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Using machine learning and accelerometry data for differential diagnosis of Parkinson’s disease and essential tremor

Author
Loaiza , J.; González Vargas, A.; Sanchez Egea, Antonio J.; Gonzalez-Rojas, Hernan A.
Type of activity
Book chapter
Book
Workshop on Engineering Applications
First page
368
Last page
378
Publisher
Springer
Date of publication
2019-10-09
ISBN
978-3-030-31019-6 Open in new window
DOI
10.1007/978-3-030-31019-6_32
Repository
http://hdl.handle.net/2117/174098 Open in new window
URL
https://link.springer.com/chapter/10.1007%2F978-3-030-31019-6_32 Open in new window
Abstract
Parkinson’s disease (PD) and Essential Tremor (ET) are the most common tremor syndromes in the world. Currently, a specific Single Photon Emission Computed Tomography (123I-FP-CIT SPECT) has proven to be an effective tool for the diagnosis of these diseases (97% sensitivity and 100% specificity). However, this test is invasive and expensive, and not all countries can have a SPECT system for an accurate differential diagnosis of PD patients. Clinical evaluation by a neurologist remains the gold...
Citation
Loaiza , J. [et al.]. Using machine learning and accelerometry data for differential diagnosis of Parkinson’s disease and essential tremor. A: "Workshop on Engineering Applications". Berlín: Springer, 2019, p. 368-378.
Keywords
Accelerometry, Essential Tremor, Machine Learning, Parkinson’s Disease, Wearable device
Group of research
GAECE - Electronically Commutated Motor Drives Group
TECNOFAB - Manufacturing Technologies Research Group

Participants