Granular Computing (GC) Demonstrates Interactions Between Depression and Symptoms Development in Parkinson’s Disease Patients

Przybyszewski, Andrzej W.; Nowacki, J.; Drabik, Aldona; Szlufik, Stanisław; Habela, Piotr; Koziorowski, Dariusz

doi:10.1007/978-3-030-14802-7_51

Cited by 6 publications

(9 citation statements)

References 16 publications

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“…The predictions were performed with the help of RSES and with standard neurological testing and EM parameters. Based on rules from BMT visit-one patients, the prediction of symptoms (UPDRS) of BMT visits two and three had accuracies of 0.7 and 0.7, but by adding the depression score, accuracies increased to 0.77 and 0.80 [42]. Similar predictions were calculated for the DBS group progression based on BMT visit-one rules.…”

Section: Prediction Of the Disease Progression Related To Motor Cogni...mentioning

confidence: 62%

“…We have demonstrated for PD patients that the depression score (measured by Beck's Depression Inventory) is an essential attribute in the estimation of disease progression, and its value significantly increases the accuracy of UPDRS estimation [42]. In Alzheimer's disease, depression is a significant factor, especially for older subjects with late onset of AD (LOAD) for patients over 65 years of age [81].…”

Section: Discussionmentioning

confidence: 99%

“…Obtained accuracies for the DBS group were the following: visit 1 -0.64, visit 2 -0.77, and visit 3 -0.74. By adding the depression score to all attributes, improved accuracies of visit 1 to 0.77, visit 2 to 0.85 and visit 3 to 0.8 [42]. In summary, the depression score has a significant influence on predicting Parkinson's disease progression.…”

Section: Prediction Of the Disease Progression Related To Motor Cogni...mentioning

confidence: 93%

“…The influence of the patient's emotions was also tested, the depression score (Beck test), on the accuracy of the predictions of the disease progression in the same or different groups of patients [42]. The progressions of the BMT (only on medication) group visits 2 and 3 and DBS (deep brain stimulation) group visit 1 based on the BMT visit 1 symptom were compared.…”

Section: Prediction Of the Disease Progression Related To Motor Cogni...mentioning

confidence: 99%

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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: Prediction Of the Disease Progression Related To Motor Cogni...mentioning

confidence: 62%

Section: Discussionmentioning

confidence: 99%

Section: Prediction Of the Disease Progression Related To Motor Cogni...mentioning

confidence: 93%

Section: Prediction Of the Disease Progression Related To Motor Cogni...mentioning

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

“…The influence of the patient’s emotions on the accuracy of the predictions of disease progression in the same group or different groups of patients was also tested through the depression score (Beck test) [ 42 ]. The progressions of the BMT group (only on medication) for visits 2 and 3 and the DBS group (deep brain stimulation) for visit 1 were compared based on the BMT symptoms during visit 1.…”

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

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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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IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson’s Disease Patients

Przybyszewski

Nowacki

Drabik

et al. 2020

Lecture Notes in Computer Science

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Granular Computing (GC) Demonstrates Interactions Between Depression and Symptoms Development in Parkinson’s Disease Patients

Cited by 6 publications

References 16 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

IGrC: Cognitive and Motor Changes During Symptoms Development in Parkinson’s Disease Patients

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