In-vitro Major Arterial Cardiovascular Simulator: Benchmark Data Set for in-silico Model Validation

Background

The data described here supplements the paper "In-vitro Major Arterial Cardiovascular Simulator to generate Benchmark Data Sets for in-silico Model Validation" (to be submitted).  It was created at Technische Hochschule Mittelhessen (THM) in Germany and uploaded to Zenodo. Please cite the paper M. Wisotzki, A. Mair, P. Schlett, B. Lindner, M. Oberhardt, S. Bernhard, In Vitro Major Arterial Cardiovascular Simulator to Generate Benchmark Data Sets for In Silico Model Validation (2022), Data 7(11), DOI: 10.3390/data7110145 and the Zenodo doi when using this dataset.

General description / Dataset Structure

Each mat-File describes a different stenosis degree at the popliteal artery of the in-vitro simulator MACSim (details can be found in the paper). There are 17 pressure signals for different positions, one flow sensor close to the stenosis location and one monitor signal of the proportional valve use to control the input curve. Total duration of each signal is 60s with a sampling rate of 1000 Hz. Each mat-file contains a header structure with metadata and struct array for signals of each sensor. Signals in each mat-File are aligned with respect to a common time axis, but this is not guaranteed between different measurements/files. The file format can either be loaded directly in Matlab or in Python with scipy's loadmat function.

The different stenosis degrees for each degree are:
ScenarioI: 100 % Area fraction (no stenosis)
ScenarioII: 37,5 % Area fraction
ScenarioIII: 23,4 % Area fraction
ScenarioIV: 6,56 % Area fraction

Data fields for each file

headerStruct

field
description

rate
sampling rate in Hz

description
name of the scenario according to the paper, corresponds to filename

configuration
parameters of the trapezoidal input curve (offset and amplitude in mmHg, ascend times and descend times and smoothing window in a fraction the time period (1.2s))

signalStruct

field
description

nodeId
corresponds to numbered nodes at which the sensor is placed, the corresponding location can be found in the paper (node numbering, not sensor numbers) or in the software SISCA (https://gitlab.com/agbernhard.lse.thm/sisca) in the example database.

type
'p' ... pressure or 'q' ... flow

data
double array, time series of each sensor,  unit mmHg for type 'p' and ml/s for type 'q'  

anatomicalPosition

name of the corresponding anatomical position