MIMIC PERform Datasets

<strong>Overview</strong> 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) 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/ . <strong>Datasets</strong> The following datasets are available: <strong>MIMIC PERform AF Dataset: </strong>Recordings from 35 critically-ill adults during routine clinical care, categorised as either AF (atrial fibrillation, 19 subjects) or non-AF (16 subjects). Matlab format (AF subjects, non-AF subjects) WFDB format (AF subjects, non-AF subjects) CSV format (AF subjects, non-AF subjects) <strong>MIMIC PERform Training Dataset: </strong>Recordings from 200 patients during routine clinical care, who are categorised as either adults (100 subjects) or neonates (100 subjects). Matlab format (all data, adults, neonates) WFDB format (all data, adults, neonates) CSV format (all data, adults, neonates) <strong>MIMIC PERform Testing Dataset: </strong>Recordings from 200 patients during routine clinical care, who are categorised as either adults (100 subjects) or neonates (100 subjects). Matlab format (all data, adults, neonates) WFDB format (all data, adults, neonates) CSV format (all data, adults, neonates) <strong>Citation</strong> When using these datasets, please cite the following publication: Charlton PH <em>et al.</em> <strong>Detecting beats in the photoplethysmogram: benchmarking open-source algorithms</strong>. <em>Physiological Measurement</em> 2022. DOI: 10.1088/1361-6579/ac826d <strong>Acknowledgments</strong> Each dataset is accompanied by a licence which acknowledges the source(s) of the data - please see the individual licenses for these acknowledgements.