A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

Rana, Arti; Dumka, Ankur; Singh, Rajesh; Panda, Manoj Kumar; Priyadarshi, Neeraj

doi:10.3390/diagnostics12112708

Cited by 12 publications

(4 citation statements)

References 213 publications

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“…Tests using fast Fourier transform (FFT) analysis and computer-aided models based on artificial intelligence-enabled the detection of PD at an early stage. The findings contributing to early PD detection are presented in the studies by Rezvanian et al [ 23 ]—the selection of Motor Subtypes and Rana et al [ 24 ] as well as Fadil et al [ 25 ]—the discovery of repeated patterns in measured physiological signals and the COP signal leading to new ways of their detection.…”

Section: Introductionmentioning

confidence: 99%

Balance assessment in selected stages of Parkinson’s disease using trend change analysis

Wodarski,

Jurkojć,

Michalska

et al. 2023

J NeuroEngineering Rehabil

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Background Balance disorders in patients diagnosed with Parkinson’s disease (PD) are associated with a change in balance-keeping strategy and reflex disorders which regulate the maintenance of vertical body posture. Center of foot pressure (COP) displacement signals were analyzed during quiet standing experiments to define such changes. The research aimed to apply stock exchange indices based on the trend change analyses to the assessment of a level of the Parkinson disease progression on the grounds of the analysis of the COP signals. Methods 30 patients in two stages of PD, 40 elderly participants, and 20 individuals at a young age were studied. Each person was subjected to 3 measurements with open and closed eyes. A technical analysis of the COP displacement signal was performed, and the following quantities were determined: indices related to the number of trend changes (TCI), indices defining a mean time (TCI_dT), and mean displacement (TCI_dS) and mean velocity (TCI_dV) between such changes. Results The results indicate a higher TCI value for PD than for aged-matched control group (p < 0.05). In the case of PD patients, there was also an increase in the TCI_dS value by 2–5 mm, which mainly contributed to the increase in TCI_dV. Statistically significant differences for the TCI_dT values occurred between all groups in which differences in the average COP velocity were noted. Conclusions The TCI and TCI_dV results obtained for the healthy participants enabled the development of indices supporting PD diagnostics. The causes of the TCI_dV changes in patients were determined, i.e., whether they resulted from an increase in the TCI_dT or TCI_dS between the moments of trend changes indicated by the developed algorithm. The developed methodology provides new information on the impact of PD on the strategy of maintaining balance, which was impossible to obtain using currently used analyses. Trial registration The conducted research is an observational study and does not include a health care intervention. Participants gave their consent to participate in the research and the procedure was approved by the Institutional Bioethics Committee.

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Section: Introductionmentioning

confidence: 99%

Balance assessment in selected stages of Parkinson’s disease using trend change analysis

Wodarski,

Jurkojć,

Michalska

et al. 2023

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

show abstract

“…Tests using fast Fourier transform (FFT) analysis and computer-aided models based on arti cial intelligence-enabled the detection of PD at an early stage. The ndings contributing to early PD detection are presented in the studies by Rezvanian et al [23] -the selection of Motor Subtypes and Rana et al [24] as well as Fadil et al [25] -the discovery of repeated patterns in measured physiological signals and the COP signal leading to new ways of their detection.…”

Section: Introductionmentioning

confidence: 99%

Balance Assessment in Selected Stages of Parkinson’s Disease Using Stock Exchange Indices

Wodarski

Jurkojć

Michalska

et al. 2023

Preprint

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Background Balance disorders in patients diagnosed with Parkinson’s disease (PD) are associated with a change in balance-keeping strategy and reflex disorders which regulate the maintenance of vertical body posture. Center of foot pressure (COP) displacement signals were analyzed during quiet standing experiments to define such changes. The research aimed to apply stock exchange indices based on the trend change analyses to the assessment of a level of the Parkinson disease progression on the grounds of the analysis of the COP signals. Methods 30 patients in two stages of PD, 40 elderly participants, and 20 individuals at a young age were studied. Each person was subjected to 3 measurements with open and closed eyes. A technical analysis of the COP displacement signal was performed, and the following quantities were determined: indices related to the number of trend changes (TCI), indices defining a mean time (TCI_dT), and mean displacement (TCI_dS) and mean velocity (TCI_dV) between such changes. Results The results indicate a higher TCI value for PD than for CG (p < 0.05). In the case of PD patients, there was also an increase in the TCI_dS value by 2–5 mm, which mainly contributed to the increase in TCI_dV. Statistically significant differences for the TCI_dT values occurred between all groups in which differences in the average COP velocity were noted. Conclusions The TCI and TCI_dV results obtained for the healthy participants enabled the development of indices supporting PD diagnostics. The causes of the TCI_dV changes in patients were determined, i.e., whether they resulted from an increase in the TCI_dT or TCI_dS between the moments of trend changes indicated by the developed algorithm. The developed methodology provides new information on the impact of PD on the strategy of maintaining balance, which was impossible to obtain using currently used analyses. The use of this method to differentiate the stage of PD requires further research. Trial registration: The conducted research is an observational study and does not include a health care intervention. Participants gave their consent to participate in the research and the procedure was approved by the Institutional Bioethics Committee.

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“…Electronics 2023, 12, 783 5 of 52 [15] They did a thorough analysis of 217 research papers that discussed the use of different ML techniques and DNN designs to diagnose PD. They also carefully looked through and examined the researcher's architectural plans.…”

Section: Survey Methodology 131 Search Strategy and Literature Sourcesmentioning

confidence: 99%

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

Dixit

Bohre²,

Singh

et al. 2023

Electronics

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Parkinson’s disease (PD) is a devastating neurological disease that cannot be identified with traditional plasma experiments, necessitating the development of a faster, less expensive diagnostic instrument. Due to the difficulty of quantifying PD in the past, doctors have tended to focus on some signs while ignoring others, primarily relying on an intuitive assessment scale because of the disease’s characteristics, which include loss of motor control and speech that can be utilized to detect and diagnose this disease. It is an illness that impacts both motion and non-motion functions. It takes years to develop and has a wide range of clinical symptoms and prognoses. Parkinson’s patients commonly display non-motor symptoms such as sleep problems, neurocognitive ailments, and cognitive impairment long before the diagnosis, even though scientists have been working to develop designs for diagnosing and categorizing the disease, only noticeable defects such as movement patterns, speech, or writing skills are offered in this paper. This article provides a thorough analysis of several AI-based ML and DL techniques used to diagnose PD and their influence on developing additional research directions. It follows the guidelines of Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). This review also examines the current state of PD diagnosis and the potential applications of data-driven AI technology. It ends with a discussion of future developments, which aids in filling critical gaps in the current Parkinson’s study.

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A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

Cited by 12 publications

References 213 publications

Balance assessment in selected stages of Parkinson’s disease using trend change analysis

Balance assessment in selected stages of Parkinson’s disease using trend change analysis

Balance Assessment in Selected Stages of Parkinson’s Disease Using Stock Exchange Indices

A Comprehensive Review on AI-Enabled Models for Parkinson’s Disease Diagnosis

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