Key result
The DETECT MATLAB toolbox achieved over 97% agreement with manual expert labeling for identifying artifacts in relatively clean EEG datasets.
The DETECT MATLAB toolbox provides an accessible, automated method for researchers to detect and identify events, such as artifacts in EEG or irregular heartbeats in ECG, in continuous multi-channel time series data.
Facilitates automated artifact detection in clean EEG/ECG time series for research; leaves open validation in noisy clinical datasets.
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.
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Lawhern et al. (2013) studied EEG artifacts (n=3). DETECT MATLAB Toolbox vs. Manual labeling by human experts was evaluated on Agreement between automated DETECT labeling and manual user labeling. The DETECT MATLAB toolbox achieved over 97% agreement with manual expert labeling for identifying artifacts in relatively clean EEG datasets.
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