title: DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.
abstract: 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.
authors: Department of Computer Science, University of Texas-San Antonio, San Antonio, Texas, United States of America. Vernon.Lawhern@utsa.edu
authors: Vernon
authors: V
authors: Lawhern
authors: W David
authors: WD
authors: Hairston
authors: Kay
authors: K
authors: Robbins
mesh_terms: D016477:Artifacts; D004569:Electroencephalography / Q000592:standards*; D006801:Humans; D008490:Medical Informatics; D015233:Models, Statistical; D015203:Reproducibility of Results; D060388:Support Vector Machine; D014584:User-Computer Interface
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