Rough Set Data Mining Algorithms and Pursuit Eye Movement Measurements Help to Predict Symptom Development in Parkinson’s Disease

Śledzianowski, Albert; Szymański, Artur; Szlufik, Stanisław; Koziorowski, Dariusz

doi:10.1007/978-3-319-75420-8_41

Cited by 5 publications

(8 citation statements)

References 5 publications

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“…The gain and accuracy (EM measurement section) were estimated. The discretization and attribute reduction with RSES demonstrated that the significant attributes were precise for the accuracy of the fastest sinusoidal movement speed, and gains for medium and high sinusoidal movement speeds [29,30]. The result of the 4-fold cross-validation gave a global accuracy of 0.77 for the UPDRS III prediction.…”

Section: Em In Pd -Pursuitmentioning

confidence: 97%

“…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%

“…The above predictions were obtained on a sample of 20 patients using different binning methods (KNIME auto binner), which allowed the grouping of UPDRS III data in intervals of equal frequencies. The 90% accuracy in predictions on these data was achieved with the RSES and 5-fold cross-validation [30]. When comparing the accuracy results of different classifiers, the RSES is in first place in the ranking, ahead of SVM (59%), Naive Bayes (55%), and Random Forest (52%) [30].…”

Section: Em In Pd -Pursuitmentioning

confidence: 99%

“…The 90% accuracy in predictions on these data was achieved with the RSES and 5-fold cross-validation [30]. When comparing the accuracy results of different classifiers, the RSES is in first place in the ranking, ahead of SVM (59%), Naive Bayes (55%), and Random Forest (52%) [30].…”

Section: Em In Pd -Pursuitmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Em In Pd -Pursuitmentioning

confidence: 97%

Section: Em In Pd -Pursuitmentioning

confidence: 99%

Section: Em In Pd -Pursuitmentioning

confidence: 99%

Section: Em In Pd -Pursuitmentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Przybyszewski¹,

Śledzianowski²,

Chudzik³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

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

Section: Eye Movements and Neurodegenerative Diseasesmentioning

confidence: 99%

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Przybyszewski

Śledzianowski

Chudzik

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Humans are a vision-dominated species; what we perceive depends on where we look. Therefore, eye movements (EMs) are essential to our interactions with the environment, and experimental findings show EMs are affected in neurodegenerative disorders (ND). This could be a reason for some cognitive and movement disorders in ND. Therefore, we aim to establish whether changes in EM-evoked responses can tell us about the progression of ND, such as Alzheimer’s (AD) and Parkinson’s diseases (PD), in different stages. In the present review, we have analyzed the results of psychological, neurological, and EM (saccades, antisaccades, pursuit) tests to predict disease progression with machine learning (ML) methods. Thanks to ML algorithms, from the high-dimensional parameter space, we were able to find significant EM changes related to ND symptoms that gave us insights into ND mechanisms. The predictive algorithms described use various approaches, including granular computing, Naive Bayes, Decision Trees/Tables, logistic regression, C-/Linear SVC, KNC, and Random Forest. We demonstrated that EM is a robust biomarker for assessing symptom progression in PD and AD. There are 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, e.g., related to place or/and orientation problems. In conclusion, EM parameters with clinical symptoms are powerful precision instruments that, in addition to their potential for 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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Measurements of Antisaccades Parameters Can Improve the Prediction of Parkinson’s Disease Progression

Śledzianowski

Szymański

Drabik

et al. 2019

Intelligent Information and Database Systems

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Rough Set Data Mining Algorithms and Pursuit Eye Movement Measurements Help to Predict Symptom Development in Parkinson’s Disease

Cited by 5 publications

References 5 publications

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Machine Learning and Eye Movements Give Insights into Neurodegenerative Disease Mechanisms

Measurements of Antisaccades Parameters Can Improve the Prediction of Parkinson’s Disease Progression

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