Data Chromatix

This package was developed at the Wyss Institute at Harvard by A. Burykin, S. Mariani, T. Silva and T. Henriques. It is described in:

Burykin A, Mariani S, Henriques T, Silva T, Schnettler W, Costa MD, Goldberger AL. “Remembrance of time series past: simple chromatic method for visualizing trends in biomedical signals.” Physiol Meas 2015;36(7):N95.

Please cite the above publication when referencing this material, and also include the standard citation for PhysioNet: 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," Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215.full]; 2000 (June 13).

Description

Analysis of biomedical time series plays a key role in clinical management and basic investigation. However, most conventional monitors streaming data in real-time show only the most recent values, not referenced to past dynamics. The proposed visualization method (termed “data chromatix”) was developed to address this challenge by bringing memory of the system’s past behavior into the current display window.

The function DataChromatix.m (version 1.0) assigns a color to each data point of a time series. The color is determined by the values of a normalized histogram (estimated probability density function) computed from a pre-selected segment of the data. The algorithm receives the time series as input and generates a video of its colorized version as it would look on a typical monitor display, as well as a static graph of the entire colorized signal.

The algorithm has the following parameters: the memory and colorization window lengths, the shift (s), the histogram bin size, and the number of colors (c) in the chromatic map (Fig. 1).

Figure 1

Figure 1: Memory window and colorization window at a given time tC and at the following time instant tC+s. Both windows are shifted to the right by the shift s. Adapted from (1).

At each step, a normalized histogram of the data points in the memory window is computed. Then, the interval [0,1] is divided into c adjacent intervals, and each interval is assigned a color. If the jet color-map is used, the interval [0,1/c) corresponds to dark red and the interval ((c-1)/c,1] corresponds to dark blue. Subsequently, each data point in the colorization window is assigned the color of the histogram bin into which it falls. Finally, the colorization window is advanced by the shift, s, and the memory window is either extended or advanced by the same amount (Fig. 2).

Figure 2

Figure 2: Schematic illustration of the colorization algorithm: the histogram of the time series (left panel) is colorized according to the bins height. Each point of the time series (right panel) is then colorized according to the bin it belongs to. Adapted from (1).

This colorization algorithm is intended to facilitate analysis of physiologic and non-physiologic time series. Future studies will help assess its utility.

The function has been tested on MATLAB R2014a, R2014b and R2015a.

Sample Input

Mandatory inputs to the function are:

Optional inputs to the function are:

Along with the function, we provide two examples that employ a fetal heart rate time series from the CTU-UHB Intrapartum Cardiotocography Database on PhysioNet, one using a memory window starting from the beginning of the recording, the other using a moving memory window of fixed length. Please note that for loading the time series, the MATLAB version of the wfdb library (http://physionet.org/physiotools/matlab/wfdb-app-matlab/) must be installed.

Acknowledgments

This package was developed at the Wyss Institute at Harvard by A. Burykin, S. Mariani, T. Silva and T. Henriques.

Users of our software should cite: (1) Burykin A*, Mariani S*, Henriques T, Silva T, Schnettler W, Costa MD**, Goldberger AL**. “Remembrance of time series past: simple chromatic method for visualizing trends in biomedical signals.” Physiol Meas 2015;36(7):N95.

* Joint first authors
** Joint senior authors

Icon  Name                    Last modified      Size  Description
[TXT] DataChromatix.m 12-Jun-2015 18:56 9.1K [TXT] example1.m 12-Jun-2015 18:56 519 [TXT] example2.m 12-Jun-2015 18:56 485 [TXT] local-old.css 18-Jun-2015 14:47 594 C# source file [TXT] local.css 02-Oct-2015 18:29 3.1K C# source file

Questions and Comments

If you would like help understanding, using, or downloading content, please see our Frequently Asked Questions.

If you have any comments, feedback, or particular questions regarding this page, please send them to the webmaster.

Comments and issues can also be raised on PhysioNet's GitHub page.

Updated Friday, 28 October 2016 at 18:58 BRST

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 2R01GM104987-09.