.. _module_spindledetectorsumo: Spindle Detector Sumo ===================== **Module name:** ``SpindleDetectorSumo`` **Package:** CEAMSModules 7.4.0 **Version:** 3.0.0 Overview -------- Class to detect spindles based on the SUMO deep learning algorithm Inputs ------ .. list-table:: :widths: 25 20 15 50 :header-rows: 1 :align: left :class: left-align-caption wrap-table * - Input - Format - Default - Description * - ``signals`` - List of SignalModel - — - | List of signal with dictionary of channels with SignalModel with | properties : | name: The name of the channel | samples: The samples of the signal | alias: The alias of the channel | sample_rate: The sample rate of the signal | start_time: The start time of the recording | montage_index: The index of the montage used for this signal | is_modified: Value caracterizing if the signal as been modify | from the original * - ``event_group`` - String - — - List of Event group to filter separated by comma (discard too long, short) * - ``event_name`` - String - — - List of Event name to filter separated by comma (discard too long, short) Outputs ------- .. list-table:: :widths: 25 20 65 :header-rows: 1 :align: left :class: left-align-caption wrap-table * - Output - Format - Description * - ``events`` - Pandas DataFrame (columns=['group','name','start_sec','duration_sec','channels']) - Events list for spindle detections. Usage in a process ------------------ 1. Open **Dev Tools -> New process** in Snooz. 2. In the Module Library, find **Spindle Detector Sumo** 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 :doc:`/dev_guide/explore_ex`.