Spectral Detector

Module name: SpectralDetector

Package: CEAMSModules 7.4.0

Version: 2.2.0

Overview

This plugin detects events based on the spectrum. The plugin is flexible, an event can be detected when power goes above or below the threshold. The threshold can be fixed (as a uV^2 value) , adaptive based on a baseline window around the event (x BSL median) or adaptif based on the epochs of the recording (x epochs STD). The adaptive threshold can be x times the baseline median value or x times the standard deviation of the baseline. When a z-score is used as threshold (x BSL STD log10), the power values are log10 transformed to make them more normally distributed.

Inputs

Input

Format

Default

Description

psds

dict

—
The power spectral density of all windows.
psd : power (µV^2) narray [n_fft_windows x n_frequency_bins]
freq_bins : frequency bins (Hz); The frequencies of the bins within the psds
win_len : windows length (s); The length of the detection windows on which the psds was done (in seconds)
win_step : windows step (s); The step between the detection windows on which the psds was done (in seconds)
sample_rate : sampling rate of the original signal (Hz)
chan_label : channel label
start_time : start (s) of the signal (item of signals) on which the ffts are performed
end_time : end (s) of the signal (item of signals) on which the ffts are performed
duration : duraiton (s) of the signal (item of signals) on which the ffts are performed

event_group

string

—

Event group.

event_name

string

—

Event label.

low_freq

string

0.5

The lower frequency of the bandwidth targeted by the detection

high_freq

string

30

The higher frequency of the bandwidth targeted by the detection

rel_freq

string

0
‘0’ the frequency band is absolute (low_freq to high_freq)
‘1’ the frequency band is relative (absolute band / background band)

bsl_low_freq

string

0.5

The lower frequency of the baseline used to compute the relative power.

bsl_high_freq

string

30

The higher frequency of the baseline used to compute the relative power.

threshold_val

string or a list of float

—
String : the value to threshold to detect events.
list : the value to threshold for each signal included in signals.

threshold_unit

string

fixed
The threshold unit
-fxed
-x BSL median, x BSL STD or x BSL STD(log10)
-x epochs STD, x epochs STD(log10)

threshold_behavior

string

Above
Above : Event is detected when activity goes above the threshold.
Below : Event is detected when activity goes below the threshold.

sleep_stages

pandas dataframe (columns=[‘group’,’name’,’start_sec’,’duration_sec’,’channels’])

—

(optional) Sleep stages list.

baseline_win_len

string

—

(optional) The baseline window length in seconds

art_events

Pandas DataFrame (columns=[‘group’,’name’,’start_sec’,’duration_sec’,’channels’])

—

(optional) Artefact events previously detected

channel_dbg

String

—

Channel label to save and exit detection info.

Outputs

Output

Format

Description

events

Pandas DataFrame

DataFrame events (columns=[‘group’,’name’,’start_sec’,’duration_sec’,’channels’])

win_activity

ndarray of n_windows

Spectral power in the frequency bins from low_freq to high_freq.

win_bsl

ndarray of n_windows

(Optional) MedianSTD spectral power of the baseline window (low_freq to high_freq).

Usage in a process

  1. Open Dev Tools -> New process in Snooz.

  2. In the Module Library, find Spectral Detector under the Detectors category.

  3. Drag the module onto the process canvas.

  4. Connect the required inputs from upstream modules (or set values in the Settings tab).

  5. Connect outputs to downstream modules as needed.

  6. Double-click the module to configure parameters in the Settings tab.

  7. Run the process and inspect results in the Results tab.

Note

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