” Diagnostics, mdpi, 2022, https://doi.org/10.3390/diagnostics12082003#Academic.RanaA.DumkaA.SinghR.PandaM. K.PriyadarshiN.TwalaB.“Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations,”2022https://doi.org/10.3390/diagnostics12082003#Academic.Search in Google Scholar
M. S. Alzubaidi et al., “The role of neural network for the detection of parkinson's disease: A scoping review,” Healthc., vol. 9, no. 6, pp. 1–20, 2021, doi: 10.3390/healthcare9060740.AlzubaidiM. S.“The role of neural network for the detection of parkinson's disease: A scoping review,”96120202110.3390/healthcare9060740Open DOISearch in Google Scholar
H. W. Loh et al., “Application of deep learning models for automated identification of parkinson's disease: A review (2011–2021),” Sensors, vol. 21, no. 21, pp. 1–25, 2021, doi: 10.3390/s21217034.LohH. W.“Application of deep learning models for automated identification of parkinson's disease: A review (2011–2021),”2121125202110.3390/s21217034Open DOISearch in Google Scholar
M. B. T. Noor, N. Z. Zenia, M. S. Kaiser, S. Al Mamun, and M. Mahmud, “Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's disease and schizophrenia,” Brain Informatics, vol. 7, no. 1, 2020, doi: 10.1186/s40708-020-00112-2.NoorM. B. T.ZeniaN. Z.KaiserM. S.Al MamunS.MahmudM.“Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's disease and schizophrenia,”71202010.1186/s40708-020-00112-2Open DOISearch in Google Scholar
H. Khachnaoui, R. Mabrouk, and N. Khlifa, “Machine learning and deep learning for clinical data and PET/SPECT imaging in parkinson's disease: A review,” IET Image Process., vol. 14, no. 16, pp. 1–14, 2020, doi: 10.1049/iet-ipr.2020.1048.KhachnaouiH.MabroukR.KhlifaN.“Machine learning and deep learning for clinical data and PET/SPECT imaging in parkinson's disease: A review,”1416114202010.1049/iet-ipr.2020.1048Open DOISearch in Google Scholar
L. Di Biase et al., “Gait analysis in parkinson's disease: An overview of the most accurate markers for diagnosis and symptoms monitoring,” Sensors (Switzerland), vol. 20, no. 12, p. 1, 2020, doi: 10.3390/s20123529.Di BiaseL.“Gait analysis in parkinson's disease: An overview of the most accurate markers for diagnosis and symptoms monitoring,”20121202010.3390/s20123529Open DOISearch in Google Scholar
S. Mangesius et al., “Novel decision algorithm to discriminate parkinsonism with combined blood and imaging biomarkers,” Park. Relat. Disord., vol. 77, pp. 57–63, Aug. 2020, doi: 10.1016/j.parkreldis.2020.05.033.MangesiusS.“Novel decision algorithm to discriminate parkinsonism with combined blood and imaging biomarkers,”775763Aug.202010.1016/j.parkreldis.2020.05.033Open DOISearch in Google Scholar
S. Sivaranjini and C. M. Sujatha, “Deep learning based diagnosis of Parkinson's disease using convolutional neural network,” Multimed. Tools Appl., vol. 79, no. 21–22, pp. 15467–15479, Jun. 2020, doi: 10.1007/s11042-019-7469-8.SivaranjiniS.SujathaC. M.“Deep learning based diagnosis of Parkinson's disease using convolutional neural network,”7921–221546715479Jun.202010.1007/s11042-019-7469-8Open DOISearch in Google Scholar
G. Solana-Lavalle and R. Rosas-Romero, “Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson's disease,” Comput. Methods Programs Biomed., vol. 198, Jan. 2021, doi: 10.1016/j.cmpb.2020.105793.Solana-LavalleG.Rosas-RomeroR.“Classification of PPMI MRI scans with voxel-based morphometry and machine learning to assist in the diagnosis of Parkinson's disease,”198Jan.202110.1016/j.cmpb.2020.105793Open DOISearch in Google Scholar
E. Huseyn, “Deep Learning Based Early Diagnostics of Parkinson's Disease,” 2020. doi: arXiv Preprint arXiv:2008.01792.HuseynE.2020doi: arXiv Preprint arXiv:2008.01792.Search in Google Scholar
S. Chakraborty, S. Aich, and H. C. Kim, “Detection of Parkinson's disease from 3T T1 weighted MRI scans using 3D convolutional neural network,” Diagnostics, vol. 10, no. 6, pp. 1–17, 2020, doi: 10.3390/diagnostics10060402.ChakrabortyS.AichS.KimH. C.“Detection of Parkinson's disease from 3T T1 weighted MRI scans using 3D convolutional neural network,”106117202010.3390/diagnostics10060402Open DOISearch in Google Scholar
X. Cui et al., “Diagnosis of Parkinson's disease based on feature fusion on T2 MRI images,” Int. J. Intell. Syst. - Wiley Online Libr., vol. 37, no. 12, pp. 11362–11381, 2022, doi: https://doi.org/10.1002/int.23046.CuiX.“Diagnosis of Parkinson's disease based on feature fusion on T2 MRI images,”371211362113812022doi: https://doi.org/10.1002/int.23046.Search in Google Scholar
S. Sangeetha, K. Baskar, P. C. Kalaivaani, and T. Kumaravel, “Deep Learning-based Early Parkinson's Disease Detection from Brain MRI Image,” in ICICCS-2023, 2023, pp. 490–495. [Online]. Available: https://ieeexplore.ieee.org/document/10142754SangeethaS.BaskarK.KalaivaaniP. C.KumaravelT.“Deep Learning-based Early Parkinson's Disease Detection from Brain MRI Image,”in2023490495[Online]. Available: https://ieeexplore.ieee.org/document/10142754Search in Google Scholar
G. C. Monte-Rubio et al., “Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson's disease data-set,” Hum. Brain Mapp., vol. 43, no. 10, pp. 3130–3142, 2022, doi: 10.1002/hbm.25838.Monte-RubioG. C.“Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson's disease data-set,”431031303142202210.1002/hbm.25838Open DOISearch in Google Scholar
J. Hathaliya et al., “Stacked Model-Based Classification of Parkinson's Disease Patients Using Imaging Biomarker Data,” Biosensors, vol. 12, no. 8, Aug. 2022, doi: 10.3390/bios12080579.HathaliyaJ.“Stacked Model-Based Classification of Parkinson's Disease Patients Using Imaging Biomarker Data,”128Aug.202210.3390/bios12080579Open DOISearch in Google Scholar
R. Splinter, “Positron emission tomography,” Handbook of Physics in Medicine and Biology, 2010.SplinterR.“Positron emission tomography,”2010Search in Google Scholar
A. P. Strafella et al., “Imaging Markers of Progression in Parkinson's Disease,” Movement Disorders Clinical Practice, vol. 5, no. 6. Wiley-Blackwell, pp. 586–596, Nov. 01, 2018. doi: 10.1002/mdc3.12673.StrafellaA. P.“Imaging Markers of Progression in Parkinson's Disease,”56Wiley-Blackwell,586596Nov.01201810.1002/mdc3.12673Open DOISearch in Google Scholar
Y. Dai, Z. Tang, Y. Wang, and Z. Xu, “Data Driven Intelligent Diagnostics for Parkinson's Disease,” IEEE Access, vol. 7, pp. 106941–106950, 2019, doi: 10.1109/ACCESS.2019.2931744.DaiY.TangZ.WangY.XuZ.“Data Driven Intelligent Diagnostics for Parkinson's Disease,”7106941106950201910.1109/ACCESS.2019.2931744Open DOISearch in Google Scholar
S. Booth, K. W. Park, C. S. Lee, and J. H. Ko, “Predicting cognitive decline in Parkinson's disease using FDG-PET based supervised learning,” J. Clin. Invest., Oct. 2022, doi: 10.1172/jci157074.BoothS.ParkK. W.LeeC. S.KoJ. H.“Predicting cognitive decline in Parkinson's disease using FDG-PET based supervised learning,”Oct.202210.1172/jci157074Open DOISearch in Google Scholar
Y. Wu et al., “Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls,” Ann. Transl. Med., vol. 7, no. 23, pp. 773–773, Dec. 2019, doi: 10.21037/atm.2019.11.26.WuY.“Use of radiomic features and support vector machine to distinguish Parkinson's disease cases from normal controls,”723773773Dec.201910.21037/atm.2019.11.26Open DOISearch in Google Scholar
X. Sun et al., “Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [18F]FDG PET imaging,” Eur. Radiol., vol. 32, no. 11, pp. 8008–8018, 2022, doi: 10.1007/s00330-022-08799-z.SunX.“Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [18F]FDG PET imaging,”321180088018202210.1007/s00330-022-08799-zOpen DOISearch in Google Scholar
B. Abhisheka, S. K. Biswas, B. Purkayastha, D. Das, and A. Escargueil, Recent trend in medical imaging modalities and their applications in disease diagnosis: a review, no. 0123456789. Springer US, 2023. doi: 10.1007/s11042-023-17326-1.AbhishekaB.BiswasS. K.PurkayasthaB.DasD.EscargueilA.no. 0123456789. Springer US,202310.1007/s11042-023-17326-1Open DOISearch in Google Scholar
M. Rumman, A. N. Tasneem, S. Farzana, M. I. Pavel, and M. A. Alam, “Early detection of Parkinson's disease using image processing and artificial neural network,” 2018 Jt. 7th Int. Conf. Informatics, Electron. Vis. 2nd Int. Conf. Imaging, Vis. Pattern Recognition, ICIEV-IVPR 2018, no. 1, pp. 256–261, 2019, doi: 10.1109/ICIEV.2018.8641081.RummanM.TasneemA. N.FarzanaS.PavelM. I.AlamM. A.2018 Jt. 7th Int. Conf. Informatics, Electron. Vis. 2nd Int. Conf. Imaging, Vis. Pattern Recognition, ICIEV-IVPR 20181256261201910.1109/ICIEV.2018.8641081Open DOISearch in Google Scholar
J. Hathaliya et al., “Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data,” Mathematics, vol. 10, no. 15, Aug. 2022, doi: 10.3390/math10152566.HathaliyaJ.“Convolutional Neural Network-Based Parkinson Disease Classification Using SPECT Imaging Data,”1015Aug.202210.3390/math10152566Open DOISearch in Google Scholar
P. R. Magesh, R. D. Myloth, and R. J. Tom, “An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery,” Comput. Biol. Med., vol. 126, Nov. 2020, doi: 10.1016/j.compbiomed.2020.104041.MageshP. R.MylothR. D.TomR. J.“An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery,”126Nov.202010.1016/j.compbiomed.2020.104041Open DOISearch in Google Scholar
H. R. Pereira and H. A. Ferreira, “Classification of Patients with Parkinson's Disease Using Medical Imaging and Artificial Intelligence Algorithms,” IFMBE Proc., vol. 76, pp. 2043–2056, 2020, doi: 10.1007/978-3-030-31635-8_241.PereiraH. R.FerreiraH. A.“Classification of Patients with Parkinson's Disease Using Medical Imaging and Artificial Intelligence Algorithms,”7620432056202010.1007/978-3-030-31635-8_241Open DOISearch in Google Scholar
M. Wenzel et al., “Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics,” Eur. J. Nucl. Med. Mol. Imaging, vol. 46, no. 13, pp. 2800–2811, Dec. 2019, doi: 10.1007/s00259-019-04502-5.WenzelM.“Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics,”461328002811Dec.201910.1007/s00259-019-04502-5Open DOISearch in Google Scholar
A. Ortiz, J. Munilla, M. Martínez-Ibañez, J. M. Górriz, J. Ramírez, and D. Salas-Gonzalez, “Parkinson's disease detection using isosurfaces-based features and convolutional neural networks,” Front. Neuroinform., vol. 13, May 2019, doi: 10.3389/fninf.2019.00048.OrtizA.MunillaJ.Martínez-IbañezM.GórrizJ. M.RamírezJ.Salas-GonzalezD.“Parkinson's disease detection using isosurfaces-based features and convolutional neural networks,”13May201910.3389/fninf.2019.00048Open DOISearch in Google Scholar
T. Mortezazadeh, H. Seyedarabi, B. Mahmoudian, and J. P. Islamian, “Imaging modalities in differential diagnosis of Parkinson's disease: opportunities and challenges,” Egypt. J. Radiol. Nucl. Med., vol. 52, no. 1, 2021, doi: 10.1186/s43055-021-00454-9.MortezazadehT.SeyedarabiH.MahmoudianB.IslamianJ. P.“Imaging modalities in differential diagnosis of Parkinson's disease: opportunities and challenges,”521202110.1186/s43055-021-00454-9Open DOISearch in Google Scholar
M. K. Reddy and P. Alku, “Exemplar-based Sparse Representations for Detection of Parkinson's Disease from Speech,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. PP, pp. 1–11, 2023, doi: 10.1109/TASLP.2023.3260709.ReddyM. K.AlkuP.“Exemplar-based Sparse Representations for Detection of Parkinson's Disease from Speech,”vol. PP,111202310.1109/TASLP.2023.3260709Open DOISearch in Google Scholar
S. C. and S. S. S. Aarushi Agarwal, “International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016: 3rd-5th, March 2016,”S. C. and S. S. S. Aarushi AgarwalSearch in Google Scholar
A. U. Haq et al., “Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson's Disease Using Voice Recordings,” IEEE Access, vol. 7, pp. 37718–37734, 2019, doi: 10.1109/ACCESS.2019.2906350.HaqA. U.“Feature Selection Based on L1-Norm Support Vector Machine and Effective Recognition System for Parkinson's Disease Using Voice Recordings,”73771837734201910.1109/ACCESS.2019.2906350Open DOISearch in Google Scholar
Z. Soumaya, B. D. Taoufiq, N. Benayad, B. Achraf, and A. Ammoumou, “A Hybrid Method for the Diagnosis and Classifying Parkinson's Patients based on Time – frequency Domain Properties and K - nearest Neighbor,” J. Med. Signals Sensors, 2020, doi: 10.4103/jmss.JMSS_61_18.SoumayaZ.TaoufiqB. D.BenayadN.AchrafB.AmmoumouA.“A Hybrid Method for the Diagnosis and Classifying Parkinson's Patients based on Time – frequency Domain Properties and K - nearest Neighbor,”202010.4103/jmss.JMSS_61_18Open DOISearch in Google Scholar
I. Karabayir, S. M. Goldman, S. Pappu, and O. Akbilgic, “Gradient boosting for Parkinson's disease diagnosis from voice recordings,” BMC Med. Inform. Decis. Mak., vol. 20, no. 1, pp. 1–7, 2020, doi: 10.1186/s12911-020-01250-7.KarabayirI.GoldmanS. M.PappuS.AkbilgicO.“Gradient boosting for Parkinson's disease diagnosis from voice recordings,”20117202010.1186/s12911-020-01250-7Open DOISearch in Google Scholar
Z. Soumaya, B. Drissi Taoufiq, N. Benayad, K. Yunus, and A. Abdelkrim, “The detection of Parkinson disease using the genetic algorithm and SVM classifier,” Appl. Acoust., vol. 171, 2021, doi: 10.1016/j.apacoust.2020.107528.SoumayaZ.Drissi TaoufiqB.BenayadN.YunusK.AbdelkrimA.“The detection of Parkinson disease using the genetic algorithm and SVM classifier,”171202110.1016/j.apacoust.2020.107528Open DOISearch in Google Scholar
S. Stuart et al., “Pro-Saccades Predict Cognitive Decline in Parkinson's Disease: ICICLE-PD,” Mov. Disord., vol. 34, no. 11, pp. 1690–1698, 2019, doi: 10.1002/mds.27813.StuartS.“Pro-Saccades Predict Cognitive Decline in Parkinson's Disease: ICICLE-PD,”341116901698201910.1002/mds.27813Open DOISearch in Google Scholar
F. Raschellà, S. Scafa, A. Puiatti, E. Martin Moraud, and P. L. Ratti, “Actigraphy Enables Home Screening of Rapid Eye Movement Behavior Disorder in Parkinson's Disease,” Annals of Neurology, vol. 93, no. 2. pp. 317–329, 2023. doi: 10.1002/ana.26517.RaschellàF.ScafaS.PuiattiA.Martin MoraudE.RattiP. L.“Actigraphy Enables Home Screening of Rapid Eye Movement Behavior Disorder in Parkinson's Disease,”932317329202310.1002/ana.26517Open DOISearch in Google Scholar
J. Bek, E. Poliakoff, and K. Lander, “Measuring emotion recognition by people with Parkinson's disease using eye-tracking with dynamic facial expressions,” J. Neurosci. Methods, vol. 331, p. 108524, 2020, doi: 10.1016/j.jneumeth.2019.108524.BekJ.PoliakoffE.LanderK.“Measuring emotion recognition by people with Parkinson's disease using eye-tracking with dynamic facial expressions,”331108524202010.1016/j.jneumeth.2019.108524Open DOISearch in Google Scholar
S. Masiala, W. Huijbers, and M. Atzmueller, “Feature-Set-Engineering for Detecting Freezing of Gait in Parkinson's Disease using Deep Recurrent Neural Networks,” Electr. Eng. Syst. Sci., 2019, [Online]. Available: https://arxiv.org/abs/1909.03428MasialaS.HuijbersW.AtzmuellerM.“Feature-Set-Engineering for Detecting Freezing of Gait in Parkinson's Disease using Deep Recurrent Neural Networks,”2019[Online]. Available: https://arxiv.org/abs/1909.03428Search in Google Scholar
S. Rupprechter et al., “A clinically interpretable computer-vision based method for quantifying gait in parkinson's disease,” Sensors, vol. 21, no. 16, pp. 1–21, 2021, doi: 10.3390/s21165437.RupprechterS.“A clinically interpretable computer-vision based method for quantifying gait in parkinson's disease,”2116121202110.3390/s21165437Open DOISearch in Google Scholar
N. Kour, Sunanda, and S. Arora, “Computer-vision based diagnosis of Parkinson's disease via gait: A survey,” IEEE Access, vol. 7, pp. 156620–156645, 2019, doi: 10.1109/ACCESS.2019.2949744.KourN.SunandaAroraS.“Computer-vision based diagnosis of Parkinson's disease via gait: A survey,”7156620156645201910.1109/ACCESS.2019.2949744Open DOISearch in Google Scholar
S. B. Zahra, M. A. Khan, S. Abbas, K. M. Khan, M. A. Al-Ghamdi, and S. H. Almotiri, “Marker-based and marker-less motion capturing video data: Person and activity identification comparison based on machine learning approaches,” Comput. Mater. Contin., vol. 66, no. 2, pp. 1269–1282, 2020, doi: 10.32604/cmc.2020.012778.ZahraS. B.KhanM. A.AbbasS.KhanK. M.Al-GhamdiM. A.AlmotiriS. H.“Marker-based and marker-less motion capturing video data: Person and activity identification comparison based on machine learning approaches,”66212691282202010.32604/cmc.2020.012778Open DOISearch in Google Scholar
B. Sathya Bama and Y. Bevish Jinila, “Vision-based gait analysis for real-time Parkinson disease identification and diagnosis system,” Heal. Syst., pp. 1–11, Sep. 2022, doi: 10.1080/20476965.2022.2125838.Sathya BamaB.Bevish JinilaY.“Vision-based gait analysis for real-time Parkinson disease identification and diagnosis system,”111Sep.202210.1080/20476965.2022.2125838Open DOISearch in Google Scholar
N. Kour, S. Gupta, and S. Arora, “A vision-based clinical analysis for classification of knee osteoarthritis, Parkinson's disease and normal gait with severity based on k-nearest neighbour,” Expert Syst., vol. 39, no. 6, pp. 1–36, 2022, doi: 10.1111/exsy.12955.KourN.GuptaS.AroraS.“A vision-based clinical analysis for classification of knee osteoarthritis, Parkinson's disease and normal gait with severity based on k-nearest neighbour,”396136202210.1111/exsy.12955Open DOISearch in Google Scholar
X. Pei, H. Fan, and Y. Tang, “Temporal pyramid attention-based spatiotemporal fusion model for Parkinson's disease diagnosis from gait data,” IET Signal Process., vol. 15, no. 2, pp. 80–87, 2021, doi: 10.1049/sil2.12018.PeiX.FanH.TangY.“Temporal pyramid attention-based spatiotemporal fusion model for Parkinson's disease diagnosis from gait data,”1528087202110.1049/sil2.12018Open DOISearch in Google Scholar
Y. Xia, Z. M. Yao, Q. Ye, and N. Cheng, “A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson's Disease Characteristics,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 28, no. 1, pp. 42–51, 2020, doi: 10.1109/TNSRE.2019.2946194.XiaY.YaoZ. M.YeQ.ChengN.“A Dual-Modal Attention-Enhanced Deep Learning Network for Quantification of Parkinson's Disease Characteristics,”2814251202010.1109/TNSRE.2019.2946194Open DOISearch in Google Scholar
Y. Guo, J. Yang, Y. Liu, X. Chen, and G. Z. Yang, “Detection and assessment of Parkinson's disease based on gait analysis: A survey,” Front. Aging Neurosci., vol. 14, 2022, doi: 10.3389/fnagi.2022.916971.GuoY.YangJ.LiuY.ChenX.YangG. Z.“Detection and assessment of Parkinson's disease based on gait analysis: A survey,”14202210.3389/fnagi.2022.916971Open DOISearch in Google Scholar
A. A. Bhurane, S. Dhok, M. Sharma, R. Yuvaraj, M. Murugappan, and U. R. Acharya, “Diagnosis of Parkinson's disease from electroencephalography signals using linear and self-similarity features,” Expert Syst., vol. 39, no. 7, 2022, doi: 10.1111/exsy.12472.BhuraneA. A.DhokS.SharmaM.YuvarajR.MurugappanM.AcharyaU. R.“Diagnosis of Parkinson's disease from electroencephalography signals using linear and self-similarity features,”397202210.1111/exsy.12472Open DOISearch in Google Scholar
P. Chawla, S. B. Rana, H. Kaur, K. Singh, R. Yuvaraj, and M. Murugappan, “A decision support system for automated diagnosis of Parkinson's disease from EEG using FAWT and entropy features,” Biomed. Signal Process. Control, vol. 79, no. P1, p. 104116, 2023, doi: 10.1016/j.bspc.2022.104116.ChawlaP.RanaS. B.KaurH.SinghK.YuvarajR.MurugappanM.“A decision support system for automated diagnosis of Parkinson's disease from EEG using FAWT and entropy features,”79P1104116202310.1016/j.bspc.2022.104116Open DOISearch in Google Scholar
N. Wagh and Y. Varatharajah, “EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network,” Proc. Mach. Learn. Res., Mach. Learn. Heal., pp. 1–12, 2020, [Online]. Available: http://arxiv.org/abs/2011.12107WaghN.VaratharajahY.“EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network,”1122020[Online]. Available: http://arxiv.org/abs/2011.12107Search in Google Scholar
B. F. O. Coelho, A. B. R. Massaranduba, C. A. dos S. Souza, G. G. Viana, I. Brys, and R. P. Ramos, “Parkinson's disease effective biomarkers based on Hjorth features improved by machine learning,” Expert Syst. Appl., vol. 212, no. August 2022, p. 118772, 2023, doi: 10.1016/j.eswa.2022.118772.CoelhoB. F. O.MassarandubaA. B. R.SouzaC. A. dos S.VianaG. G.BrysI.RamosR. P.“Parkinson's disease effective biomarkers based on Hjorth features improved by machine learning,”212August2022118772202310.1016/j.eswa.2022.118772Open DOISearch in Google Scholar
S. K. Khare, V. Bajaj, and U. R. Acharya, “PDCNNet: An Automatic Framework for the Detection of Parkinson's Disease Using EEG Signals,” IEEE Sens. J., vol. 21, no. 15, pp. 17017–17024, 2021, doi: 10.1109/JSEN.2021.3080135.KhareS. K.BajajV.AcharyaU. R.“PDCNNet: An Automatic Framework for the Detection of Parkinson's Disease Using EEG Signals,”21151701717024202110.1109/JSEN.2021.3080135Open DOISearch in Google Scholar
D. Gupta, S. Sundaram, A. Khanna, A. Ella Hassanien, and V. H. C. de Albuquerque, “Improved diagnosis of Parkinson's disease using optimized crow search algorithm,” Comput. Electr. Eng., vol. 68, no. April, pp. 412–424, 2018, doi: 10.1016/j.compeleceng.2018.04.014.GuptaD.SundaramS.KhannaA.Ella HassanienA.de AlbuquerqueV. H. C.“Improved diagnosis of Parkinson's disease using optimized crow search algorithm,”68April412424201810.1016/j.compeleceng.2018.04.014Open DOISearch in Google Scholar
A. Naseer, M. Rani, S. Naz, M. I. Razzak, M. Imran, and G. Xu, “Refining Parkinson's neurological disorder identification through deep transfer learning,” Neural Comput. Appl., vol. 32, no. 3, pp. 839–854, 2020, doi: 10.1007/s00521-019-04069-0.NaseerA.RaniM.NazS.RazzakM. I.ImranM.XuG.“Refining Parkinson's neurological disorder identification through deep transfer learning,”323839854202010.1007/s00521-019-04069-0Open DOISearch in Google Scholar
B. Jin, Y. Qu, L. Zhang, and Z. Gao, “Diagnosing parkinson disease through facial expression recognition: Video analysis,” J. Med. Internet Res., vol. 22, no. 7, pp. 1–12, 2020, doi: 10.2196/18697.JinB.QuY.ZhangL.GaoZ.“Diagnosing parkinson disease through facial expression recognition: Video analysis,”227112202010.2196/18697Open DOISearch in Google Scholar
B. Sonawane and P. Sharma, “Review of automated emotion-based quantification of facial expression in Parkinson's patients,” Vis. Comput., vol. 37, no. 5, pp. 1151–1167, May 2021, doi: 10.1007/s00371-020-01859-9.SonawaneB.SharmaP.“Review of automated emotion-based quantification of facial expression in Parkinson's patients,”37511511167May202110.1007/s00371-020-01859-9Open DOISearch in Google Scholar
I. Adjabi, A. Ouahabi, A. Benzaoui, and A. Taleb-Ahmed, “Past, Present, and Future of Face Recognition: A Review,” Electronics, vol. 9, p. 1188, 2020, doi: 10.3390/electronics9081188.AdjabiI.OuahabiA.BenzaouiA.Taleb-AhmedA.“Past, Present, and Future of Face Recognition: A Review,”91188202010.3390/electronics9081188Open DOISearch in Google Scholar
D. Berg et al., “Prodromal Parkinson disease subtypes — key to understanding heterogeneity,” Nature Reviews Neurology, vol. 17, no. 6. Nature Research, pp. 349–361, Jun. 01, 2021. doi: 10.1038/s41582-021-00486-9.BergD.“Prodromal Parkinson disease subtypes — key to understanding heterogeneity,”176Nature Research,349361Jun.01202110.1038/s41582-021-00486-9Open DOISearch in Google Scholar
P. Chakrabarti, A. Mozhdehfarahbakhsh, S. Chitsazian, T. Chakrabarti, B. Kateb, and M. Nami, “An MRI-based Deep Learning Model to Predict Parkinson's Disease Stages,” 2021, doi: 10.1101/2021.02.19.21252081.ChakrabartiP.MozhdehfarahbakhshA.ChitsazianS.ChakrabartiT.KatebB.NamiM.202110.1101/2021.02.19.21252081Open DOISearch in Google Scholar
M. S. R. Sajal, M. T. Ehsan, R. Vaidyanathan, S. Wang, T. Aziz, and K. A. Al Mamun, “Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis,” Brain Informatics, vol. 7, no. 1, pp. 1–11, 2020, doi: 10.1186/s40708-020-00113-1.SajalM. S. R.EhsanM. T.VaidyanathanR.WangS.AzizT.Al MamunK. A.“Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis,”71111202010.1186/s40708-020-00113-1Open DOISearch in Google Scholar
M. Raza, M. Awais, N. Singh, M. Imran, and S. Hussain, “Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient,” IEEE J. Sel. Areas Commun., vol. 39, no. 2, pp. 593–602, 2021, doi: 10.1109/JSAC.2020.3021571.RazaM.AwaisM.SinghN.ImranM.HussainS.“Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient,”392593602202110.1109/JSAC.2020.3021571Open DOISearch in Google Scholar
M. Nilashi et al., “Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map,” Expert Syst. Appl., vol. 159, p. 113562, 2020, doi: 10.1016/j.eswa.2020.113562.NilashiM.“Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map,”159113562202010.1016/j.eswa.2020.113562Open DOISearch in Google Scholar
U. Kleinholdermann, M. Wullstein, and D. Pedrosa, “Prediction of motor Unified Parkinson's Disease Rating Scale scores in patients with Parkinson's disease using surface electromyography,” Clin. Neurophysiol., vol. 132, no. 7, pp. 1708–1713, 2021, doi: 10.1016/j.clinph.2021.01.031.KleinholdermannU.WullsteinM.PedrosaD.“Prediction of motor Unified Parkinson's Disease Rating Scale scores in patients with Parkinson's disease using surface electromyography,”132717081713202110.1016/j.clinph.2021.01.031Open DOISearch in Google Scholar
M. D. Hssayeni, J. Jimenez-Shahed, M. A. Burack, and B. Ghoraani, “Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III,” Biomed. Eng. Online, vol. 20, no. 1, pp. 1–20, 2021, doi: 10.1186/s12938-021-00872-w.HssayeniM. D.Jimenez-ShahedJ.BurackM. A.GhoraaniB.“Ensemble deep model for continuous estimation of Unified Parkinson's Disease Rating Scale III,”201120202110.1186/s12938-021-00872-wOpen DOISearch in Google Scholar
M. Lu et al., “Quantifying Parkinson's disease motor severity under uncertainty using MDS-UPDRS videos,” Med. Image Anal., vol. 73, Oct. 2021, doi: 10.1016/j.media.2021.102179.LuM.“Quantifying Parkinson's disease motor severity under uncertainty using MDS-UPDRS videos,”73Oct.202110.1016/j.media.2021.102179Open DOISearch in Google Scholar
H. Abujrida, E. Agu, and K. Pahlavan, “Machine learning-based motor assessment of Parkinson's disease using postural sway, gait and lifestyle features on crowd-sourced smartphone data,” Biomed. Phys. Eng. Express, vol. 6, no. 3, 2020, doi: 10.1088/2057-1976/ab39a8.AbujridaH.AguE.PahlavanK.“Machine learning-based motor assessment of Parkinson's disease using postural sway, gait and lifestyle features on crowd-sourced smartphone data,”63202010.1088/2057-1976/ab39a8Open DOISearch in Google Scholar
N. Li, F. Tian, X. Fan, Y. Zhu, H. Wang, and G. Dai, “Monitoring motor symptoms in Parkinson's disease via instrumenting daily artifacts with inertia sensors,” CCF Trans. Pervasive Comput. Interact., vol. 1, no. 2, pp. 100–113, 2019, doi: 10.1007/s42486-019-00008-z.LiN.TianF.FanX.ZhuY.WangH.DaiG.“Monitoring motor symptoms in Parkinson's disease via instrumenting daily artifacts with inertia sensors,”12100113201910.1007/s42486-019-00008-zOpen DOISearch in Google Scholar
D. Buongiorno, I. Bortone, G. D. Cascarano, G. F. Trotta, A. Brunetti, and V. Bevilacqua, “A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson's Disease,” BMC Med. Inform. Decis. Mak., vol. 19, no. 9, pp. 1–13, 2019, doi: 10.1186/s12911-019-0987-5.BuongiornoD.BortoneI.CascaranoG. D.TrottaG. F.BrunettiA.BevilacquaV.“A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson's Disease,”199113201910.1186/s12911-019-0987-5Open DOISearch in Google Scholar
H. Yoon and J. Li, “A Novel Positive Transfer Learning Approach for Telemonitoring of Parkinson's Disease,” IEEE Trans. Autom. Sci. Eng., vol. 16, no. 1, pp. 180–191, 2019, doi: 10.1109/TASE.2018.2874233.YoonH.LiJ.“A Novel Positive Transfer Learning Approach for Telemonitoring of Parkinson's Disease,”161180191201910.1109/TASE.2018.2874233Open DOISearch in Google Scholar
A. Grammatikopoulou, K. Dimitropoulos, S. Bostantjopoulou, Z. Katsarou, and N. Grammalidis, “Motion Analysis of Parkinson Diseased Patients using a Video Game Approach,” ACM Int. Conf. Proceeding Ser., pp. 523–527, 2019, doi: 10.1145/3316782.3322757.GrammatikopoulouA.DimitropoulosK.BostantjopoulouS.KatsarouZ.GrammalidisN.“Motion Analysis of Parkinson Diseased Patients using a Video Game Approach,”523527201910.1145/3316782.3322757Open DOISearch in Google Scholar
F. Kitsios, E. Papageorgiou, M. Kamariotou, N. A. Perifanis, and M. A. Talias, “Emotional intelligence with the gender perspective in health organizations managers,” Heliyon, vol. 8, no. 11, 2022, doi: 10.1016/j.heliyon.2022.e11488.KitsiosF.PapageorgiouE.KamariotouM.PerifanisN. A.TaliasM. A.“Emotional intelligence with the gender perspective in health organizations managers,”811202210.1016/j.heliyon.2022.e11488Open DOISearch in Google Scholar
P. J. O'Connor, A. Hill, M. Kaya, and B. Martin, “The measurement of emotional intelligence: A critical review of the literature and recommendations for researchers and practitioners,” Front. Psychol., vol. 10, no. MAY, pp. 0–1, 2019, doi: 10.3389/fpsyg.2019.01116.O'ConnorP. J.HillA.KayaM.MartinB.“The measurement of emotional intelligence: A critical review of the literature and recommendations for researchers and practitioners,”10MAY01201910.3389/fpsyg.2019.01116Open DOISearch in Google Scholar
P. A. Pérez-Díaz et al., “Invariance of the Trait Emotional Intelligence Construct Across Clinical Populations and Sociodemographic Variables,” Front. Psychol., vol. 13, no. April, pp. 1–10, 2022, doi: 10.3389/fpsyg.2022.796057.Pérez-DíazP. A.“Invariance of the Trait Emotional Intelligence Construct Across Clinical Populations and Sociodemographic Variables,”13April110202210.3389/fpsyg.2022.796057Open DOISearch in Google Scholar
R. Yuvaraj et al., “Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity,” Biomed. Signal Process. Control, vol. 14, no. 1, pp. 108–116, 2014, doi: 10.1016/j.bspc.2014.07.005.YuvarajR.“Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity,”141108116201410.1016/j.bspc.2014.07.005Open DOISearch in Google Scholar
R. Parameshwara, S. Narayana, M. Murugappan, R. Subramanian, I. Radwan, and R. Goecke, “Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals,” arX-iv:2202.12936v1, Feb. 2022, [Online]. Available: http://arxiv.org/abs/2202.12936ParameshwaraR.NarayanaS.MurugappanM.SubramanianR.RadwanI.GoeckeR.“Automated Parkinson's Disease Detection and Affective Analysis from Emotional EEG Signals,”Feb.2022[Online]. Available: http://arxiv.org/abs/2202.12936Search in Google Scholar
R. Yuvaraj et al., “On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing,” Behav. Brain Funct., vol. 10, no. 1, pp. 1–19, 2014, doi: 10.1186/1744-9081-10-12.YuvarajR.“On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing,”101119201410.1186/1744-9081-10-12Open DOISearch in Google Scholar
U. Anusri, G. Dhatchayani, Y. Princely Angelinal, and S. Kamalraj, “An Early Prediction of Parkinson's Disease Using Facial Emotional Recognition,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Jun. 2021. doi: 10.1088/1742-6596/1937/1/012058.AnusriU.DhatchayaniG.Princely AngelinalY.KamalrajS.“An Early Prediction of Parkinson's Disease Using Facial Emotional Recognition,”inIOP Publishing LtdJun.202110.1088/1742-6596/1937/1/012058Open DOISearch in Google Scholar
E. Pegolo, D. Volpe, A. Cucca, L. Ricciardi, and Z. Sawacha, “Quantitative Evaluation of Hypomimia in Parkinson's Disease: A Face Tracking Approach,” Sensors, vol. 22, no. 4, Feb. 2022, doi: 10.3390/s22041358.PegoloE.VolpeD.CuccaA.RicciardiL.SawachaZ.“Quantitative Evaluation of Hypomimia in Parkinson's Disease: A Face Tracking Approach,”224Feb.202210.3390/s22041358Open DOISearch in Google Scholar
K. Sechidis, R. Fusaroli, J. R. Orozco-Arroyave, D. Wolf, and Y. P. Zhang, “A machine learning perspective on the emotional content of Parkinsonian speech,” Artif. Intell. Med., vol. 115, no. February, p. 102061, 2021, doi: 10.1016/j.artmed.2021.102061.SechidisK.FusaroliR.Orozco-ArroyaveJ. R.WolfD.ZhangY. P.“A machine learning perspective on the emotional content of Parkinsonian speech,”115February102061202110.1016/j.artmed.2021.102061Open DOISearch in Google Scholar
S. Zhao, F. Rudzicz, L. G. Carvalho, C. Márquez-Chin, and S. Livingstone, “Automatic detection of expressed emotion in Parkinson's Disease,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., no. May, pp. 4813–4817, 2014, doi: 10.1109/ICASSP.2014.6854516.ZhaoS.RudziczF.CarvalhoL. G.Márquez-ChinC.LivingstoneS.“Automatic detection of expressed emotion in Parkinson's Disease,”May48134817201410.1109/ICASSP.2014.6854516Open DOISearch in Google Scholar
N. Murad and E. Melamud, “Global patterns of prognostic biomarkers across disease space,” Sci. Rep., vol. 12, no. 1, pp. 1–13, 2022, doi: 10.1038/s41598-022-25209-y.MuradN.MelamudE.“Global patterns of prognostic biomarkers across disease space,”121113202210.1038/s41598-022-25209-yOpen DOISearch in Google Scholar
K. D. Davis et al., “Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities,” Nat. Rev. Neurol., vol. 16, no. 7, pp. 381–400, 2020, doi: 10.1038/s41582-020-0362-2.DavisK. D.“Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities,”167381400202010.1038/s41582-020-0362-2Open DOISearch in Google Scholar
L. Max, “UCI Machine Learning Repository: Parkinsons Data Set,” UCI Machine Learning Repository: Parkinsons Data Set, 2008. https://archive.ics.uci.edu/ml/datasets/parkinsonsMaxL.“UCI Machine Learning Repository: Parkinsons Data Set,”2008https://archive.ics.uci.edu/ml/datasets/parkinsonsSearch in Google Scholar
V. Despotovic, T. Skovranek, and C. Schommer, “Speech Based Estimation of Parkinson's Disease Using Gaussian Processes and Automatic Relevance Determination,” Neurocomputing, vol. 401, pp. 173–181, 2020, doi: 10.1016/j.neucom.2020.03.058.DespotovicV.SkovranekT.SchommerC.“Speech Based Estimation of Parkinson's Disease Using Gaussian Processes and Automatic Relevance Determination,”401173181202010.1016/j.neucom.2020.03.058Open DOISearch in Google Scholar
J. F. Daneault et al., “Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson's disease,” Sci. Data, vol. 8, no. 1, pp. 1–13, 2021, doi: 10.1038/s41597-021-00830-0.DaneaultJ. F.“Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson's disease,”81113202110.1038/s41597-021-00830-0Open DOISearch in Google Scholar
D. Martinez-Ramirez, M. Rodriguez-Violante, and A. Ramirez-Zamora, “Gait in Parkinson's Disease: PhysioNet,” PhysioBank, vol. 2019. 2019. doi: 10.1155/2019/1962123.Martinez-RamirezD.Rodriguez-ViolanteM.Ramirez-ZamoraA.“Gait in Parkinson's Disease: PhysioNet,”2019201910.1155/2019/1962123Open DOISearch in Google Scholar
R. S. Nancy Noella, D. Gupta, and J. Priyadarshini, “Diagnosis of Parkinson's disease using Gait Dynamics and Images,” Procedia Comput. Sci., vol. 165, no. 2019, pp. 428–434, 2019, doi: 10.1016/j.procs.2020.01.002.Nancy NoellaR. S.GuptaD.PriyadarshiniJ.“Diagnosis of Parkinson's disease using Gait Dynamics and Images,”1652019428434201910.1016/j.procs.2020.01.002Open DOISearch in Google Scholar
A. Li and C. Li, “Detecting Parkinson's Disease through Gait Measures Using Machine Learning,” Diagnostics, vol. 12, no. 10, pp. 1–9, 2022, doi: 10.3390/diagnostics12102404.LiA.LiC.“Detecting Parkinson's Disease through Gait Measures Using Machine Learning,”121019202210.3390/diagnostics12102404Open DOISearch in Google Scholar
D. Vimalajeewa, E. Mcdonald, M. Tung, and B. Vidakovic, “Parkinson's Disease Diagnosis with Gait Characteristics Extracted Using Wavelet Transforms,” TechRxiv, pp. 0–9, 2022, doi: 10.36227/techrxiv.21287547.v1.VimalajeewaD.McdonaldE.TungM.VidakovicB.“Parkinson's Disease Diagnosis with Gait Characteristics Extracted Using Wavelet Transforms,”09202210.36227/techrxiv.21287547.v1Open DOISearch in Google Scholar
M. B. Makarious et al., “Multi-modality machine learning predicting Parkinson's disease,” npj Park. Dis., vol. 8, no. 1, 2022, doi: 10.1038/s41531-022-00288-w.MakariousM. B.“Multi-modality machine learning predicting Parkinson's disease,”81202210.1038/s41531-022-00288-wOpen DOISearch in Google Scholar
K. Marek et al., “The Parkinson Progression Marker Initiative (PPMI),” Prog. Neurobiol., vol. 95, no. 80, pp. 678–687, 2011, doi: 10.1016/j.pneurobio.2011.09.005.The.MarekK.“The Parkinson Progression Marker Initiative (PPMI),”9580678687201110.1016/j.pneurobio.2011.09.005.TheOpen DOISearch in Google Scholar
A. Tsanas, M. Little, P. McSharry, and L. Ramig, “UCI Machine Learning Repository: Parkinsons Telemonitoring Data Set,” UCI Machine Learning Repository, 2009. https://archive.ics.uci.edu/ml/datasets/Parkinsons+TelemonitoringTsanasA.LittleM.McSharryP.RamigL.“UCI Machine Learning Repository: Parkinsons Telemonitoring Data Set,”2009https://archive.ics.uci.edu/ml/datasets/Parkinsons+TelemonitoringSearch in Google Scholar
M. Nilashi, O. Ibrahim, S. Samad, H. Ahmadi, L. Shahmoradi, and E. Akbari, “An analytical method for measuring the Parkinson's disease progression: A case on a Parkinson's telemonitoring dataset,” Meas. J. Int. Meas. Confed., vol. 136, pp. 545–557, 2019, doi: 10.1016/j.measurement.2019.01.014.NilashiM.IbrahimO.SamadS.AhmadiH.ShahmoradiL.AkbariE.“An analytical method for measuring the Parkinson's disease progression: A case on a Parkinson's telemonitoring dataset,”136545557201910.1016/j.measurement.2019.01.014Open DOISearch in Google Scholar
D. Roggen, M. Plotnik, and J. Hausdorff, “Daphnet Freezing of Gait Data Set,” UCI Machine Learning Repository, 2013. https://archive.ics.uci.edu/ml/datasets/Daphnet+Freezing+of+Gait%0Ahttps://archive.ics.uci.edu/ml/datasets/Daphnet+Freezing+of+Gait#RoggenD.PlotnikM.HausdorffJ.“Daphnet Freezing of Gait Data Set,”2013https://archive.ics.uci.edu/ml/datasets/Daphnet+Freezing+of+Gait%0Ahttps://archive.ics.uci.edu/ml/datasets/Daphnet+Freezing+of+Gait#Search in Google Scholar
N. Kleanthous, A. J. Hussain, W. Khan, and P. Liatsis, “A new machine learning based approach to predict Freezing of Gait,” Pattern Recognit. Lett., vol. 140, pp. 119–126, 2020, doi: 10.1016/j.patrec.2020.09.011.KleanthousN.HussainA. J.KhanW.LiatsisP.“A new machine learning based approach to predict Freezing of Gait,”140119126202010.1016/j.patrec.2020.09.011Open DOISearch in Google Scholar
L. Naranjo, C. J. Pérez, J. Martín, and Y. Campos-Roca, “A two-stage variable selection and classification approach for Parkinson's disease detection by using voice recording replications,” Comput. Methods Programs Biomed., vol. 142, pp. 147–156, 2017, doi: 10.1016/j.cmpb.2017.02.019.NaranjoL.PérezC. J.MartínJ.Campos-RocaY.“A two-stage variable selection and classification approach for Parkinson's disease detection by using voice recording replications,”142147156201710.1016/j.cmpb.2017.02.019Open DOISearch in Google Scholar
J. Dhar, “An adaptive intelligent diagnostic system to predict early stage of parkinson's disease using two-stage dimension reduction with genetically optimized lightgbm algorithm,” Neural Comput. Appl., vol. 34, no. 6, pp. 4567–4593, 2022, doi: 10.1007/s00521-021-06612-4.DharJ.“An adaptive intelligent diagnostic system to predict early stage of parkinson's disease using two-stage dimension reduction with genetically optimized lightgbm algorithm,”34645674593202210.1007/s00521-021-06612-4Open DOISearch in Google Scholar
B. Erdogdu Sakar et al., “UCI Machine Learning Repository: Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set,” IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 4. pp. 828–834, 2013. [Online]. Available: https://archive.ics.uci.edu/ml/Parkinson+Speech+Datasett+with++Multiple+Types+of+Sound+RecordingsErdogdu SakarB.“UCI Machine Learning Repository: Parkinson Speech Dataset with Multiple Types of Sound Recordings Data Set,”1748288342013[Online]. Available: https://archive.ics.uci.edu/ml/Parkinson+Speech+Datasett+with++Multiple+Types+of+Sound+RecordingsSearch in Google Scholar
S. R. Sharma, B. Singh, and M. Kaur, “Classification of Parkinson disease using binary Rao optimization algorithms,” Expert Syst., vol. 38, no. 4, pp. 1–16, 2021, doi: 10.1111/exsy.12674.SharmaS. R.SinghB.KaurM.“Classification of Parkinson disease using binary Rao optimization algorithms,”384116202110.1111/exsy.12674Open DOISearch in Google Scholar
C. O. Sakar et al., “A comparative analysis of speech signal processing algorithms for Parkinson's disease classification and the use of the tunable Q-factor wavelet transform,” Appl. Soft Comput. J., vol. 74, pp. 255–263, 2019, doi: 10.1016/j.asoc.2018.10.022.SakarC. O.“A comparative analysis of speech signal processing algorithms for Parkinson's disease classification and the use of the tunable Q-factor wavelet transform,”74255263201910.1016/j.asoc.2018.10.022Open DOISearch in Google Scholar
H. Gunduz, “Deep Learning-Based Parkinson's Disease Classification Using Vocal Feature Sets,” IEEE Access, vol. 7, pp. 115540–115551, 2019, doi: 10.1109/ACCESS.2019.2936564.GunduzH.“Deep Learning-Based Parkinson's Disease Classification Using Vocal Feature Sets,”7115540115551201910.1109/ACCESS.2019.2936564Open DOISearch in Google Scholar
Okan Sakar, G. Serbes, and A. Gunduz, “UCI Machine Learning Repository: Parkinson's Disease Classification Data Set,” UCI Machine Learning Repository. p. 1, 2018. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+ClassificationSakarOkanSerbesG.GunduzA.“UCI Machine Learning Repository: Parkinson's Disease Classification Data Set,”12018[Online]. Available: https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+ClassificationSearch in Google Scholar
R. Lamba, T. Gulati, and A. Jain, “A Hybrid Feature Selection Approach for Parkinson's Detection Based on Mutual Information Gain and Recursive Feature Elimination,” Arab. J. Sci. Eng., vol. 47, no. 8, pp. 10263–10276, 2022, doi: 10.1007/s13369-021-06544-0.LambaR.GulatiT.JainA.“A Hybrid Feature Selection Approach for Parkinson's Detection Based on Mutual Information Gain and Recursive Feature Elimination,”4781026310276202210.1007/s13369-021-06544-0Open DOISearch in Google Scholar
M. E. Isenkul and B. Erdogdu Sakar, “UCI Machine Learning Repository: Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet Data Set,” Dataset. 2017. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Parkinson+Disease+Spiral+Drawings+Using+Digitized+Graphics+tablet%0Ahttps://archive.ics.uci.edu/ml/datasets/Parkinson+Disease+Spiral+Drawings+Using+Digitized+Graphics+TabletIsenkulM. E.Erdogdu SakarB.“UCI Machine Learning Repository: Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet Data Set,”2017[Online]. Available: https://archive.ics.uci.edu/ml/datasets/Parkinson+Disease+Spiral+Drawings+Using+Digitized+Graphics+tablet%0Ahttps://archive.ics.uci.edu/ml/datasets/Parkinson+Disease+Spiral+Drawings+Using+Digitized+Graphics+TabletSearch in Google Scholar
M. Gil-Martín, J. M. Montero, and R. San-Segundo, “Parkinson's disease detection from drawing movements using convolutional neural networks,” Electron., vol. 8, no. 8, 2019, doi: 10.3390/electronics8080907.Gil-MartínM.MonteroJ. M.San-SegundoR.“Parkinson's disease detection from drawing movements using convolutional neural networks,”88201910.3390/electronics8080907Open DOISearch in Google Scholar
C. R. Pereira, S. A. T. Weber, C. Hook, G. H. Rosa, and J. P. Papa, “NewHandPD dataset,” Deep Learning-aided Parkinson's Disease Diagnosis from Handwritten Dynamics, 2016. https://wwwp.fc.unesp.br/~papa/pub/datasets/Handpd/PereiraC. R.WeberS. A. T.HookC.RosaG. H.PapaJ. P.“NewHandPD dataset,”2016https://wwwp.fc.unesp.br/~papa/pub/datasets/Handpd/Search in Google Scholar
S. Xu and Z. Pan, “A novel ensemble of random forest for assisting diagnosis of Parkinson's disease on small handwritten dynamics dataset,” Int. J. Med. Inform., vol. 144, p. 104283, 2020, doi: 10.1016/j.ijmedinf.2020.104283.XuS.PanZ.“A novel ensemble of random forest for assisting diagnosis of Parkinson's disease on small handwritten dynamics dataset,”144104283202010.1016/j.ijmedinf.2020.104283Open DOISearch in Google Scholar
M. Hireš, M. Gazda, P. Drotár, N. D. Pah, M. A. Motin, and D. K. Kumar, “Convolutional neural network ensemble for Parkinson's disease detection from voice recordings,” Comput. Biol. Med., vol. 141, no. August 2021, 2022, doi: 10.1016/j.compbiomed.2021.105021.HirešM.GazdaM.DrotárP.PahN. D.MotinM. A.KumarD. K.“Convolutional neural network ensemble for Parkinson's disease detection from voice recordings,”141no. August 2021,202210.1016/j.compbiomed.2021.105021Open DOISearch in Google Scholar
F. Amato, L. Borzì, G. Olmo, J. Rafael, and O. Arroyave, “An algorithm for Parkinson's disease speech classification based on isolated words analysis,” Heal. Inf. Sci. Syst., vol. 9, no. 1, pp. 1–15, 2021, doi: 10.1007/s13755-021-00162-8.AmatoF.BorzìL.OlmoG.RafaelJ.ArroyaveO.“An algorithm for Parkinson's disease speech classification based on isolated words analysis,”91115202110.1007/s13755-021-00162-8Open DOISearch in Google Scholar
Thanos Tagaris, “The NTUA Parkinson's Dataset,” Artificial Intelligence and Learning Systems Laboratory, 2020. https://github.com/ails-lab/ntua-parkinson-dataset (accessed Jan. 06, 2023).Thanos Tagaris“The NTUA Parkinson's Dataset,”2020https://github.com/ails-lab/ntua-parkinson-dataset (accessed Jan. 06, 2023).Search in Google Scholar
L. Moro-Velazquez et al., “A forced gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing,” Biomed. Signal Process. Control, vol. 48, pp. 205–220, 2019, doi: 10.1016/j.bspc.2018.10.020.Moro-VelazquezL.“A forced gaussians based methodology for the differential evaluation of Parkinson's Disease by means of speech processing,”48205220201910.1016/j.bspc.2018.10.020Open DOISearch in Google Scholar
L. Moro-Velazquez, J. A. Gomez-Garcia, J. D. Arias-Londoño, N. Dehak, and J. I. Godino-Llorente, “Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects,” Biomed. Signal Process. Control, vol. 66, no. May 2020, 2021, doi: 10.1016/j.bspc.2021.102418.Moro-VelazquezL.Gomez-GarciaJ. A.Arias-LondoñoJ. D.DehakN.Godino-LlorenteJ. I.“Advances in Parkinson's Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects,”66no. May 2020,202110.1016/j.bspc.2021.102418Open DOISearch in Google Scholar
L. Moro-Velazquez et al., “Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease,” Sci. Rep., vol. 9, no. 1, pp. 1–16, 2019, doi: 10.1038/s41598-019-55271-y.Moro-VelazquezL.“Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson's Disease,”91116201910.1038/s41598-019-55271-yOpen DOISearch in Google Scholar
K. W. Russell A. Poldrack, Krzysztof J. Gorgolewski et al., “Open fMRI – Sharing brain MRI data,” The Open Source Imaging Initiative, 2016. https://www.opensourceimaging.org/project/open-fmri-brain-mri-data-sharing-platform/RussellK. W.PoldrackA.GorgolewskiKrzysztof J.“Open fMRI – Sharing brain MRI data,”2016https://www.opensourceimaging.org/project/open-fmri-brain-mri-data-sharing-platform/Search in Google Scholar
R. A. Poldrack and K. J. Gorgolewski, “OpenfMRI: Open sharing of task fMRI data,” Neuroimage, vol. 144, pp. 259–261, 2017, doi: 10.1016/j.neuroimage.2015.05.073.PoldrackR. A.GorgolewskiK. J.“OpenfMRI: Open sharing of task fMRI data,”144259261201710.1016/j.neuroimage.2015.05.073Open DOISearch in Google Scholar
K. D. C. W. S. M. X. C. G. T. S. S. E. R. D. G. S. Jamie L. Adams, “PD-BioStampRC21: Parkinson's Disease Accelerometry Dataset from Five Wearable Sensor Study,” IEEE Dataport. 2020.K. D. C. W. S. M. X. C. G. T. S. S. E. R. D. G. S. Jamie L. Adams“PD-BioStampRC21: Parkinson's Disease Accelerometry Dataset from Five Wearable Sensor Study,”2020Search in Google Scholar
J. L. Adams et al., “A real-world study of wearable sensors in Parkinson's disease,” npj Park. Dis., vol. 7, no. 1, pp. 1–8, 2021, doi: 10.1038/s41531-021-00248-w.AdamsJ. L.“A real-world study of wearable sensors in Parkinson's disease,”7118202110.1038/s41531-021-00248-wOpen DOISearch in Google Scholar
G. Dimauro, V. Di Nicola, V. Bevilacqua, D. Caivano, and F. Girardi, “Assessment of speech intelligibility in Parkinson's disease using a speech-to-text system,” IEEE Access, vol. 5, pp. 22199–22208, 2017, doi: 10.1109/ACCESS.2017.2762475.DimauroG.Di NicolaV.BevilacquaV.CaivanoD.GirardiF.“Assessment of speech intelligibility in Parkinson's disease using a speech-to-text system,”52219922208201710.1109/ACCESS.2017.2762475Open DOISearch in Google Scholar
GiovanniDimauro, “Italian Parkinson's Voice and Speech,” IEEE DataPort, 2022. https://ieee-dataport.org/open-access/italian-parkinsons-voice-and-speechGiovanniDimauro“Italian Parkinson's Voice and Speech,”2022https://ieee-dataport.org/open-access/italian-parkinsons-voice-and-speechSearch in Google Scholar
P. Klinton Amaladass, M. S. P. Subathra, S. Jeba Priya, and M. Sivakumar, “Enhanced Local Pattern Transformation Based Feature Extraction for Identification of Parkinson's Disease Using Gait Signals,” SN Comput. Sci., vol. 4, no. 2, 2023, doi: 10.1007/s42979-022-01603-1.Klinton AmaladassP.SubathraM. S. P.Jeba PriyaS.SivakumarM.“Enhanced Local Pattern Transformation Based Feature Extraction for Identification of Parkinson's Disease Using Gait Signals,”42202310.1007/s42979-022-01603-1Open DOISearch in Google Scholar
D. J. M. Hausdorff, “Gait in Parkinson's Disease,” PhysioNet, 2008.HausdorffD. J. M.“Gait in Parkinson's Disease,”2008Search in Google Scholar
M. Arafe, “GitHub - mohanadarafe_Neurocon,” NEUROCON project, UEFISCDI, 2012. https://github.com/mohanadarafe/NeuroconArafeM.“GitHub - mohanadarafe_Neurocon,”2012https://github.com/mohanadarafe/NeuroconSearch in Google Scholar
L. Badea, M. Onu, T. Wu, A. Roceanu, and O. Bajenaru, “Exploring the reproducibility of functional connectivity alterations in Parkinson's disease,” PLoS One, vol. 12, no. 11, pp. 1–21, 2017, doi: 10.1371/journal.pone.0188196.BadeaL.OnuM.WuT.RoceanuA.BajenaruO.“Exploring the reproducibility of functional connectivity alterations in Parkinson's disease,”1211121201710.1371/journal.pone.0188196Open DOISearch in Google Scholar
A. Ibrahim, Y. Zhou, M. E. Jenkins, M. D. Naish, and A. L. Trejos, “Parkinson's Tremor Onset Detection and Active Tremor Classification Using a Multilayer Perceptron,” Can. Conf. Electr. Comput. Eng., vol. 2020-Augus, pp. 4–7, 2020, doi: 10.1109/CCECE47787.2020.9255672.IbrahimA.ZhouY.JenkinsM. E.NaishM. D.TrejosA. L.“Parkinson's Tremor Onset Detection and Active Tremor Classification Using a Multilayer Perceptron,”vol. 2020-Augus,47202010.1109/CCECE47787.2020.9255672Open DOISearch in Google Scholar
T. Tuncer, S. Dogan, and U. R. Acharya, “Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels,” Biocybern. Biomed. Eng., pp. 1–11, 2019, doi: https://doi.org/10.1016/j.bbe.2019.05.006.TuncerT.DoganS.AcharyaU. R.“Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels,”1112019doi: https://doi.org/10.1016/j.bbe.2019.05.006.Search in Google Scholar
S. Lee, R. Hussein, and M. J. Mckeown, “A Deep Convolutional-Recurrent Neural Network Architecture for Parkinson's Disease EEG Classification,” IEEE Glob. Conf. Signal Inf. Process., pp. 14–17, 2019.LeeS.HusseinR.MckeownM. J.“A Deep Convolutional-Recurrent Neural Network Architecture for Parkinson's Disease EEG Classification,”14172019Search in Google Scholar
D. Iakovakis et al., “Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks,” Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 3535–3538, 2019, doi: 10.1109/EMBC.2019.8857211.IakovakisD.“Early Parkinson's Disease Detection via Touchscreen Typing Analysis using Convolutional Neural Networks,”35353538201910.1109/EMBC.2019.8857211Open DOISearch in Google Scholar
D. Iakovakis, S. Hadjidimitriou, V. Charisis, S. Bostantzopoulou, Z. Katsarou, and L. J. Hadjileontiadis, “Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson's disease,” Sci. Rep., vol. 8, no. 1, pp. 1–13, 2018, doi: 10.1038/s41598-018-25999-0.IakovakisD.HadjidimitriouS.CharisisV.BostantzopoulouS.KatsarouZ.HadjileontiadisL. J.“Touchscreen typing-pattern analysis for detecting fine motor skills decline in early-stage Parkinson's disease,”81113201810.1038/s41598-018-25999-0Open DOISearch in Google Scholar
X. Shi, T. Wang, L. Wang, H. Liu, and N. Yan, “Hybrid convolutional recurrent neural networks outperform CNN and RNN in Task-state EEG detection for parkinson's disease,” 2019 Asia-Pacific Signal Inf. Process. Assoc. Annu. Summit Conf. APSIPA ASC 2019, no. November, pp. 939–944, 2019, doi: 10.1109/APSIPAASC47483.2019.9023190.ShiX.WangT.WangL.LiuH.YanN.“Hybrid convolutional recurrent neural networks outperform CNN and RNN in Task-state EEG detection for parkinson's disease,”November939944201910.1109/APSIPAASC47483.2019.9023190Open DOISearch in Google Scholar
P. Khojasteh, R. Viswanathan, B. Aliahmad, S. Ragnav, P. Zham, and D. K. Kumar, “Parkinson's disease diagnosis based on multivariate deep features of speech signal,” 2018 IEEE Life Sci. Conf. LSC 2018, pp. 187–190, 2018, doi: 10.1109/LSC.2018.8572136.KhojastehP.ViswanathanR.AliahmadB.RagnavS.ZhamP.KumarD. K.“Parkinson's disease diagnosis based on multivariate deep features of speech signal,”187190201810.1109/LSC.2018.8572136Open DOISearch in Google Scholar
T. A. A. Abdullah, Z. Mohd Soperi Mohd, and W. Ali, “A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions,” Symmetry 2021, vol. 13, pp. 1–28, 2021.AbdullahT. A. A.Mohd Soperi MohdZ.AliW.“A Review of Interpretable ML in Healthcare: Taxonomy, Applications, Challenges, and Future Directions,”131282021Search in Google Scholar
D. Stripelis et al., “Secure neuroimaging analysis using federated learning with homomorphic encryption,” SPIE Proc., vol. 12088, p. 44, 2021, doi: 10.1117/12.2606256.StripelisD.“Secure neuroimaging analysis using federated learning with homomorphic encryption,”1208844202110.1117/12.2606256Open DOISearch in Google Scholar
Prayitno et al., “A systematic review of federated learning in the healthcare area: From the perspective of data properties and applications,” Appl. Sci., vol. 11, no. 23, 2021, doi: 10.3390/app112311191.Prayitno“A systematic review of federated learning in the healthcare area: From the perspective of data properties and applications,”1123202110.3390/app112311191Open DOISearch in Google Scholar
S. I. Manzoor, S. Jain, and Y. Singh, “Federated Learning based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks,” IEEE Access, vol. 4, pp. 1–31, 2023.ManzoorS. I.JainS.SinghY.“Federated Learning based Privacy Ensured Sensor Communication in IoT Networks: A Taxonomy, Threats and Attacks,”41312023Search in Google Scholar
G. Mattavelli et al., “Facial expressions recognition and discrimination in Parkinson's disease,” J. Neuropsychol., vol. 15, no. 1, pp. 46–68, 2021, doi: 10.1111/jnp.12209.MattavelliG.“Facial expressions recognition and discrimination in Parkinson's disease,”1514668202110.1111/jnp.12209Open DOISearch in Google Scholar
Y. Liu et al., “Vision-Based Method for Automatic Quantification of Parkinsonian Bradykinesia,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 27, no. 10, pp. 1952–1961, 2019, doi: 10.1109/TNSRE.2019.2939596.LiuY.“Vision-Based Method for Automatic Quantification of Parkinsonian Bradykinesia,”271019521961201910.1109/TNSRE.2019.2939596Open DOISearch in Google Scholar
I. G. Tsoulos, G. Mitsi, A. Stavrakoudis, and S. Papapetropoulos, “Application of machine learning in a parkinson's disease digital biomarker dataset using Neural Network Construction (NNC) methodology discriminates patient motor status,” Front. ICT, vol. 6, no. MAY, pp. 1–7, 2019, doi: 10.3389/fict.2019.00010.TsoulosI. G.MitsiG.StavrakoudisA.PapapetropoulosS.“Application of machine learning in a parkinson's disease digital biomarker dataset using Neural Network Construction (NNC) methodology discriminates patient motor status,”6MAY17201910.3389/fict.2019.00010Open DOISearch in Google Scholar
S. T. and N. K. P. Bhattacharya, S. Tanwar, U. Bodkhe, “BinDaaS_Blockchain-Based Deep-Learning as-a-Service in Healthcare 4,” IEEE Trans. Netw. Sci. Eng., vol. 8, no. 2, pp. 1242–1255, 2021, doi: 10.1109/TNSE.2019.2961932.S. T. and N. K. P. BhattacharyaTanwarS.BodkheU.“BinDaaS_Blockchain-Based Deep-Learning as-a-Service in Healthcare 4,”8212421255202110.1109/TNSE.2019.2961932Open DOISearch in Google Scholar