Machine Learning on the Video Basis of Slow Pursuit Eye Movements Can Predict Symptom Development in Parkinson’s Patients

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

doi:10.1007/978-3-319-15705-4_26

Cited by 6 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: 98%

“…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: 98%

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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“…Another study tested how effective diagnostic parameters of slow (pursuit) eye movements are for the prediction of PD symptom development [29,30]. The horizontal pursuit EM with three different sinusoidal movement speeds was measured.…”

Section: Em In Pd -Pursuitmentioning

confidence: 99%

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Przybyszewski¹,

Śledzianowski²,

Chudzik³

et al. 2022

Preprint

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Humans are a vision-dominated species, and what we see depends on where we look. Therefore, eye movements (EM) are essential to our interactions with the environment, and experimental findings show EM is affected in neurodegenerative disorders (ND). It could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to determine if changes in EM-evoked responses can tell us about ND, such as Alzheimer’s (AD) and Parkinson’s Disease (PD) progression in different stages. In the present review, we have analyzed the results of neurological, psychological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with Machine Learning (ML) methods. Described predictive algorithms are using various approaches, including Granular Computing, Naive Bayes, Decision Trees/Tables, Logistic Regression, C-/LinearSVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are also navigation problems in 3D space in both diseases. Consequently, we investigated EM experiments in the virtual space and how they may help find neurodegeneration-related brain changes. In conclusion: EM parameters with clinical symptoms are powerful precision instruments that, in addition to predictions of ND progression with the help of ML, could be used to indicate the different preclinical stages of both diseases.

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Novel Machine Learning Pipeline for Real-Time Oculometry

Śledzianowski,

Nowacki,

Sitarz

et al. 2023

Communications in Computer and Information Science

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Machine Learning on the Video Basis of Slow Pursuit Eye Movements Can Predict Symptom Development in Parkinson’s Patients

Cited by 6 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

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Novel Machine Learning Pipeline for Real-Time Oculometry

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