ParkDetect: Early diagnosing Parkinson's Disease

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

Distinguishing Different Stages of Parkinson’s Disease Using Composite Index of Speed and Pen-Pressure of Sketching a Spiral

“…The association of handwriting and sketching of the spiral has been established in PD in early stages ( 8 , 9 ). However, one shortcoming in the use of handwriting or sketching is the need for an expert to interpret the sketches, especially in the early stages of the disease.…”

Automatic Diagnosis of Parkinson’s Disease using Handwriting Patterns

“…Noninvasive techniques, including drawing spirals, waves, or other handwritten messages, allow people to be readily identified from one another as well as between those who have Parkinson's disease (PD) and those who do not [4]. Early on in the PD, handwriting and spiral sketching were discovered to be somewhat correlated by earlier researchers and doctors [5,6]. The primary disadvantage of these diagnosis, nevertheless, is that accurate handwriting and sketch interpretation is required.…”

A New Approach to Diagnose Parkinson’s Disease Using a Structural Cooccurrence Matrix for a Similarity Analysis

“…Handwriting exams may be conducted on paper [ 13 ] or by using more sophisticated methods such as digitizers [ 5 ] or even a smartphone [ 14 ]. This type of exam has advantages as it is easily obtainable and can also provide diversity, such as spirals, ellipses, connected syllables, connected words, and many other ways to test a patient's ability to trace such forms [ 15 – 19 ].…”