Solving The Maze of Diagnosing Parkinson’s Disease based on Portable EEG Sensing to be Adaptable to Go In-The-Wild

Rahman, Ashiqur; Tutul, Abdullah Aman; Islam, A. B. M. Alim Al

doi:10.1145/3428363.3428374

Cited by 3 publications

(5 citation statements)

References 25 publications

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“…Firstly, several devices that are widely used as conventional medical equipment can be adopted in free-living environments in portable versions as well. For example, EEG signals produced by portable headsets have trained feed-forward NNs to detect PD with 96.5% accuracy [133]. Surface EMG (sEMG) signals collected from wrist-bands have trained RF regression models that estimate MDS-UPDRS III scores with 0.739 correlation coefficient [134].…”

Section: Other Types Of Sensorsmentioning

confidence: 99%

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Giannakopoulou

Roussaki

Demestichas

2022

Sensors

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Parkinson’s disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients’ quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson’s disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson’s disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson’s disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.

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Section: Other Types Of Sensorsmentioning

confidence: 99%

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Giannakopoulou

Roussaki

Demestichas

2022

Sensors

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“…As can be appreciated, there was great heterogeneity in the density of electrodes, with a mode of 14 electrodes considered in eight studies. There were studies (such as [35,41,63,[68][69][70]) that used EEG with less than five channels, but it should be noted that three of them detected gait disturbances, two diagnosed PD in combination with EMG, and [70] used one channel with emotional stimuli. Regarding the duration of the EEG recording, the mode was 5 min.…”

Section: Assessment Of the Quality Of The Datasetmentioning

confidence: 99%

“…For the 31 articles that covered the diagnostic problem of PD, it was found that the models with at least two metrics greater than 90% were: refs. [51,64,66,70] using ANN with values over 97% for sensitivity and precision, refs. [38,39,49] considering CNN with values over 99% for accuracy, precision and sensitivity ( [38,39] were based on the same study), refs.…”

Section: Evaluation Of the Models Usedmentioning

confidence: 99%

“…It was found that the predominant method was crossvalidation for 9 of the 12 articles, whereas [29,49,64] split the data into training, validation, and test. The models with the best results in the problem of classifying patients with PD and controls were CNN, and the group of ANN models, found in articles [38,39,51,59,64,66,70].…”

Section: Evaluation Of the Models Usedmentioning

confidence: 99%

“…This was reflected in [65], which classified PD patients in the ON state vs. OFF state of medication, and in those studies that evaluated cognitive impairment [32,34,45,47,53]. Consequently, those articles that did not specify the clinical setting of the patients [37,40,42,43,46,51,53,55,56,[58][59][60][61][62][64][65][66][68][69][70], nor the status of the medication in the patients [32,33,35,41,50,54,63,71] were excluded from the evaluation process of the current state of diagnosis of PD, because they did not allow the objective evaluation of the results of the models used. These exclusions, based on quality criteria, resulted in 15 admissible articles.…”

Section: Quality Of the Datasetmentioning

confidence: 99%

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Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

2022

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Background: Parkinson’s disease (PD) affects 7–10 million people worldwide. Its diagnosis is clinical and can be supported by image-based tests, which are expensive and not always accessible. Electroencephalograms (EEG) are non-invasive, widely accessible, low-cost tests. However, the signals obtained are difficult to analyze visually, so advanced techniques, such as Machine Learning (ML), need to be used. In this article, we review those studies that consider ML techniques to study the EEG of patients with PD. Methods: The review process was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, which are used to provide quality standards for the objective evaluation of various studies. All publications before February 2022 were included, and their main characteristics and results were evaluated and documented through three key points associated with the development of ML techniques: dataset quality, data preprocessing, and model evaluation. Results: 59 studies were included. The predominating models were Support Vector Machine (SVM) and Artificial Neural Networks (ANNs). In total, 31 articles diagnosed PD with a mean accuracy of 97.35 ± 3.46%. There was no standard cleaning protocol for EEG and a great heterogeneity in EEG characteristics was shown, although spectral features predominated by 88.37%. Conclusions: Neither the cleaning protocol nor the number of EEG channels influenced the classification results. A baseline value was provided for the PD diagnostic problem, although recent studies focus on the identification of cognitive impairment.

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Predicting Parkinson’s Disease Using Analytical Algorithm: A Review

Gaur,

Awasthi

2024

Advances in Geographical and Environmental Sciences

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Solving The Maze of Diagnosing Parkinson’s Disease based on Portable EEG Sensing to be Adaptable to Go In-The-Wild

Cited by 3 publications

References 25 publications

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Survey of Machine Learning Techniques in the Analysis of EEG Signals for Parkinson’s Disease: A Systematic Review

Predicting Parkinson’s Disease Using Analytical Algorithm: A Review

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