Slow Waves Details

Module name: SlowWavesDetails

Package: CEAMSModules 7.4.0

Version: 2.2.0

Overview

To average slow wave events characteristics such as duration, amplitude, frequency and so on per stage and sleep cycle.

Inputs

Input	Format	Default	Description
recording_path	string	—	The recording path.
subject_info	dict	—	filename : Recording filename without path and extension. id1 : Identification 1 id2 : Identification 2 first_name : first name of the subject recorded last_name : last name of the subject recorded sex : …
signals	a list of SignalModel	—	Each item of the list is a SignalModel object as described below: signal.samples : The actual signal data as numpy list signal.sample_rate : the sampling rate of the signal signal.channel : current channel label signal.start_time : The start time of the signal in sec signal.end_time : The end time of the signal in sec (for more info : look into common/SignalModel)
sw_events_details	Pandas DataFrame	—	Slow wave events defined as (columns=[‘group’, ‘name’, ‘start_sec’,’pkpk_amp_uV’,’neg_amp_uV’,’neg_sec’,’pos_sec’,’Pap_raw’,’Neg_raw’,’mfr’,’trans_freq_Hz’, ‘channels’])
artifact_events	Pandas DataFrame	—	Artifact events defined as (columns=[‘group’, ‘name’,’start_sec’,’duration_sec’,’channels’]) Artifacts are forced to zeros for the detection (with a tukey window)
sleep_cycle_param	Dict	—	Options used to define the cycles “{ ‘defined_option’:’Minimum Criteria’ ‘Include_SOREMP’ : ‘1’ ‘Include_last_incompl’ : ‘1’ ‘Include_all_incompl: : ‘1’ ‘dur_ends_REMP’ = ‘15’ ‘NREM_min_len_first’:’0’ ‘NREM_min_len_mid_last’:’15’ ‘NREM_min_len_val_last’:’0’ ‘REM_min_len_first’:’0’ ‘REM_min_len_mid’:’0’ ‘REM_min_len_last’:’0’ ‘mv_end_REMP’:’0’ ‘sleep_stages’:’N1, N2, N3, N4, R’ ‘details’: ‘<p>Adjust options based on minimum criteria.</p> }”
stages_cycles	Pandas DataFrame	—	Events defined as (columns=[‘group’, ‘name’,’start_sec’,’duration_sec’,’channels’]) The sleep stage group has to be commons.sleep_stage_group “stage” and the sleep cycle group has to be commons.sleep_cycle_group “cycle”.
slow_wave_det_param	Dict	—	stage_sel : Sleep stages selection to detect slow waves in. detect_excl_remp : Flag to exclude rem period from the spindle detection. sw_event_name : String label of the event name filt_low_hz : Low frequency bandpass filter (Hz) filt_high_hz : High frequency bandpass filter (Hz) min_amp_pk-pk_uV : minimum peak-to-peak amplitude (uV) min_neg_amp_uV : minimum negative amplitude (uV) min_dur_neg_ms : minimum duration of negative part of the slow wave (ms) max_dur_neg_ms : maximum duration of negative part of the slow wave (ms) min_dur_pos_ms : minimum duration of positive part of the slow wave (ms) max_dur_pos_ms : maximum duration of positive part of the slow wave (ms)
report_constants	dict	{}	Constants used in the report (N_HOURS, N_CYCLES)
cohort_filename	string	—	Path and filename to save the slow wave characteristics for the cohort.
export_slow_wave	bool or string	False	True : generate a file per subject of the characteristics of each slow wave.

Outputs

This module has no outputs.

Usage in a process

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

2. In the Module Library, find Slow Waves Details under the Events Utilities 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.