MIT-BIH Polysomnographic Database

@article{PhysioNet-slpdb-1.0.0,
  author = {Moody, George},
  title = {{MIT-BIH Polysomnographic Database}},
  journal = {{PhysioNet}},
  year = {1999},
  month = aug,
  note = {Version 1.0.0},
  doi = {10.13026/C23K5S},
  url = {https://doi.org/10.13026/C23K5S}
}

@article{pollard_physionet_2026, title = {{PhysioNet} as a global platform for biomedical research}, volume = {1}, issn = {3005-0693}, url = {https://doi.org/10.1038/s44360-026-00096-z}, doi = {10.1038/s44360-026-00096-z}, abstract = {Twenty-five years after its first public repository was established, the PhysioNet resource for biomedical and clinical data has enabled tens of thousands of research papers. Further efforts are ongoing to advance secure data sharing, community engagement and scalable research infrastructure.}, number = {8}, journal = {Nature Health}, author = {Pollard, Tom and Moody, Benjamin E. and Lehman, Li-wei H. and Gow, Brian J. and Fernandes, Chrystinne and Xie, Chen and Johnson, Alistair and Mark, Roger G. and Heldt, Thomas}, month = aug, year = {2026}, pages = {792--795},}

Abstract

The MIT-BIH Polysomnographic Database is a collection of recordings of multiple physiologic signals during sleep. Subjects were monitored in Boston's Beth Israel Hospital Sleep Laboratory for evaluation of chronic obstructive sleep apnea syndrome, and to test the effects of constant positive airway pressure (CPAP), a standard therapeutic intervention that usually prevents or substantially reduces airway obstruction in these subjects.

Background

Polysomnography provides simultaneous measurements of sleep and cardiorespiratory physiology, supporting investigation of sleep-related events such as apnea, arrhythmias, hemodynamic changes, and myocardial ischemia. Historically, polysomnographic recordings were stored on paper or microfilm, making them difficult to reanalyze and share between research centers.

This database was developed to make polysomnographic recordings available in a digital format for research and education. Following the example of the MIT-BIH Arrhythmia Database [1], it was originally distributed on CD-ROM to facilitate access to physiological waveforms and accompanying annotations [2]. The recordings support studies of sleep apnea physiology and non-linear physiological dynamics, development and evaluation of automated signal-analysis methods, and teaching of sleep physiology and sleep staging.

Methods

Sixteen polysomnographic recordings were selected from a cohort of 60 male subjects with or without sleep apnea syndrome. The selected subjects had a mean age of 40 years (range: 32–56 years). Recording durations ranged from 2 to 7 hours.

Physiological measurements included electrocardiography (ECG), electroencephalography (EEG), electrooculography (EOG), chin electromyography (EMG), invasive blood pressure, oxygen saturation, nasal and chest respiration, and cardiac volume measured using the VEST system (Capintec). Signals were digitized at 250 Hz with a resolution of 12 bits per sample.

Recordings were reviewed and edited using WAVE (Waveform Analyzer, Viewer, and Editor), developed at the Massachusetts Institute of Technology. QRS complexes were detected automatically, and the resulting annotations were reviewed and corrected using WAVE. Sleep stages were assigned according to the criteria of Rechtschaffen and Kales.

Data Description

The database contains physiological waveform recordings and accompanying metadata and annotations for 16 subjects. Each recording is associated with the following files:

• Waveform files: Digitized physiological signals from polysomnographic monitoring.
• Header files: Recording metadata, including duration, signal gains and offsets, data structure, and the date and start time of monitoring.
• QRS annotation files: Reviewed annotations identifying QRS complexes in the ECG.
• Sleep-stage files: Sleep-stage classifications assigned using the Rechtschaffen and Kales criteria.

Additional event information described in the original distribution includes the timing of sleep apnea episodes and periods of continuous positive airway pressure (CPAP).

The original CD-ROM distribution also included software written in C and Bourne shell for waveform visualization and physiological signal analysis. These tools supported beat-by-beat analysis of heart rate, blood pressure, and ventricular volume; frequency-domain analysis using discrete Fourier transforms and maximum entropy methods; and estimation of periodic components using least-squares cosine spectrum analysis.

Usage Notes

These data have been used in several studies: (i) Rigney investigated the non-linear dynamics of respiration, heart rate, and blood pressure during sleep apnea; [3] (ii) Garpestad examined cardiovascular dynamics at the termination of obstructive apnea using the VEST method, which requires a specialized device to monitor cardiac volume; [4] and (iii) we investigated how sleep stage affects cardiorespiratory events during the night, [5] including counts of K-complexes and sleep spindles during sleep apnea, the frequency of apnea episodes, and automated sleep-stage classification.

This dataset may be useful for: (i) mathematicians investigating non-linear dynamics in physiological data; (ii) engineers developing software to analyze sleep-related data; (iii) clinicians studying the physiology of sleep apnea; and (iv) students learning about sleep physiology, including sleep staging through visual inspection of physiological recordings.

References

1. Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database. IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001). (PMID: 11446209). https://doi.org/10.1109/51.932724
2. Ichimaru Y, Moody GB. Development of the polysomnographic database on CD-ROM. Psychiatry and Clinical Neurosciences 53:175-177 (April 1999).
3. Rigney DR, Goldberger AL, Ocasio WC et al. Multichannel physiological data description and analysis. In: Weigend AS, Gerschenfeld NA (eds), Time Series Prediction. Addison-Wesley 1994; 105–129.
4. Garpestad E & Katayama H et al. Stroke volume and cardiac output decrease at termination of obstructive apneas. J. Appl. Physiol. 1992; 73(5): 1743–1748.
5. Ichimaru Y, Clark K, Weiss WJ et al. Effect of sleep on the relationship between respiration and heart rate variability. IEEE Comp. Soc. Computers in Cardiology 1990; 657–660.