DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have devel...

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Autores principales:	Vernon Lawhern, W David Hairston, Kay Robbins
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0f14a9067d7b4a639589b0d82c992d23
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id	oai:doaj.org-article:0f14a9067d7b4a639589b0d82c992d23
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spelling	oai:doaj.org-article:0f14a9067d7b4a639589b0d82c992d232021-11-18T07:47:55ZDETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.1932-620310.1371/journal.pone.0062944https://doaj.org/article/0f14a9067d7b4a639589b0d82c992d232013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23638169/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.Vernon LawhernW David HairstonKay RobbinsPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 4, p e62944 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vernon Lawhern W David Hairston Kay Robbins DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.
description	Recent advances in sensor and recording technology have allowed scientists to acquire very large time-series datasets. Researchers often analyze these datasets in the context of events, which are intervals of time where the properties of the signal change relative to a baseline signal. We have developed DETECT, a MATLAB toolbox for detecting event time intervals in long, multi-channel time series. Our primary goal is to produce a toolbox that is simple for researchers to use, allowing them to quickly train a model on multiple classes of events, assess the accuracy of the model, and determine how closely the results agree with their own manual identification of events without requiring extensive programming knowledge or machine learning experience. As an illustration, we discuss application of the DETECT toolbox for detecting signal artifacts found in continuous multi-channel EEG recordings and show the functionality of the tools found in the toolbox. We also discuss the application of DETECT for identifying irregular heartbeat waveforms found in electrocardiogram (ECG) data as an additional illustration.
format	article
author	Vernon Lawhern W David Hairston Kay Robbins
author_facet	Vernon Lawhern W David Hairston Kay Robbins
author_sort	Vernon Lawhern
title	DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.
title_short	DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.
title_full	DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.
title_fullStr	DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.
title_full_unstemmed	DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.
title_sort	detect: a matlab toolbox for event detection and identification in time series, with applications to artifact detection in eeg signals.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/0f14a9067d7b4a639589b0d82c992d23
work_keys_str_mv	AT vernonlawhern detectamatlabtoolboxforeventdetectionandidentificationintimeserieswithapplicationstoartifactdetectionineegsignals AT wdavidhairston detectamatlabtoolboxforeventdetectionandidentificationintimeserieswithapplicationstoartifactdetectionineegsignals AT kayrobbins detectamatlabtoolboxforeventdetectionandidentificationintimeserieswithapplicationstoartifactdetectionineegsignals
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DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

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