Recent machine learning advancements in sensor-based mobility analysis: Deep learning for Parkinson's disease assessment

2016 ◽  
Author(s):  
Bjoern M. Eskofier ◽  
Sunghoon I. Lee ◽  
Jean-Francois Daneault ◽  
Fatemeh N. Golabchi ◽  
Gabriela Ferreira-Carvalho ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Disease Assessment ◽  
Mobility Analysis

scholarly journals Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147635-147646 ◽  
Author(s):  
Wu Wang ◽  
Junho Lee ◽  
Fouzi Harrou ◽  
Ying Sun
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Detection

scholarly journals A Study On Deep Learning And Machine Learning Techniques On Detection Of Parkinson’s Disease

2021 ◽  
Vol 309 ◽  
pp. 01008
Author(s):  
P. Mounika ◽  
S. Govinda Rao
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Stage ◽  
Signs And Symptoms ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Machine Learning Methods ◽  
Learning Techniques

Parkinson’s disease (PD) is a sophisticated anxiety malady that impairs movement. Symptoms emerge gradually, initiating with a slight tremor in only one hand occasionally. Tremors are prevalent, although the condition is sometimes associated with stiffness or slowed mobility. In the early degrees of PD, your face can also additionally display very little expression. Your fingers won’t swing while you walk. Your speech can also additionally grow to be gentle or slurred. PD signs and symptoms get worse as your circumstance progresses over time. The goal of this study is to test the efficiency of deep learning and machine learning approaches in order to identify the most accurate strategy for sensing Parkinson’s disease at an early stage. In order to measure the average performance most accurately, we compared deep learning and machine learning methods.

scholarly journals Novel computational deep learning strategy for neuroprotection identification reveals unique set of nicotine analogs as potential therapeutic compounds against Parkinson’s disease

2019 ◽  
Author(s):  
Felipe Rojas-Rodríguez ◽  
Carlos Morantes ◽  
Andrés Pinzón ◽  
George E. Barreto ◽  
Ricardo Cabezas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Neurodegenerative Diseases ◽  
Signaling Pathways ◽  
Neuroprotective Activity ◽  
Nicotine Analogs ◽  
Α7 Nachrs ◽  
Computational Strategy

AbstractDopaminergic replacement has been used for Parkinson’s Disease (PD) treatment with positive effects on motor symptomatology but with low effects over disease progression and prevention. Different epidemiological studies have shown that nicotine consumption decreases PD prevalence through the activation of neuroprotective mechanisms. Nicotine-induced neuroprotection has been associated with the overstimulation of intracellular signaling pathways (SP) such as Phosphatidyl Inositol 3-kinase/Protein kinase-B (PI3K/AKT) through nicotinic acetylcholine receptors (e.g α7 nAChRs) and the over-expression of the anti-apoptotic gene Bcl-2. Considering its harmful effects (toxicity and dependency), the search for nicotine analogs with decreased secondary effects, but similar neuroprotective activity, remains a promissory field of study. In this work, a computational strategy integrating structural bioinformatics, signaling pathway (SP) manual reconstruction, and deep learning was performed to predict the potential neuroprotective activity of a series of 8 novel nicotine analogs over the behavior of PI3K/AKT. We performed a protein-ligand analysis between nicotine analogs and α7 nAChRs receptor using geometrical conformers, physicochemical characterization of the analogs and developed a manually curated neuroprotective dataset to analyze their potential activity. Additionally, we developed a predictive machine-learning model for neuroprotection in PD through the integration of Markov Chain Monte-Carlo transition matrix for the SP with synthetic training datasets of the physicochemical properties and structural dataset. Our model was able to predict the potential neuroprotective activity of seven new nicotine analogs based on the binomial Bcl-2 response regulated by the activation of PI3K/AKT. We present a new computational strategy to predict the pharmacological neuroprotective potential of nicotine analogs based on SP architecture, using deep learning and structural data. Our theoretical strategy can be further applied to the study new treatments related with SP deregulation and may ultimately offer new opportunities for therapeutic interventions in neurodegenerative diseases.Author SummaryParkinson’s disease is one of the most prevalent neurodegenerative diseases across population over age 50. Affecting controlled movements and non-motor symptoms, treatments for Parkinson prevention are indispensable to reduce patient’s population in the future. Epidemiological data provide evidence that nicotine have a neuroprotective effect decreasing Parkinson prevalence. By interacting with nicotine receptors in neurons and modulating signaling pathways expressing anti-apoptotic genes nicotine arise as a putative neuroprotective therapy. Nevertheless, toxicity and dependency prevent the use of nicotine as a suitable drug. Nicotine analogs, structurally similar compounds emerge as an alternative for Parkinson preventive treatment. In this sense we developed a quantitative strategy to predict the potential neuroprotective activity of nicotine analogs. Our model is the first approach to predict neuroprotection in the context of Parkinson and signaling pathways using machine learning and computational chemistry.

scholarly journals A mobile-assisted voice condition analysis system for Parkinson’s disease: assessment of usability conditions

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Javier Carrón ◽  
Yolanda Campos-Roca ◽  
Mario Madruga ◽  
Carlos J. Pérez
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Assessment ◽  
Accuracy Rate ◽  
Additional Tool ◽  
Professional Supervision ◽  
Controlled Conditions ◽  
Analysis System ◽  
The Impact

Abstract Background and objective Automatic voice condition analysis systems to detect Parkinson’s disease (PD) are generally based on speech data recorded under acoustically controlled conditions and professional supervision. The performance of these approaches in a free-living scenario is unknown. The aim of this research is to investigate the impact of uncontrolled conditions (realistic acoustic environment and lack of supervision) on the performance of automatic PD detection systems based on speech. Methods A mobile-assisted voice condition analysis system is proposed to aid in the detection of PD using speech. The system is based on a server–client architecture. In the server, feature extraction and machine learning algorithms are designed and implemented to discriminate subjects with PD from healthy ones. The Android app allows patients to submit phonations and physicians to check the complete record of every patient. Six different machine learning classifiers are applied to compare their performance on two different speech databases. One of them is an in-house database (UEX database), collected under professional supervision by using the same Android-based smartphone in the same room, whereas the other one is an age, sex and health-status balanced subset of mPower study for PD, which provides real-world data. By applying identical methodology, single-database experiments have been performed on each database, and also cross-database tests. Cross-validation has been applied to assess generalization performance and hypothesis tests have been used to report statistically significant differences. Results In the single-database experiments, a best accuracy rate of 0.92 (AUC = 0.98) has been obtained on UEX database, while a considerably lower best accuracy rate of 0.71 (AUC = 0.76) has been achieved using the mPower-based database. The cross-database tests provided very degraded accuracy metrics. Conclusion The results clearly show the potential of the proposed system as an aid for general practitioners to conduct triage or an additional tool for neurologists to perform diagnosis. However, due to the performance degradation observed using data from mPower study, semi-controlled conditions are encouraged, i.e., voices recorded at home by the patients themselves following a strict recording protocol and control of the information about patients by the medical doctor at charge.

scholarly journals Role of Wearable Sensors with Machine Learning Approaches in Gait Analysis for Parkinson's Disease Assessment: A Review

2022 ◽  
Author(s):  
Aishwarya Balakrishnan ◽  
◽  
Jeevan Medikonda ◽  
Pramod Kesavan Namboothiri ◽  
Manikandan Natarajan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gait Analysis ◽  
Wearable Sensors ◽  
Disease Assessment ◽  
Learning Approaches

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification
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