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Parkinson’s disease (võtmesõna)

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Kirjeid leitud 7
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Deep CNN based classification of the Archimedes spiral drawing tests to support diagnostics of the Parkinson’s diseaseNõmm, Sven; Zarembo, Sergei; Medijainen, Kadri; Taba, Pille; Toomela, AaroIFAC-Papers OnLine2020 / p. 260-264 https://doi.org/10.1016/j.ifacol.2021.04.185 Conference proceedings at Scopus Article at Scopus Article at WOS

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An efficient neural network for the diagnosis of Parkinson’s disease using dynamic handwriting analysisWang, Xuechao; Nõmm, Sven; Huang, Junqing; Chatzakou, Marianna; Ruzhansky, MichaelExtended Abstracts MWCAPDE 2023 : Methusalem Workshop on Classical Analysis and Partial Differential Equations2024 / p. 97 - 104 https://doi.org/10.1007/978-3-031-41665-1_11 Article Collection metrics at Scopus Article at Scopus Article at WOS

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3

artikkel kogumikus

Mathematical and physical modelling of the dynamic electrical impedance both of a healthy neuron and one affected by Parkinson’s diseaseGiannoukos, GeorgiosAdvances in applied information science : proceedings of the 12th WSEAS International Conference on Applied Informatics and Communications (AIC '12) : proceedings of the 5th WSEAS International Conference on Biomedical Electronics and Biomedical Informatics (BEBI '12) : Istanbul, Turkey, August 21-23, 20122012 / p. 79-84 : ill https://www.researchgate.net/publication/264128963_Mathematical_and_Physical_Modelling_of_the_Dynamic_Electrical_Impedance_of_a_Neuron

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artikkel ajakirjas

Mathematical and physical modelling of the dynamic electrical impedance of a neuronGiannoukos, Georgios; Min, MartInternational journal of circuits, systems and signal processing2012 / p. 359-366 : ill https://www.researchgate.net/publication/264128963_Mathematical_and_Physical_Modelling_of_the_Dynamic_Electrical_Impedance_of_a_Neuron

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5

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Parkinson's disease diagnostics based on the analysis of digital sentence writing testNetšunajev, Aleksei; Nõmm, Sven; Toomela, Aaro; Medijainen, Kadri; Taba, PilleVietnam journal of computer science2021 / p. 493-512 https://doi.org/10.1142/S2196888821500238 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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6

artikkel ajakirjas

Topological fingerprints as an aid in finding structural patterns for LRRK2 inhibitionKahn, Iiris; Lomaka, Andre; Karelson, MatiMolecular informatics2014 / p. 269-275 : ill

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7

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Tremor-related feature engineering for machine learning based Parkinson’s disease diagnosticsValla, Elli; Nõmm, Sven; Medijainen, Kadri; Taba, Pille; Toomela, AaroBiomedical signal processing and control2022 / art. 103551 https://doi.org/10.1016/j.bspc.2022.103551 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

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Kirjeid leitud 7, kuvan 1 - 7

1. Parkinson’s disease
2. Parkinson’s disease machine learning
3. Parkinson's disease
4. Parkinson disease
5. Parkinson’s diseas
6. Alzheimer’s disease
7. Alzheimer's disease
8. anti-GBM disease
9. cardiovascular disease
10. celiac disease
11. cerebrovascular disease
12. chronic kidney disease
13. chronic obstructive pulmonary disease
14. coronary artery disease
15. coronary artery disease (CAD)
16. coronary disease
17. coronary heart disease
18. disease
19. disease diagnosis
20. disease model
21. disease models
22. disease progression
23. disease resistance
24. end stage renal disease
25. end-stage kidney disease
26. end-stage renal disease
27. garment workers and patients with occupational disease
28. genetic predisposition to disease
29. heart disease risk factors
30. Huntington’s disease
31. infectious disease
32. in-transit disease
33. late blight disease
34. loco-regionally metastatic disease
35. lyme disease
36. mechanisms of disease
37. Menkes disease
38. molecular bases of disease
39. NAFLD (non-alcoholic fatty liver disease)
40. neurodegenerative disease
41. neuromuscular disease
42. non-alcoholic fatty liver disease
43. oat striate and red disease
44. physical overload disease
45. preclinical Alzheimer disease
46. renal disease
47. stable coronary heart disease
48. subclinical cardiovascular disease
49. Wilson disease
50. Wilson’s disease
51. von Willebrand disease

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