Literature DB >> 21095695

Automated Levodopa-induced dyskinesia assessment.

Markos G Tsipouras1, Alexandros T Tzallas, Georgios Rigas, Panagiota Bougia, Dimitrios I Fotiadis, Spyridon Konitsiotis.   

Abstract

An automated methodology for Levodopa-induced dyskinesia (LID) assessment is presented in this paper. The methodology is based on the analysis of the signals recorded from accelerometers and gyroscopes, which are placed on certain positions on the subject's body. The obtained signals are analyzed and several features are extracted. Based on these features a classification technique is used for LID detection and classification of its severity. The method has been evaluated using a group of 10 subjects. Results are presented related to each individual sensor as well as for various sensor combinations. The obtained results indicate high classification ability (93.73% classification accuracy).

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Year:  2010        PMID: 21095695     DOI: 10.1109/IEMBS.2010.5626130

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  6 in total

1.  Continuous Assessment of Levodopa Response in Parkinson's Disease Using Wearable Motion Sensors.

Authors:  Christopher L Pulliam; Dustin A Heldman; Elizabeth B Brokaw; Thomas O Mera; Zoltan K Mari; Michelle A Burack
Journal:  IEEE Trans Biomed Eng       Date:  2017-04-25       Impact factor: 4.538

2.  Wearability assessment of a wearable system for Parkinson's disease remote monitoring based on a body area network of sensors.

Authors:  Jorge Cancela; Matteo Pastorino; Alexandros T Tzallas; Markos G Tsipouras; Giorgios Rigas; Maria T Arredondo; Dimitrios I Fotiadis
Journal:  Sensors (Basel)       Date:  2014-09-16       Impact factor: 3.576

3.  Using the Analytic Hierarchy Process (AHP) to understand the most important factors to design and evaluate a telehealth system for Parkinson's disease.

Authors:  Jorge Cancela; Giuseppe Fico; Maria T Arredondo Waldmeyer
Journal:  BMC Med Inform Decis Mak       Date:  2015-09-04       Impact factor: 2.796

4.  Assessing Motor Fluctuations in Parkinson's Disease Patients Based on a Single Inertial Sensor.

Authors:  Carlos Pérez-López; Albert Samà; Daniel Rodríguez-Martín; Andreu Català; Joan Cabestany; Juan Manuel Moreno-Arostegui; Eva de Mingo; Alejandro Rodríguez-Molinero
Journal:  Sensors (Basel)       Date:  2016-12-15       Impact factor: 3.576

Review 5.  Optimizing Clinical Assessments in Parkinson's Disease Through the Use of Wearable Sensors and Data Driven Modeling.

Authors:  Ritesh A Ramdhani; Anahita Khojandi; Oleg Shylo; Brian H Kopell
Journal:  Front Comput Neurosci       Date:  2018-09-11       Impact factor: 2.380

6.  Improving Medication Regimen Recommendation for Parkinson's Disease Using Sensor Technology.

Authors:  Jeremy Watts; Anahita Khojandi; Rama Vasudevan; Fatta B Nahab; Ritesh A Ramdhani
Journal:  Sensors (Basel)       Date:  2021-05-20       Impact factor: 3.576

  6 in total

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