Abstract
AbstractHere we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test.
Publisher
Springer Science and Business Media LLC
Subject
Cellular and Molecular Neuroscience,Neurology (clinical),Neurology
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