Suppression of neurostimulation artifacts and adaptive clustering of Parkinson's patients behavioral tasks using EEG

A. Maurer; S. Hanrahan; J. Nedrud; A. O. Hebb; Antonia Papandreou-Suppappola

doi:10.1109/ACSSC.2016.7869169

Suppression of neurostimulation artifacts and adaptive clustering of Parkinson's patients behavioral tasks using EEG

A. Maurer, S. Hanrahan, J. Nedrud, A. O. Hebb, Antonia Papandreou-Suppappola

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

Biological and biomedical signals, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters to improve treatment. This work proposes advanced statistical signal processing and machine learning methodologies to assess neurostimulation from neural recordings. This is done using adaptive processing and unsupervised clustering methods applied to neural recordings to suppress neurostimulation artifacts and classify between various behavior tasks to assess the level of neurostimulation in patients.

Original language	English (US)
Title of host publication	Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Publisher	IEEE Computer Society
Pages	851-855
Number of pages	5
ISBN (Electronic)	9781538639542
DOIs	https://doi.org/10.1109/ACSSC.2016.7869169
State	Published - Mar 1 2017
Event	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 - Pacific Grove, United States Duration: Nov 6 2016 → Nov 9 2016

Other

Other	50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016
Country/Territory	United States
City	Pacific Grove
Period	11/6/16 → 11/9/16

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

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10.1109/ACSSC.2016.7869169

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Maurer, A., Hanrahan, S., Nedrud, J., Hebb, A. O., & Papandreou-Suppappola, A. (2017). Suppression of neurostimulation artifacts and adaptive clustering of Parkinson's patients behavioral tasks using EEG. In Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016 (pp. 851-855). Article 7869169 IEEE Computer Society. https://doi.org/10.1109/ACSSC.2016.7869169

Suppression of neurostimulation artifacts and adaptive clustering of Parkinson's patients behavioral tasks using EEG. / Maurer, A.; Hanrahan, S.; Nedrud, J. et al.
Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016. IEEE Computer Society, 2017. p. 851-855 7869169.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Maurer, A, Hanrahan, S, Nedrud, J, Hebb, AO & Papandreou-Suppappola, A 2017, Suppression of neurostimulation artifacts and adaptive clustering of Parkinson's patients behavioral tasks using EEG. in Conference Record of the 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016., 7869169, IEEE Computer Society, pp. 851-855, 50th Asilomar Conference on Signals, Systems and Computers, ACSSC 2016, Pacific Grove, United States, 11/6/16. https://doi.org/10.1109/ACSSC.2016.7869169

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abstract = "Biological and biomedical signals, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters to improve treatment. This work proposes advanced statistical signal processing and machine learning methodologies to assess neurostimulation from neural recordings. This is done using adaptive processing and unsupervised clustering methods applied to neural recordings to suppress neurostimulation artifacts and classify between various behavior tasks to assess the level of neurostimulation in patients.",

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AB - Biological and biomedical signals, when adequately analyzed and processed, can be used to impart quantitative diagnosis during primary health care consultation to improve patient adherence to recommended treatments. For example, analyzing neural recordings from neurostimulators implanted in patients with neurological disorders can be used by a physician to adjust detrimental stimulation parameters to improve treatment. This work proposes advanced statistical signal processing and machine learning methodologies to assess neurostimulation from neural recordings. This is done using adaptive processing and unsupervised clustering methods applied to neural recordings to suppress neurostimulation artifacts and classify between various behavior tasks to assess the level of neurostimulation in patients.

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Suppression of neurostimulation artifacts and adaptive clustering of Parkinson's patients behavioral tasks using EEG

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