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A versatile computational algorithm for time-series data analysis and machine-learning models
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DOI：10.1038/s41531-021-00240-4.png)
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Abstract
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Here 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.
Keywords:
RECURRENCE PLOTS
CLASSIFICATION
NETWORK
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