Slow Wave Classifier

Module name: SlowWaveClassifier

Package: CEAMSModules 7.4.0

Version: 2.0.0

Overview

This plugin classifies slow wave events based on a gaussian mixture.

Note

It selects the best gaussian fit based on Akaike information criterion (AIC) and passes those informations to a KMean function which then seperates each detected slow wave into n categories, where n is determined by the best gaussian fit. Values of n range between 1 and 4. Categorized slow waves are then saved in cache along with data for each slow wave criterion.

Inputs

Input

Format

Default

Description

sw_char_files

list of strings

[]
List of filename to load events_details.
events_details are pandas DataFrames (columns=[‘group’, ‘name’, ‘cycle’, ‘stage’,
‘start_sec’, ‘duration_sec’, ‘pkpk_amp_uV’, ‘freq_Hz’, ‘neg_amp_uV’, ‘neg_sec’, ‘pos_sec’,|
‘slope_0_min’, ‘slope_min_max’, ‘slope_max_0’, ‘trans_freq_Hz’, ‘channels’])

sw_cohort_file

string

—

File containing the slow wave cohort information generated by the slow wave detector.

sw_stages_files

list of strings

—

List of filename to load sleep stages.

automatic_classification

boolean

1
The user specifies whether they want to specify the number of categories or not.
Usually selected (True) for a group classification. Unselect (False)
when the user inputs an individual of the same group in a second round
of classification to have more details.

num_categories

int

2

The number of categories of sleep slow waves to analyse.

num_divisions

int

2

The number of divisions in the recording length to divide the night into.

report_constants

dict

{‘N_HOURS’: 9, ‘N_CYCLES’: 6}

Constants used in the report (N_HOURS, N_CYCLES)

output_dir

string

—

The directory where the results are saved.

Outputs

Output

Format

Description

categorized_data

pandas DataFrame
DataFrame events (columns=[‘category’,’n_t’,’PaP’,’Neg’, ‘tNe’, ‘tPo’, ‘Pap_raw’, ‘Neg_raw’, ‘mfr’, ‘trans_freq_Hz’])
containing data analysis of parameters for each slow wave category found

data_details

Pandas DataFrame
DataFrame containing a column for each category of sleep slow waves
found. The indexes describe what the data is about.

distr_time

Pandas DataFrame
DataFrame events showing the number of ssw in each category accros each
the entire night.

distr_cycle

Pandas DataFrame

DataFrame events showing the mean value for each quarter of the night

distr_quarter

Pandas DataFrame
DataFrame events showing the number of ssw in each category accros each
cycle.

Usage in a process

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

  2. In the Module Library, find Slow Wave Classifier under the Statistics 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.