Merjenje bradikinezije za odkrivanje Parkinsonove bolezni

diplomsko delo

Edi Čebokli (Author), Jure Žabkar (Mentor)

Abstract

Ovrednotenje nemotoričnih in motoričnih simptomov povezanih s Parkinsonovo boleznijo je po navadi opravljeno z UPDRS. Opisi ocen za posamezne točke so opisani kvalitativno, zato je ocenjevanje odvisno od izkušenj nevrologa. Posledično se ocene med nevrologi razlikujejo. Problem subjektivnosti ocenjevanja tapkanja s prsti, ki je eden izmed motoričnih preizkusov, poskušamo rešiti z izgradnjo aplikacije, ki je zmožna s kamero paciente posneti in analizirati posnetke ter izračunati oceno UPDRS. V prvem koraku smo zaznali dotike in konice palcev in kazalcev, nato pa posneli skupino ljudi s Parkinsonovo boleznijo in skupino zdravih ljudi. Iz razdalj med konicami smo definirali več atributov in z njimi zgradili model, ki najboljše loči primere z različnimi ocenami UPDRS. Ugotovili smo, da model primere precej uspešno loči, vendar bomo za klasifikacijo morali zbrati več posnetkov.

Keywords

bradikinezija;Enotna ocenjevalna lestvica Parkinsonove bolezni;UPDRS;Parkinsonova bolezen;tapkanje s prsti;računalništvo in informatika;univerzitetni študij;diplomske naloge;

Data

Language:	Slovenian
Year of publishing:	2019
Typology:	2.11 - Undergraduate Thesis
Organization:	UL FRI - Faculty of Computer and Information Science
Publisher:	[E. Čebokli]
UDC:	004.8:616.858(043.2)
COBISS:	1538336451
Views:	902
Downloads:	233
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Measurement of bradykinesia for the detection of Parkinson’s disease
Secondary abstract:	Non-motor and motor symptoms that are linked with Parkinson's disease are often clinically assessed by neurologists using the Unified Parkinson's Disease Rating Scale (UPDRS). UPDRS scores are described as qualitative and are dependent on neurologist's experience. Consequently, clinical scores may differ among neurologists. We develop an application for measuring bradykinesia in the UPDRS finger tapping task, with which patients are recorded with a depth camera and by analyzing videos, given a more objective rating. In the first stage, we detect touches and thumb's and pointer's fingertips. Following, we calculate distances between the fingertips. From distances we then extract finger tapping features. We record a group of people with Parkinson's disease and a control group. Furthermore, we define a model that best separates instances with different UPDRS scores. Considering the small number of training data, the model successfully separates the instances, however, we need to obtain more data for classification.
Secondary keywords:	bradykinesia;finger tapping;Parkinson's disease;disease rating scale;UPDRS;computer and information science;diploma;
Type (COBISS):	Bachelor thesis/paper
Study programme:	1000468
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages:	42 str.
ID:	11220342