Early diagnosis of Parkinson’s disease based on non-motor symptoms: a descriptive and factor analysis

Kuresan, Harisudha; Dhanalakshmi, Samiappan; Ghosh, Senjuti; Gupta, Adrij Sen

doi:10.1007/s12652-021-02944-0

Cited by 15 publications

(4 citation statements)

References 33 publications

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“…These studies are just examples of many other studies (Parziale et al 2021;Qasim et al 2021;Orozco-Arroyave et al 2016;Tsanas et al 2012;Kurt et al 2019;Solana-Lavalle et al 2020;Kurt et al 2018;Kuresan et al 2021). The application of IoT has also guaranteed better management and control Sun et al 2021;Bhardwaj et al 2021;Połap 2018).…”

Section: Resultsmentioning

confidence: 85%

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Yousif

Balaha

Haikal

et al. 2022

J Ambient Intell Human Comput

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Parkinson’s disease (PD) is a neurodegenerative disorder with slow progression whose symptoms can be identified at late stages. Early diagnosis and treatment of PD can help to relieve the symptoms and delay progression. However, this is very challenging due to the similarities between the symptoms of PD and other diseases. The current study proposes a generic framework for the diagnosis of PD using handwritten images and (or) speech signals. For the handwriting images, 8 pre-trained convolutional neural networks (CNN) via transfer learning tuned by Aquila Optimizer were trained on the NewHandPD dataset to diagnose PD. For the speech signals, features from the MDVR-KCL dataset are extracted numerically using 16 feature extraction algorithms and fed to 4 different machine learning algorithms tuned by Grid Search algorithm, and graphically using 5 different techniques and fed to the 8 pretrained CNN structures. The authors propose a new technique in extracting the features from the voice dataset based on the segmentation of variable speech-signal-segment-durations, i.e., the use of different durations in the segmentation phase. Using the proposed technique, 5 datasets with 281 numerical features are generated. Results from different experiments are collected and recorded. For the NewHandPD dataset, the best-reported metric is 99.75% using the VGG19 structure. For the MDVR-KCL dataset, the best-reported metrics are 99.94% using the KNN and SVM ML algorithms and the combined numerical features; and 100% using the combined the mel-specgram graphical features and VGG19 structure. These results are better than other state-of-the-art researches.

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Section: Resultsmentioning

confidence: 85%

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Yousif

Balaha

Haikal

et al. 2022

J Ambient Intell Human Comput

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“…Due to the large amount and wide range of NMS, as well as accessibility for the assessment of the symptom, a large portion cannot be objectively identified in the same way. Multiple research studies have focused on NMS being present in early disease states and are therefore focused on use for diagnostic purposes [44][45][46]. The tracking of NMS using digital technologies reduces the likelihood of the patient or carer misreporting symptoms and provides a much larger data set, eradicating the issue with clinical assessments providing only a snapshot in time.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review

Bounsall¹,

Milne‐Ives²,

Hall³

et al. 2023

JMIR Res Protoc

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Background Parkinson disease (PD) is the second most prevalent neurodegenerative disease, with around 10 million people with PD worldwide. Current assessments of PD symptoms are conducted by questionnaires and clinician assessments and have many limitations, including unreliable reporting of symptoms, little autonomy for patients over their disease management, and standard clinical review intervals regardless of disease status or clinical need. To address these limitations, digital technologies including wearable sensors, smartphone apps, and artificial intelligence (AI) methods have been implemented for this population. Many reviews have explored the use of AI in the diagnosis of PD and management of specific symptoms; however, there is limited research on the application of AI to the monitoring and management of the range of PD symptoms. A comprehensive review of the application of AI methods is necessary to address the gap of high-quality reviews and highlight the developments of the use of AI within PD care. Objective The purpose of this protocol is to guide a systematic review to identify and summarize the current applications of AI applied to the assessment, monitoring, and management of PD symptoms. Methods This review protocol was structured using the PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols) and the Population, Intervention, Comparator, Outcome, and Study (PICOS) frameworks. The following 5 databases will be systematically searched: PubMed, IEEE Xplore, Institute for Scientific Information’s Web of Science, Scopus, and the Cochrane Library. Title and abstract screening, full-text review, and data extraction will be conducted by 2 independent reviewers. Data will be extracted into a predetermined form, and any disagreements in screening or extraction will be discussed. Risk of bias will be assessed using the Cochrane Collaboration Risk of Bias 2 tool for randomized trials and the Mixed Methods Appraisal Tool for nonrandomized trials. Results As of April 2023, this systematic review has not yet been started. It is expected to begin in May 2023, with the aim to complete by September 2023. Conclusions The systematic review subsequently conducted as a product of this protocol will provide an overview of the AI methods being used for the assessment, monitoring, and management of PD symptoms. This will identify areas for further research in which AI methods can be applied to the assessment or management of PD symptoms and could support the future implementation of AI-based tools for the effective management of PD. International Registered Report Identifier (IRRID) PRR1-10.2196/46581

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“…The classic motor symptoms are frequently mentioned by authors ( 6 ). PD can also be evidenced by non-motor symptoms, which include psychiatric disorders (e.g., hallucinations and delusions, mood disorder), cardiovascular disorders (e.g., orthostatic hypertension, fatigue), neurocognitive disorders, attention deficit, sexual dysfunction, visual disorders, among others ( 7 , 8 ). However, a combination of these symptoms poses a more significant impact on the person's quality of life with PD.…”

Section: Introductionmentioning

confidence: 99%

“…It has been reported that 81% of people with PD experienced FoG after a disease duration of 20 years ( 18 ). Nieuwboer and Giladi ( 7 ) point to four potential mechanisms for FoG events that may explain this time-dependent increase in the prevalence. According to these authors, FoG may occur due to: a motor breakdown associated with the accumulation of various motor deficits (threshold model); an inability to deal with multiple sensory and motor inputs leading to the interruption of locomotion (interference model); behavioral indecision (cognitive model); a cleavage between introductory programming and the intended motor response (decoupling model).…”

Section: Introductionmentioning

confidence: 99%

Wearable Walking Assistant for Freezing of Gait With Environmental IoT Monitoring: A Contribution to the Discussion

Bernardes

Ventura

Neves

et al. 2022

Front. Public Health

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Parkinson's disease (PD) is the second most common neurodegenerative disease, significantly increasing in the last three decades. Worldwide, seven to ten million people are affected by PD. In people living with PD, freezing of gait (FoG) significantly impacts activities of daily living, potentially leading to falls, injuries, and loss of autonomy. FoG prevalence rates vary widely, reaching at least 50% of patients with PD. Current therapeutic options have limited effectiveness, and their complement with innovative technology-based solutions in the real world is demanded to enhance daily functioning for people living with PD. This article provides a narrative review of current technological developments for people living with PD and, derived from that evidence, presents a perspective on integrating wearable technology and IoT to support telemonitoring and self-management of people living with PD in their daily living environment. Complementing current therapeutic options with technology-based solutions in PD patients' real-world environment is crucial to enhancing the quality of life of people living with PD. In that way, wearable technology and IoT might constitute resources of excellence in seamless monitoring and self-management in people's home environments.

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Early diagnosis of Parkinson’s disease based on non-motor symptoms: a descriptive and factor analysis

Cited by 15 publications

References 33 publications

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

A generic optimization and learning framework for Parkinson disease via speech and handwritten records

Artificial Intelligence Applications for Assessment, Monitoring, and Management of Parkinson Disease Symptoms: Protocol for a Systematic Review

Wearable Walking Assistant for Freezing of Gait With Environmental IoT Monitoring: A Contribution to the Discussion

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