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ParkDetect: Early diagnosing Parkinson's Disease

 
: Graca, Ricardo; Castro, Rui S.E.; Cevada, Joao

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Instrumentation and Measurement Society:
IEEE International Symposium on Medical Measurements and Applications, MeMeA 2014. Proceedings : Lisbon, Portugal, 11 - 12 June 2014
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-2922-1
ISBN: 978-1-4799-2921-4
ISBN: 978-1-4799-2920-7
pp.40-45
International Symposium on Medical Measurements and Applications (MeMeA) <9, 2014, Lisbon>
English
Conference Paper
Fraunhofer AICOS ()
Parkinson; diagnostic; mobile; data mining

Abstract
Parkinson's Disease is one of the most common neurodegenerative disorders of the central nervous system that affects elderly. There are six main symptoms: tremors, rigidity, bradykinesia (slow movements), hand asymmetry, posture instability and freezing of gait. Nowadays any type of diagnose for this disorder is done through observation by a health care professional specialized in this area. Therefore a simpler and more efficient method that General Practioners can use to have some grounded information to decide to forward a possible patient to a specialist is needed. With this in mind different systems were studied coming to the conclusion that a mobile application is among the best options. This work can be split in four important phases (see Figure 1): (1) study of the current market for this problem and for the solution to be developed, (2) development of a smartphone application capable of gathering data of the early symptoms of Parkinson's taking into consideration all the smartphone's specifications; (3) use the application to gather data from real patients and a control group and (4) test and select a classification algorithm. The first phase involved two research topics: problem and solution. The problem consisted in studying all the symptoms that could theoretically be detected by the different smartphone components. The solution consisted in studying the different methods used to solve such a problem using data mining techniques (different feature selection and classification algorithms that best take advantage of the nature of the data gathered). The second phase consisted in the development of the smartphone application with four components (spiral analysis, tap analysis, simple questions and gait analysis). The third phase was dedicated in building the control group gathering data from healthy people and a Parkinson patients group for a total of 35 subjects. Finally, the fourth phase was using the studied algorithms to filter the different features- and compare the different algorithms selected. With the available data from the test subjects it was possible to achieve promising results from the gait analysis of the patients where the pelvic sway was a good feature to help differentiate Parkinson patients from healthy ones.

: http://publica.fraunhofer.de/documents/N-324367.html