Recent Survey on Parkinson Disease Diagnose using Deep Learning Mechanism

Anju, P; Varghese, Aleena; Roy, Ashly; Suresh, S.; Joy, Emily; Sunder, R.

doi:10.1109/icimia48430.2020.9074896

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Furthermore, the evaluation incorporates dynamic aspects, such as the number of times the difference between HT and ET radii changes sign, indicating shifts in the writing dynamics. This comprehensive set of metrics provides a detailed characterization of various aspects of the writing process, offering valuable insights into pressure variations, pen grip, and pen movement dynamics [ 25 , 42 , 43 ]. Professional microphone Channel-Yaffe (YA) feature, IS4- par-alaling compact, IS6-speaker trait, EM1 embose, Smartphone channel- KTU features, MP-MPEG7 descriptors, EM1-emboss [ 44 ].…”

Section: Methodsmentioning

confidence: 99%

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A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

Islam,

Hasan Majumder,

Hussein

et al. 2024

Heliyon

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Section: Methodsmentioning

confidence: 99%

“…Anju et al (2020) [ 43 ] demonstrated that PD can be diagnosed using cell phones to track patients' steps. They suggested the use of a deep MLP clas-sifier that doesn't require communication with doctors and can be based on the affected person's voice and movements.…”

Section: Methodsmentioning

confidence: 99%

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

Islam,

Hasan Majumder,

Hussein

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…In addition to gait information, sensors capture voice information to evaluate voice disorders, in which case the created systems are capable of recording in real time specific voice activities based on UPDRS [25]. According to Anju et al [26] a smartphone with its sensors and extensive capabilities can be used for the self-assessment of patients, since it is capable of assessing motor and mental disorders.…”

Section: Related Workmentioning

confidence: 99%

Learning Spatio-Temporal Radon Footprints for Assessment of Parkinson’s Dyskinesia

Theofilou,

Tsatiris,

Kollias

2024

Electronics

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Parkinson’s disease is a severe neurodegenerative disorder that leads to loss of control over various motor and mental functions. Its progression can be limited with medication, particularly through the use of levodopa. However, prolonged administration of levodopa often results in disorders independent of those caused by the disease. The detection of these disorders is based on the clinical examination of patients, through different type of activities and tasks, using the Unified Dyskinesia Rating Scale (UDysRS). In the present work, our aim is to develop a state-of-the-art assessment system for levodopa-induced dyskinesia (LID) using the joint coordinate data of a human skeleton body depicted on videotaped activities related to UDysRS. For this reason, we combine a robust mathematical method for encoding action sequences known as Spatio-temporal Radon Footprints (SRF) with a Convolutional Neural Network (CNN), in order to estimate dyskinesia’s ratings for six body parts. We introduce two different methodological approaches: Global SRF-CNN and Local SRF-CNN, based on the set of skeletal points used in the encoding scheme. A comparison between these approaches reveals that Local SRF-CNN demonstrates better performance than the Global one. Finally, Local SRF-CNN outperforms the state-of-the-art technique, on both tasks, for UDysRS dyskinesia assessment, using joint coordinate data of the human body, achieving an overall performance in mean RMSE value of 0.6198 for Drinking task and 0.4885 for Communication, compared to 0.6575 and 0.5175, respectively. This illustrates the ability of the proposed machine learning system to successfully assess LID.

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A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s Disease

Saravanan

Ramkumar

Adalarasu

et al. 2022

Arch Computat Methods Eng

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Recent Survey on Parkinson Disease Diagnose using Deep Learning Mechanism

Cited by 4 publications

References 9 publications

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

A review of machine learning and deep learning algorithms for Parkinson's disease detection using handwriting and voice datasets

Learning Spatio-Temporal Radon Footprints for Assessment of Parkinson’s Dyskinesia

A Systematic Review of Artificial Intelligence (AI) Based Approaches for the Diagnosis of Parkinson’s Disease

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