These data were contributed by Plamen Ch. Ivanov, Zhi Chen and Kun Hu, who used them in:
Please cite these publications when referencing this material, and also include the standard citation for PhysioNet:
The data in this collection include: (1) 6 surrogate stationary signals with different correlations; (2) 7 surrogate correlated signals with linear, sinusoidal and powerlaw trends; and (3) 15 surrogate correlated signals with different types of nonstationarities. Each data file contains one column of data in ASCII format. Results on correlated signals with trends are discussed in Physical Review E 64, 011114 (2001). Results on correlated signals with different types of nonstationarities are discussed in Physical Review E 65, 041107 (2002). The parameter "alpha" (see below) is an exponent measuring the degree of correlations in a signal, and Nmax is the signal length. A detailed description of these signals can be found in the original articles.
Correlations in these signals can be quantified using Detrended Fluctuation Analysis (DFA). Limitations of the DFA method are discussed in the articles cited above. In particular, the second paper notes that
... for anticorrelated signals, the scaling exponent obtained from the DFA method overestimates the true correlations at small scales. To avoid this problem, one needs first to integrate the original anticorrelated signal and then apply the DFA method. The correct scaling exponent can thus be obtained from the relation between n [the DFA box length] and F(n)/n instead of F(n) ... In order to provide a more accurate estimate of F(n), the largest box size n we use is N_{max}/10, where N_{max} is the total number of points in the signal.
Since these files are quite large, they are provided as gzipcompressed text.
1. Correlated stationary signals
 noise0117.txt.gz alpha = 0.1, N_{max} = 2^{17};
 noise0217.txt.gz alpha = 0.2, N_{max} = 2^{17};
 noise0517.txt.gz alpha = 0.5, N_{max} = 2^{17};
 noise0817.txt.gz alpha = 0.8, N_{max} = 2^{17};
 noise0917.txt.gz alpha = 0.9, N_{max} = 2^{17};
 noise1517.txt.gz alpha = 1.5, N_{max} = 2^{17}.
2. Surrogate signals with trends
2a) Signals with linear trends
 trlina1.txt.gz alpha = 0.1, N_{max} = 2^{17}, slope of linear trend A_{l} = 2^{16} / index;
 trlina2.txt.gz alpha = 0.1, N_{max} = 2^{17}, slope of linear trend A_{l} = 2^{12} / index;
 trlina3.txt.gz alpha = 0.1, N_{max} = 2^{17}, slope of linear trend A_{l} = 2^{8} / index.
 trsin1.txt.gz alpha = 0.9, N_{max} = 2^{17}, Amplitude of trend A_{s} = 2, period T = 128;
 trsin2.txt.gz alpha = 0.1, N_{max} = 2^{17}, Amplitude of trend A_{s} = 2, period T = 128.
 trpow1.txt.gz alpha = 0.9, N_{max} = 2^{17}, power lambda = 0.4, Amplitude A_{p} = 1000 / (N_{max}) ^{lambda};
 trpow2.txt.gz alpha = 1.5, N_{max} = 2^{17}, power lambda = 0.7, Amplitude A_{p} = 0.01 / (N_{max}) ^{lambda}.
3. Surrogate nonstationary signals
3a) Signals with cutout segments (discontinuities)
 cut0117w20p95.txt.gz alpha = 0.1, seg. cutout probability p = 0.05, Width W = 20, N_{max} = 2^{17};
 cut0117w20p50.txt.gz alpha = 0.1, seg. cutout probability p = 0.50, Width W = 20, N_{max} = 2^{17};
 cut0917w20p95.txt.gz alpha = 0.9, seg. cutout probability p = 0.05, Width W = 20, N_{max} = 2^{17};
 cut0917w20p50.txt.gz alpha = 0.9, seg. cutout probability p = 0.50, Width W = 20, N_{max} = 2^{17}.
 sp02p05a1.txt.gz spikes probability p = 0.05, Amplitude Asp = 1, N_{max} = 2^{17};
 sp02p05a1sp.txt.gz spikes signal only, spikes probability p = 0.05, Amplitude Asp = 1, N_{max} = 2^{17};
 sp08p05a10.txt.gz spikes probability p = 0.05, Amplitude Asp = 10, N_{max} = 2^{17};
 sp08p05a10sp.txt.gz spikes signal only, spikes probability p = 0.05, Amplitude Asp = 10, N_{max} = 2^{17}.
 d2h4pd050118s.txt.gz alpha = 0.1, sigma_{1} = 1, sigma_{2} = 4 (probability p = 0.05), N_{max} = 2^{18};
 d2h4pd950118s.txt.gz alpha = 0.1, sigma_{1} = 1, sigma_{2} = 4 (probability p = 0.95), N_{max} = 2^{18};
 d2h4pd050918s.txt.gz alpha = 0.9, sigma_{1} = 1, sigma_{2} = 4 (probability p = 0.05), N_{max} = 2^{18};
 d2h4pd950918s.txt.gz alpha = 0.9, sigma_{1} = 1, sigma_{2} = 4 (probability p = 0.95), N_{max} = 2^{18}.
 cut010917p90w20_sum.txt.gz (mixed signal) alpha_{1} = 0.1 (90%), alpha_{2} = 0.9(10%), Width = 20, N_{max} = 2^{17};
 cut010917p90w20_comp1.txt.gz (component 1) alpha_{1} = 0.1 (90%) only, Width W = 20, N_{max} = 2^{17};
 cut010917p90w20_comp2.txt.gz (component 2) alpha_{2} = 0.9 (10%) only, Width W = 20, N_{max} = 2^{17}.
Address for correspondence:
Plamen Ch. Ivanov, Ph.D.
Room 247, Dept. of Physics
Boston Univeristy
590 Commonwealth Avenue
Boston, MA 02215, USA
Email: plamen@meta.bu.edu
If you would like help understanding the content of this page, or using and downloading data/software, please see our Frequently Asked Questions. If you have any comments, feedback, or particular questions regarding this page or our website, please send them to the webmaster. Updated Monday, 31 August 2015 at 15:30 BRT 
PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM10498709.
