Published August 1, 2022 | Version 1.01
Dataset Open

MIMIC PERform Datasets

  • 1. University of Cambridge
  • 1. University of Cambridge
  • 2. City, University of London
  • 3. University of Surrey
  • 4. Technion Israel Institute of Technology

Description

Overview

The MIMIC PERform datasets contain physiological signals recorded from critically-ill patients during routine clinical care. Specifically, the datasets contain the following signals:

  • electrocardiogram (ECG)
  • photoplethysmogram (PPG)
  • impedance pneumography (imp), also known as respiratory (resp)
  • (in some cases) arterial blood pressure (abp)

They also contain some fixed parameters, stored in the 'fix' field.

The datasets were extracted from the MIMIC III Waveform Database. Further details of the datasets are provided in the documentation accompanying the ppg-beats project, which is available at: https://ppg-beats.readthedocs.io/en/latest/ .

Datasets

The following datasets are available:

Dataset Samples

The following dataset samples are available:

  • Truncated MIMIC PERform Dataset:
    • MIMIC PERform Training Dataset - Data from the first 10 patients in the (5 adults and 5 neonates) in Matlab format.
    • MIMIC PERform Testing Dataset - Data from the first 10 patients in the (5 adults and 5 neonates) in Matlab format.
    • MIMIC PERform AF Dataset - Data from the first 10 AF and non-AF patients in Matlab format (AF patients, non-AF patients).
  • 1-minute sample of adult data: Extracted from a non-AF subject in the MIMIC PERform AF Dataset, available in Matlab format.
  • 1-minute sample of neonate data: Extracted from a neonate subject in the MIMIC PERform Training Dataset, available in Matlab format.
  • 1-minute sample of AF data: Extracted from an AF subject in the MIMIC PERform AF Dataset, available in Matlab format.
  • 1-minute sample of noisy data: Extracted from a non-AF subject in the MIMIC PERform AF Dataset, available in Matlab format.

Citation

When using these datasets, please cite the following publication:

Charlton PH et al. Detecting beats in the photoplethysmogram: benchmarking open-source algorithms. Physiological Measurement 2022. DOI: 10.1088/1361-6579/ac826d

Acknowledgments

Each dataset is accompanied by a licence which acknowledges the source(s) of the data - please see the individual licenses for these acknowledgements.

Notes

This work was supported by British Heart Foundation (BHF) grants [FS/20/20/34626] and [PG/15/104/31913], and an EPSRC Impact Acceleration Award.

Files

mimic_perform_af_csv.zip

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Additional details