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Fig 1.

Main GUI window.

The signal organization window can be used to change the order of signals (top left). This window is opened from the signal selection window. Also shown are the filtering and image saving windows. The main window includes a leftside-panel with various options and the main signal plot to the right.

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Fig 1 Expand

Fig 2.

Visualizing predictions.

Model predictions are shown in light cyan across the channels. Since multi-channel predictions are plotted, topoplots have been generated.

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Fig 2 Expand

Fig 3.

Annotation editor and statistics.

The dock on the right side of the plot contains the annotation editor (top) and signal statistics (bottom). The red rectangle on the main plot can be used to select a region over which statistics will be computed.

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Fig 3 Expand

Fig 4.

Anonymization window.

This option can be used to strip identifiable information from the EDF file prior to saving.

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Fig 4 Expand

Table 1.

Unit testing code coverage for EPViz source files.

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Table 1 Expand

Fig 5.

UWM dataset recording times.

Total time in minutes of EEG included for each patient in the UWM dataset.

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Fig 5 Expand

Fig 6.

Seizure detection performance on the UWM dataset.

Box plots capture the average performance for each metric across the 16 patients. The orange bar denotes the median performance, boxes correspond to the inter-quartile range, and the whiskers denote the 10th and 90th percentiles. Top: Window level results are calculated on one-second windows of EEG. Bottom: Seizure level results are calculated over the duration of the seizure period.

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Fig 6 Expand

Fig 7.

EPViz visualizations of seizure detections from each model.

Detected seizures are shown superimposed in blue on the EEG. Annotated seizure onset at 739 seconds is shown by the vertical dashed line. The CNN-BLSTM shows a contiguous seizure detection with low latency while alternative models show higher latency, non-continuous detection, or misses.

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Fig 7 Expand

Table 2.

Summary of features provided by EPViz, as compared to existing EEG software.

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Table 2 Expand