Data Mining and Machine Learning on the Basis from Reflexive Eye Movements Can Predict Symptom Development in Individual Parkinson’s Patients

Przybyszewski, Andrzej W.; Kon, Mark A.; Szlufik, Stanisław; Dutkiewicz, Justyna; Habela, Piotr; Koziorowski, Dariusz

doi:10.1007/978-3-319-13650-9_43

Cited by 8 publications

(10 citation statements)

References 7 publications

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“…We have analyzed POM data using the following parameters averaged for both eyes: gain (eye movement amplitude/sinus amplitude) and accuracy (difference between sinusoid and eye positions) for three different frequencies. More details can be found in [3,4].…”

Section: Methodsmentioning

confidence: 99%

“…We have performed our analysis on PD data used earlier in [3,4]. All of the 12 patients had implanted electrodes in the subthalamic nucleus that is a standard procedure in advanced Parkinson's.…”

Section: Methodsmentioning

confidence: 99%

“…Apparently, many terms are defined differently in rough sets (applied and described in [3,4]) and fuzzy rough sets approach, e.g. in RST upper approximation is a global term, defined for the whole data set while in FRST we define upper approximation separately for each element.…”

Section: Theoretical Basismentioning

confidence: 99%

“…As every patient suffers from PD in a different way and reacts differently to the treatment, averaging methods can lead to the confusing results. Therefore, in continuation of [3,4], we propose to extend statistical analysis to data mining techniques in order to adjust PD treatment to an individual patient. Our method is based on fuzzy rough sets theory application as this approach should better fit to predict partly noisy and continuous medical measurements than previously proposed rough sets theory [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Rough Sets Theory Applied to Parameters of Eye Movements Can Help to Predict Effects of Different Treatments in Parkinson’s Patients

Kubis¹,

Szymański

Przybyszewski

2015

Lecture Notes in Computer Science

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Abstract. Parkinson (PD) is the second most common neurodegenerative disease (ND) with characteristic movement disorders. There are well defined standard procedures to measure disease stage (Hohen Yahr scale), progression and effects of treatments (UPDRS -unified Parkinson Disease Rate Scale). But these procedures can only be performed by experienced neurologist and they are partly subjective. The purpose of our work was to test objective and noninvasive method that may help to estimate disease stage by measuring fast and slow eye movements (EM). It was demonstrated earlier that EM changes in PD. We have measured reflexive saccades (RS) and slow pursuit ocular movements (POM) in four sessions related to different treatments. With help of fuzzy rough sets theory (FRST) we have related measurements with expert's opinion by generalizing experimental finding by fuzzy rules. In order to test our approach, we have divided our measurements into training and testing sets. In the second test, we have removed expert's decisions and predicted them from the training set in two situations: on the basis of only classical neurological measurements and on the basis of EM measurements. We have observed, on 12 PD patients basis, an increase in predictions accuracy when eye movements were included as condition attributes. Our results with help of the FRST suggest that EM measurements may become an important diagnostic tool in PD.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Theoretical Basismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy Rough Sets Theory Applied to Parameters of Eye Movements Can Help to Predict Effects of Different Treatments in Parkinson’s Patients

Kubis¹,

Szymański

Przybyszewski

2015

Lecture Notes in Computer Science

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“…If we want to improve such investigations, we need to take into account an excessive diversity of patients’ symptoms and the changeable effects of cares and therapies in distinctive PD cases. Here we follow an earlier proposal [ 1 ] to extend statistical analysis by data mining and machine learning (ML) methods, which can give a higher meaning to an individual patient’s symptoms and singular PD development. As a consequence, our methods will suggest a specific treatment adjusted to different individual patients that may lead in slowing down symptom progression and improvement of quality of life.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients

Przybyszewski

Kon

Szlufik

et al. 2016

Sensors

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We still do not know how the brain and its computations are affected by nerve cell deaths and their compensatory learning processes, as these develop in neurodegenerative diseases (ND). Compensatory learning processes are ND symptoms usually observed at a point when the disease has already affected large parts of the brain. We can register symptoms of ND such as motor and/or mental disorders (dementias) and even provide symptomatic relief, though the structural effects of these are in most cases not yet understood. It is very important to obtain early diagnosis, which can provide several years in which we can monitor and partly compensate for the disease’s symptoms, with the help of various therapies. In the case of Parkinson’s disease (PD), in addition to classical neurological tests, measurements of eye movements are diagnostic. We have performed measurements of latency, amplitude, and duration in reflexive saccades (RS) of PD patients. We have compared the results of our measurement-based diagnoses with standard neurological ones. The purpose of our work was to classify how condition attributes predict the neurologist’s diagnosis. For n = 10 patients, the patient age and parameters based on RS gave a global accuracy in predictions of neurological symptoms in individual patients of about 80%. Further, by adding three attributes partly related to patient ‘well-being’ scores, our prediction accuracies increased to 90%. Our predictive algorithms use rough set theory, which we have compared with other classifiers such as Naïve Bayes, Decision Trees/Tables, and Random Forests (implemented in KNIME/WEKA). We have demonstrated that RS are powerful biomarkers for assessment of symptom progression in PD.

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Building Classifiers for Parkinson’s Disease Using New Eye Tribe Tracking Method

Szymański

Szlufik

Koziorowski

et al. 2017

Lecture Notes in Computer Science

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Data Mining and Machine Learning on the Basis from Reflexive Eye Movements Can Predict Symptom Development in Individual Parkinson’s Patients

Cited by 8 publications

References 7 publications

Fuzzy Rough Sets Theory Applied to Parameters of Eye Movements Can Help to Predict Effects of Different Treatments in Parkinson’s Patients

Fuzzy Rough Sets Theory Applied to Parameters of Eye Movements Can Help to Predict Effects of Different Treatments in Parkinson’s Patients

Multimodal Learning and Intelligent Prediction of Symptom Development in Individual Parkinson’s Patients

Building Classifiers for Parkinson’s Disease Using New Eye Tribe Tracking Method

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