Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

PMID: 31744673

Abstract

OBJECTIVE: Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD.

Authors

Lin Yao

School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
Peter Brown

Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Mahsa Shoaran

School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.

Keywords

Aged Algorithms Analysis of Variance Deep Brain Stimulation Female Humans Machine Learning Male Middle Aged Parkinson Disease Reaction Time Rest Subthalamic Nucleus Tremor Wavelet Analysis

External Resources

View on PubMed Access via DOI PubMed (31744673)

Improved detection of Parkinsonian resting tremor with feature engineering and Kalman filtering.

Abstract

Authors

Keywords

External Resources

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