magistrsko delo
Matjaž Zupanič (Author), Jure Žabkar (Mentor), Dejan Georgiev (Co-mentor)

Abstract

Parkinsonova bolezen je kronična nevrodegenerativna bolezen, ki močno poslabša kvaliteto življenja pacientov. Čakalne vrste za nevrologa so danes precej dolge, pacienti pa so v tem obdobju brez ustrezne terapije s katero bi si olajšali simptome. Zato smo razvili avtomatsko metodo za določanje stopnje motorične prizadetosti oziroma bradikinezije Parkinsonove bolezni na podlagi testa tapkanja in s tem omogočili hitrejšo postavitev diagnoze. Zbrali smo 183 video posnetkov tapkanja, posnetih kar s pametnim telefonom v vsakdanjem okolju. Videe je v 5 razredov lestvice MDS-UPDRS ocenil nevrolog. Za prepoznavo roke smo uporabili MediaPipe Hand, ki nam kot rezultat vrne časovno vrsto skeleta roke. Za klasifikacijo smo ubrali dva različna pristopa. Prvič smo iz časovne vrste skeleta roke sami sestavili značilke, enkrat strogo po lestvici MDS-UPDRS, drugič pa se te nismo strogo držali. Te značilke smo nato uporabili v klasifikatorjih in z večplastnim perceptronom dosegli 61 \% točnost in 0,62 F1 vrednost. V drugem pristopu smo časovno vrsto razdalj med palcem in kazalcem uporabili neposredno v polnem konvolucijskem nevronskem omrežju in dosegli 77 \% točnost in 0,75 F1 vrednost. Izdelali smo še orodje za vizualizacijo tapkanja in izpis ključnih podatkov.

Keywords

klasifikacija;test tapkanja s prsti;bolniki s Parkinsonovo boleznijo;magisteriji;

Data

Language: Slovenian
Year of publishing:
Typology: 2.09 - Master's Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [M. Zupanič]
UDC: 004.85:616.858(043.2)
COBISS: 201613827 Link will open in a new window
Views: 110
Downloads: 35
Average score: 0 (0 votes)
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Other data

Secondary language: English
Secondary title: Automatic assessment of finger tapping in patients with Parkinson's disease
Secondary abstract: Parkinson's disease is a chronic neurodegenerative disorder that severely impairs patients' quality of life. Currently, the waiting lists for a neurologist are quite long, and during this period, patients are without adequate therapy to alleviate their symptoms. Therefore, we have developed an automatic method to determine the level of motor impairment or bradykinesia of Parkinson's disease based on a tapping test, thus enabling faster diagnosis. We collected 183 tapping videos recorded with a smartphone in everyday environments, which were assessed by a neurologist into 5 classes of the MDS-UPDRS scale. For hand detection we used MediaPipe Hand, which returns a time series of the hand skeleton. For classification, we took two different approaches. First, we constructed features from the hand skeleton time series, once strictly following the MDS-UPDRS scale, and another time not strictly adhering to it. These features were then used in classifiers and achieved 61 \% accuracy and 0,62 F1 score using a multi-layer perceptron. In the second approach, we used the time series of thumb-pointer distances directly in a fully convolutional neural network achieving 77 \% accuracy and 0,75 F1 score. We also created a tool for visualizing tapping and displaying key data.
Secondary keywords: classification;Parkinson’s disease;finger tapping test;machine learning;computer science;master's degree;Parkinsonova bolezen;Strojno učenje;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS): Master's thesis/paper
Study programme: 1000471
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 81 str.
ID: 24398918