Database Open Access
Published: Feb. 10, 2000. Version: 1.0.0
Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals (2003). Circulation. 101(23):e215-e220.
The data consist of 70 records, divided into a learning set of 35 records (a01 through a20, b01 through b05, and c01 through c10), and a test set of 35 records (x01 through x35), all of which may be downloaded from this page. Recordings vary in length from slightly less than 7 hours to nearly 10 hours each. Each recording includes a continuous digitized ECG signal, a set of apnea annotations (derived by human experts on the basis of simultaneously recorded respiration and related signals), and a set of machine-generated QRS annotations (in which all beats regardless of type have been labeled normal). In addition, eight recordings (a01 through a04, b01, and c01 through c03) are accompanied by four additional signals (Resp C and Resp A, chest and abdominal respiratory effort signals obtained using inductance plethysmography; Resp N, oronasal airflow measured using nasal thermistors; and SpO2, oxygen saturation).
Several files are associated with each recording. The files with names of the form rnn.dat contain the digitized ECGs (16 bits per sample, least significant byte first in each pair, 100 samples per second, nominally 200 A/D units per millivolt). The .hea files are (text) header files that specify the names and formats of the associated signal files; these header files are needed by the software available from this site. The .apn files are (binary) annotation files, containing an annotation for each minute of each recording indicating the presence or absence of apnea at that time; these are available for the 35 learning set recordings only. The qrs files are machine-generated (binary) annotation files, made using sqrs125, and provided for the convenience of those who do not wish to use their own QRS detectors. Please note that the .qrs files are unaudited and contain errors. You may wish to correct these errors (if you do, please send your corrections to us). Otherwise, you may use these annotations in uncorrected form if you wish to investigate methods of apnea detection that are robust with respect to small numbers of QRS detection errors, or you may ignore these annotations entirely and work directly from the signal files. Further information about the annotation files, including interpretations of the annotation types (codes) and details of how the .qrs files were created, are available here.
In April 2013, Chiu-wen Wu reported that training set control records c05 and c06 come from the same original recording (c05 begins 80 seconds later than c06). The slightly different descriptions of these records in additional-information.txt suggest that c06 may have been a corrected version of c05.
The eight records that include respiration signals have several additional files each. The four respiration-related signals are combined in a file named rnnr.dat, which has its own header file (rnnr.hea), as well as a header file named rnner.hea, which (when used with software such as WAVE or WVIEW) allows you to examine the ECG and the respiration signals side-by-side.
Finally, if you are running Linux and have installed WAVE, you may click on the .xws file associated with each record to view that record without downloading it first. WAVE and the related WFDB software package may be downloaded from this site.
If you wish to download all of the files in this directory without selecting each one individually, try using a utility for batch HTTP transfers such as wget, available here in source form for all versions of UNIX and as a precompiled binary for MS-Windows. Most Linux distributions include wget. Once you have installed wget, retrieve these files using
wget -r -np http://www.physionet.org/physiobank/database/apnea-ecg/
(or substitute the name of a nearby PhysioNet mirror for www.physionet.org above). The files in this directory occupy 583 megabytes.
The data in this directory have been contributed by Dr. Thomas Penzel of Phillips-University, Marburg, Germany. See the announcement of CinC Challenge 2000 for details on the competition for which these data have been assembled here.
Anyone can access the files, as long as they conform to the terms of the specified license.
License (for files):
Open Data Commons Attribution License v1.0
Files on Google Cloud
Click here to view the files in the Google Cloud Console. Login with a Google account is required.
Total uncompressed size: 580.6 MB.Download Zip (580.6 MB)