Motion Artifact Contaminated ECG Database

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The data set and its collection methods are described in

Vahid Behravan, Neil E. Glover, Rutger Farry, Mohammed Shoaib, Patrick Y. Chiang. Rate-Adaptive Compressed-Sensing and Sparsity Variance of Biomedical Signals. Body Sensor Networks (BSN) 2015 IEEE International Conference in June 2015.

A support PDF showing the lead placement is also provided.

When referencing this material, please include the citation above, 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).

Short duration ECG signals are recorded from a healthy 25-year-old male performing different physical activities to study the effect of motion artifacts on ECG signals and their sparsity.

For each measurement, 4 pairs of electrodes built into a single patch are placed on the subject. The electrodes are arranged at 45-degree offsets as shown below:

The patch itself is also placed at multiple orientations relative to the body:

[Position of electrode patch on the body]

Each recording contains four signals (ECG 1 to ECG 4) corresponding to the four pairs of electrodes.

Recording information:

Sampling rate: 500 Hz
Resolution: 16 bits
An analogue gain of 100 is applied to the raw ECG recordings before ADC.
The 2-digit number before the last letter shows the offset of the whole electrode patch in degrees.
The last letter of each record name corresponds to the type of physical activity as below:
1. s = standing
2. w = walking
3. j = single jump