Threshold Computation

Module name: ThresholdComputation

Package: CEAMSModules 7.4.0

Version: 2.1.0

Overview

To compute the value to threshold (i.e. µV) from a signals (i.e. EEG time series) based on a threshold definition (i.e. 4) and a metric or a unit (i.e. standard deviation).

Inputs

Input	Format	Default	Description
`signals`	dict of SignalModel, the key is the label of the channel.	—	signals[channel_label].samples : The actual signal data as numpy list
`cycle_events`	pandas DataFrame	—	Events (columns=[‘group’,’name’,’start_sec’,’duration_sec’,’channels’]) Sleep cycles are defined with the ‘group’ cycle and the ‘name’ nremp or remp
`threshold_definition`	float	—	threshold definition to compute the value from the signals
`threshold_metric`	String	standard deviation	Metric (unit) used for thresholding. “percentile”, “standard deviation”, “variance” or “median”
`threshold_scope`	String	0	‘0’ to compute a threshold per item of signals ‘1’ to compute a threshold per sleep cycle and channel ‘2’ to compute a threshold per channel (through all signals).

Outputs

Output	Format	Description
`threshold_value`	list of float	The value to threshold for each signal included in signals. (Can vary for each item of signals depending of the threshold_scope.)

Usage in a process

Open Dev Tools -> New process in Snooz.
In the Module Library, find Threshold Computation under the Statistics category.
Drag the module onto the process canvas.
Connect the required inputs from upstream modules (or set values in the Settings tab).
Connect outputs to downstream modules as needed.
Double-click the module to configure parameters in the Settings tab.
Run the process and inspect results in the Results tab.

Note

For general guidance on building processes with modules, see Explore examples.