Combining Results of Different Oculometric Tests Improved Prediction of Parkinson’s Disease Development

Śledzianowski, Albert; Szymański, Artur; Drabik, Aldona; Szlufik, Stanisław; Koziorowski, Dariusz; Przybyszewski, Andrzej W.

doi:10.1007/978-3-030-42058-1_43

Cited by 4 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The red line is related to light spot movements, and the blue and green curves are related to reflexive saccades performed by two different PD patients in different stages of the disease: the blue curve EM at the beginning of the disease, and the green curve at advanced PD. Notice the significant difference in response latencies [11].…”

Section: Figurementioning

confidence: 97%

“…The same group of patients as in [21], was used for UPDRS prediction based on RS and AS measurements. The best accuracy of 0.89 was achieved by the Decision Trees [11]. The results showed that the accuracy of the predictions increased with the number of significant attributes that were obtained by, for example, averaging RS and AS duration or by adding the averaged standard deviations of each patient's latencies [11].…”

Section: Em In Pd -Saccades and Antisaccadesmentioning

confidence: 99%

“…A comparison of the average results +/-SD of the AS latencies, obtained in a group of 27 age-matched normal controls (healthy people), 10 AD subjects, and 12 patients with PD. The averages of the control group and AD patients are derived from the research results obtained from [43] and the average of the PD patients from the results obtained from [11].…”

Section: Figurementioning

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

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.

show abstract

Section: Figurementioning

confidence: 97%

Section: Em In Pd -Saccades and Antisaccadesmentioning

confidence: 99%

Section: Figurementioning

confidence: 99%

See 1 more Smart Citation

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

“…The same group of patients as in [ 21 ] was used for UPDRS prediction based on RS and AS measurements. The best accuracy of 0.89 was achieved by Decision Trees [ 11 ]. The results showed that the accuracy of the predictions increased with the number of significant attributes that were obtained by, for example, averaging RS and AS duration or by adding the averaged standard deviations of each patient’s latencies [ 11 ].…”

Section: Eye Movements and Neurodegenerative Diseasesmentioning

confidence: 99%

“…The red line is related to light spot movements, and the blue and green curves are related to reflexive saccades performed by two different PD patients in different stages of the disease: the blue curve denotes EM at the beginning of the disease, and the green curve at advanced PD. Notice the significant difference in response latencies [ 11 ].…”

Section: Figurementioning

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.

show abstract

Universal Machine-Learning Processing Pattern for Computing in the Video-Oculography

Śledzianowski,

Nowacki,

Sitarz

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Combining Results of Different Oculometric Tests Improved Prediction of Parkinson’s Disease Development

Cited by 4 publications

References 11 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

Universal Machine-Learning Processing Pattern for Computing in the Video-Oculography

Contact Info

Product

Resources

About