Using LASA Excel exports and pivot tables
For information on how to format and download your Excel table from the LASA software, please consult this article.
Data Structure: The "All" tab centralizes your measurements. This file does not contain raw data (ECG and RIP signals) but rather the data you have formatted and analyzed using LASA (Calculated Physiological Parameters). Associated metadata is also available.
Pivot Tables: The essential Excel tool for segmenting and synthesizing your results. Master the different panes and fields (Rows, Columns, Values) to isolate and compare your data.
Iterative Approach: Analysis in Excel is not an end in itself. It allows you to identify trends and validate the detection quality of your signals. If necessary, return to LASA to refine algorithm detection (Re-detect) or specific time windows (Timeframes) before re-running the export.
Flexibility: Whether comparing groups, tracking temporal evolution, or detecting outliers, Pivot Tables offer total flexibility. Do not hesitate to enrich your "All" sheet manually (e.g., adding a condition column) to adapt the analysis to your specific needs.
1. Getting Started: Opening and Saving the File
Extract the .xlsx file contained in the archive.
Figure 1: Opening the export file from the compressed archive.
Open the file (named according to the export date and your study name).
Figure 2: Opening the Excel file.
Click 'Enable Editing' and then save the file to your desired location.
Figure 3: Enabling editing in Excel.
2. Understanding Your Export File Structure
Figure 4: Overview of the All, Metas, and Markers tabs.
All: This is the heart of your file. It gathers all the measurement points of the analysis, including physiological variables and session characteristics.
Metas: Contains general acquisition information (session name, phase, recorded subjects, transmitters used).
Markers: Lists the characteristics of event markers applied during recordings (name, timestamp, session).
Protocol (Study): Summarizes the source study properties.
Context Columns
Essential time variables are also found here: Second (data synchronized to a specific T0) and Second raw (total duration elapsed since the start of the session).
Row Organization and Structure
The sorting is done in chronological order for each individual: you will find all results from Subject 1's session before moving on to Subject 2, etc.
Physiological Parameter Columns
The DECRO vest simultaneously records several vital functions and automatically calculates the corresponding parameters:
Cardiac & Variability Parameters (ECG): R-R interval between two beats (R-Ri_analyse), total duration of the ventricular complex (QRSi_analyse).
Respiratory Parameters (RIP Plethysmography): Respiratory rate (RR_analyse), raw and calibrated tidal volume (VT_analyse / VT_cal_analyse), minute ventilation (MV_cal_analyse), inspiratory/expiratory time (IT_analyse / ET_analyse), etc.
Actimetry: Global activity level measured by accelerometer (AL_analyse).
Advanced ECG Morphology: Fine segmentation of electrical waves P, Q, R, S, T in terms of amplitude, duration, or area (e.g., Tp-e_analyse for arrhythmia susceptibility).
Figure 5: Preview of columns and data in the "All" sheet.
💡 Methodological Note:
> Raw vs. Calibrated Parameters (_cal_analyse)
For volume and flow variables (VT, MV, PIF, PEF), the export offers two versions:
Raw parameters (e.g., VT_analyse, MV_analyse): Represent relative trunk variation values measured directly by inductive plethysmography (RIP) bands, without animal-related adjustment.
Calibrated parameters (e.g., VT_cal_analyse, MV_cal_analyse): These are corrected values converted into real units (mL or mL/min). They incorporate the RIP Calibration Gain value calculated based on each subject's own weight.
> Parameter Availability in the Export:
If your Excel file was generated from a recording where one of the physiological functions was not detected by the algorithms (e.g., if the cardiac or respiratory modules were not activated in LASA), or if calibration was not performed, the corresponding parameter columns will not appear in your "All" sheet.
If you notice specific variables are missing when building your Pivot Table, return to the LASA software to ensure detection and calibration have been applied and validated for all your sessions before re-exporting.
Metadata columns associated with each parameter
The LASA software systematically generates and associates statistical indicators with each extracted physiological metric.
When initially opening the Excel file, these columns are hidden by default to ensure the readability and clarity of the "All" sheet. However, they remain fully available and viewable in your Pivot Table's field list.
For example, for tidal volume, you will find the pure value (CR_analyse), as well as its standard deviation (CR_analyse std), standard error (CR_analyse sem), signal quality score (CR_analyse score), and unit (CR_analyse unit), alongside other advanced indicators (aggregation method, filters applied, quartiles, and time markers).
3. Creating Your Pivot Table
Find below the detailed method for configuring your first Pivot Table using the data from the All tab.
Step 1: Selecting and preparing the source
Access the All tab in your Excel document.
Select all data. Tip: Use the Ctrl + A shortcut after clicking cell A1 to instantly select the entire table.
Step 2: Setting up the Pivot Table
In the top Excel menu, choose the Insert tab.
Press the PivotTable button.
Verify that the New Worksheet option is enabled in the window that appears.
Confirm by clicking OK. A new sheet opens: the construction area is on the left, and the Field List appears on the right.
Figure 6: Interface for creating a Pivot Table (Excel).
4. Building Your Pivot Table
Here is the complete and detailed protocol to perfectly create and configure your first Pivot Table from the "All" sheet.
Step 3: Understanding and using the Field List
The right-hand side interface appears when you click on the table location. It presents all your columns as well as four destination areas to organize your data:
Values: This is the space dedicated to scientific calculations of your physiological indicators. Simply drag the labels of the physiological parameters you wish to analyze (e.g., CR_analyse).
Columns: This area allows you to juxtapose variables for direct comparison. By placing the Group label here, you separate data by condition (e.g., "Healthy" vs. "Sick"), while the Phase label allows display by time period according to your design.
Rows: This box defines vertical organization. Use it to display temporal evolution via Second, Second raw, or Parameter Analysis Time labels. If left empty, it will display a parameter value calculated over the entire recording period.
Filters: This box allows isolating specific contexts, such as the resting state with the Timeframe label ("Baseline" selection), or targeting specific individuals with Subject.
Note on field organization: depending on the expected visual rendering, it is possible to swap labels between rows and columns.
For illustration:
To visualize the temporal evolution of a parameter by group, the "Group" label will preferably be placed in the Columns.
To obtain a comparison summary table by group for multiple parameters, it can be positioned in the Rows (see examples in part 4).
IMPORTANT: By default, when a physiological parameter (e.g., CR_analyse) is dragged into the Values area, Excel automatically applies the "Sum" function. Adding up heart rates or volumes makes no scientific sense. You must imperatively change this parameter to Average:
Drag the chosen parameter (e.g., CR_analyse) from the list above to the Values area.
In the Values area, do a left click (or click the small downward arrow) on the parameter name.
Select Value Field Settings... in the context menu.
Figure 7: Menu for modifying value field settings.
In the list that appears, click Average.
(Optional) If you also wish to display data dispersion (Standard Deviation and variability), drag this same parameter a second time into the Values area, return to its Value Field Settings, and select StdDev.
Figure 8: Selecting standard deviation calculation.
Step 4: Generating the associated chart
Click anywhere inside your finalized Pivot Table.
Go to the PivotTable Analyze tab that has appeared in the top ribbon.
PivotChart.
Choose the appropriate format:
A Line Chart to see evolution over time.
A Bar Chart to compare fixed averages between groups.
5. Concrete Configuration Examples
This section presents scenarios based on classic scientific study models. The two examples detailed below address the temporal comparison of physiological parameters between two distinct groups (e.g., Healthy vs. Impaired) as well as individual analysis within the same group to detect potential outliers.
These models are purely illustrative. It is up to you to define relevant comparisons for your research and adjust your Pivot Table (TCD) based on the specific structure of your own experimental protocol.
Scenario A: Comparing the temporal evolution of heart rate between two groups (Healthy vs. Treated (TRTD)) on a recording.
Rows: Drag the temporal variable (Second, Second raw, or CR_analyse time).
Columns: Drag the Group field.
Values: Drag a physiological parameter like CR_analyse configured as Average.
Result: A dynamic table displaying the evolution of tidal volume minute-by-minute for each group, ideal for plotting as a line chart.
Figure 9: Field configuration for Scenario A.
Figure 10: Pivot Table result with group averages.
Figure 11: Example of line chart (Scenario A).
Bonus: By removing the temporal variable from the Rows area and replacing it with the Group label, you generate a synthetic table displaying a single consolidated value over all data from different recordings for each group. This configuration allows grouping multiple physiological indicators within the same summary table.
You also have the possibility to integrate the standard deviation: to do this, duplicate the concerned parameter label in the area. Note that it is possible to add as many parameters as needed in this section to enrich your analysis.
Figure 12: Adding standard deviation to the Values area.
Figure 13: Summary table with averages and standard deviations.
Scenario B: Individual animal comparison
Rows: Drag the temporal variable (Second, Second raw, or CR_analyse time).
Columns: Drag the Group field then the Subject field.
Values: Drag a physiological parameter like CR_analyse configured as Average.
Result: Excel automatically segments your subjects according to their group (e.g., all TRTD subjects together, then all WT).
Figure 14: Field configuration for Scenario B.
Figure 15: Segmentation by subject in Scenario B.
Figure 16: Example of chart for individual comparison.
Bonus: You can also drag the Group label into Filters to display only individuals of one group at a time in your spreadsheets and graphical representations.
Figure 17: Using filters for selective display.
Special Case:
Longitudinal follow-up with a single group (repeated measures): If your study relies on following a single, unique group subjected to repeated measures over time, you will not have separation by the Group field. If your experimental design was correctly configured during acquisition in LASA, simply use the Phase label (or session ID) instead of the Group field. This will allow you to precisely compare the evolution of your physiological parameter averages from one recording week to another.
Groups not defined beforehand in LASA: If you exported a unique group containing a mix of animals (e.g., treated and controls) without separating them in the software, you can manually add a new column (e.g., Treatment or Condition) directly in the Excel All tab. Define the sorting mention for each row/individual. Once the column is filled, right-click on your Pivot Table and click "Refresh": your new field will appear in the pane and be used exactly like the others to build your tables and charts.
6. Conclusion and Perspectives: A Preliminary and Iterative Analysis
It is essential to keep in mind that this use of data in Excel constitutes a preliminary analysis. Pivot Tables and their associated charts are perfect for highlighting initial physiological trends (such as an increase in respiratory rate or a decrease in tidal volume in a pathological model) or validating the methodological quality of your signals.
However, the strength of the LASA system lies in its iterative nature: Observing your results in Excel can lead you to return to the LASA software to refine your calculation windows, manually correct specific signal portions using the Move Event function, or redefine areas of interest (Timeframes).
If your study requires advanced statistical tests (ANOVA, non-linear regressions, etc.), this export was designed to serve as a bridge. You can thus easily import your cleaned data sheet to specialized scripting and modeling software like Python or MATLAB, or to reference scientific statistical tools such as R or GraphPad Prism.
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