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Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs

An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint i...

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Autores principales: Sommer, Werner, Stapor, Katarzyna, Kończak, Grzegorz, Kotowski, Krzysztof, Fabian, Piotr, Ochab, Jeremi, Bereś, Anna, Ślusarczyk, Grażyna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139177/
https://www.ncbi.nlm.nih.gov/pubmed/35624912
http://dx.doi.org/10.3390/brainsci12050525
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author Sommer, Werner
Stapor, Katarzyna
Kończak, Grzegorz
Kotowski, Krzysztof
Fabian, Piotr
Ochab, Jeremi
Bereś, Anna
Ślusarczyk, Grażyna
author_facet Sommer, Werner
Stapor, Katarzyna
Kończak, Grzegorz
Kotowski, Krzysztof
Fabian, Piotr
Ochab, Jeremi
Bereś, Anna
Ślusarczyk, Grażyna
author_sort Sommer, Werner
collection PubMed
description An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM). The optimal changepoint in RESPERM maximizes Cohen’s effect size with the parameters estimated by the permutation of residuals in a linear model. RESPERM was compared with the SEGMENTED method, a well-established and recommended method for detecting changepoints, using extensive simulated data sets, varying the amount and distribution characteristics of noise and the location of the change point. In time series with medium to large amounts of noise, the variance of the detected changepoint was consistently smaller for RESPERM than SEGMENTED. Finally, both methods were applied to a sample dataset of single trial amplitudes of the N250 ERP component during face learning. In conclusion, RESPERM appears to be well suited for changepoint detection especially in noisy data, making it the method of choice in neuroscience, medicine and many other fields.
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spelling pubmed-91391772022-05-28 Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs Sommer, Werner Stapor, Katarzyna Kończak, Grzegorz Kotowski, Krzysztof Fabian, Piotr Ochab, Jeremi Bereś, Anna Ślusarczyk, Grażyna Brain Sci Article An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM). The optimal changepoint in RESPERM maximizes Cohen’s effect size with the parameters estimated by the permutation of residuals in a linear model. RESPERM was compared with the SEGMENTED method, a well-established and recommended method for detecting changepoints, using extensive simulated data sets, varying the amount and distribution characteristics of noise and the location of the change point. In time series with medium to large amounts of noise, the variance of the detected changepoint was consistently smaller for RESPERM than SEGMENTED. Finally, both methods were applied to a sample dataset of single trial amplitudes of the N250 ERP component during face learning. In conclusion, RESPERM appears to be well suited for changepoint detection especially in noisy data, making it the method of choice in neuroscience, medicine and many other fields. MDPI 2022-04-21 /pmc/articles/PMC9139177/ /pubmed/35624912 http://dx.doi.org/10.3390/brainsci12050525 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sommer, Werner
Stapor, Katarzyna
Kończak, Grzegorz
Kotowski, Krzysztof
Fabian, Piotr
Ochab, Jeremi
Bereś, Anna
Ślusarczyk, Grażyna
Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs
title Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs
title_full Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs
title_fullStr Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs
title_full_unstemmed Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs
title_short Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs
title_sort changepoint detection in noisy data using a novel residuals permutation-based method (resperm): benchmarking and application to single trial erps
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139177/
https://www.ncbi.nlm.nih.gov/pubmed/35624912
http://dx.doi.org/10.3390/brainsci12050525
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