Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease

Abdulhay, Enas; Arunkumar, N.; Narasimhan, K.; Elamaran, V.; Venkatraman, Vindhya

doi:10.1016/j.future.2018.02.009

Cited by 192 publications

(68 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[17]. In fact, the ground reaction force of individuals during the gait cycle has been used to detect PD in [18]. The study shows that stance time, swing time, stride time and foot strike profiles can be used to distinguish PD patients from healthy controls.…”

Section: B Applications Of Gait Analysismentioning

confidence: 99%

Deep Learning for Monitoring of Human Gait: A Review

2019

View full text Add to dashboard Cite

The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

show abstract

Section: B Applications Of Gait Analysismentioning

confidence: 99%

Deep Learning for Monitoring of Human Gait: A Review

2019

View full text Add to dashboard Cite

show abstract

“…Design of digital monitoring tools for PD with diagnostic value has been a research field with a great variety of applications, due to the wide spectrum of PD clinical symptoms. Efforts processing data streams as captured from ICT devices, have proven to be robust in distinguishing populations facing motor symptoms from healthy ones, in different sub-tasks in-the-clinic assessment, such as voice (Orozco-Arroyave et al, 2016), gait or tremor (Abdulhay et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild

et al. 2018

View full text Add to dashboard Cite

“…As examples, we may point out studies applying AI to the identification of breast cancer (Azevedo et al 2015a;Azevedo et al 2015b;Cordeiro et al 2012;Cordeiro et al 2013;Cordeiro et al 2016a;Cordeiro et al 2016b;Cordeiro et al 2017;Cruz et al 2018;de Lima et al 2014;de Lima et al 2016;Mascaro et al 2009;Santana et al 2018) and neurodegenerative diseases, such as Alzheimer's disease (Andrade et al 2018;Commowick et al 2018;dos Santos et al 2007;dos Santos et al 2008a;dos Santos et al 2008b;dos Santos et al 2009a;Dos Santos et al 2009b;dos Santos et al 2010;Farhan et al 2014;Long et al 2017;Plant et al 2010;Santos et al 2009). Regarding to Parkinson's disease, some studies are being conducted not only using EEG signals but also using information from tremors and gait assessment in subjects with PD (Abdulhay et al 2018;Lih et al 2018). This is possible due to the pattern recognition characteristics of AI techniques.…”

Section: Introductionmentioning

confidence: 99%

Early diagnosis of Parkinson’s disease using EEG, machine learning and partial directed coherence

et al. 2020

View full text Add to dashboard Cite

Background Parkinson's disease (PD) is a neurodegenerative disease, which has an upward progression. In advanced stages, motor symptoms cause functional impairment to patients due to the degeneration of the substantia nigra. In early stages of PD, there is a sensory impairment, and patients report visual processing dysfunction. There is still no cure for PD, and early diagnosis is essential to slow disease progression. New method Given the good anatomical representation and organization of the visual system in the cerebral cortex, in this study, we propose a biomarker of PD using EEG signals, photic stimulation, partial directed coherence (PDC) to perform feature extraction, and machine learning (ML) techniques. Our goal is to classify participants into three distinct groups: PD patients who are medicated; patients with PD and drug deprivation; and healthy subjects. Results We were able to achieve outstanding results, above 99% of accuracy and kappa statistic up to 0.98 using random forests and feature selection techniques. Comparison with existing methods: Our approach was evaluated using several ML methods. As features, we initially used the electrodes, without explicitly extracting feature vectors over signal samples. Conclusions The good results we obtained by using random forests made possible clinical applications for the early detection of PD and, consequently, better prognosis and patient's quality of life.

show abstract

Gait and tremor investigation using machine learning techniques for the diagnosis of Parkinson disease

Cited by 192 publications

References 17 publications

Deep Learning for Monitoring of Human Gait: A Review

Deep Learning for Monitoring of Human Gait: A Review

Motor Impairment Estimates via Touchscreen Typing Dynamics Toward Parkinson's Disease Detection From Data Harvested In-the-Wild

Early diagnosis of Parkinson’s disease using EEG, machine learning and partial directed coherence

Contact Info

Product

Resources

About