Estimating Activity from Instantaneous Heart Rate

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A common problem in many studies is to match levels of physical activity between subjects. In prospective studies, the solution to this problem is usually accomplished by developing and following a protocol that specifies activities. In retrospective studies, such as those that can be performed using PhysioBank data, however, reliable data on activity is often unavailable, so that indirect methods for assessing activity may become useful.

Using only a heart rate time series, it is possible to measure a number of features that reflect the level of physical activity. Here we provide software for deriving an "activity index" based on measurements of mean heart rate, total power of the instantaneous heart rate time series over a given interval, and stationarity. The algorithm was tested using a set of 35 ECG recordings for which an independent activity indicator was available. It consistently selected periods of minimum activity that were in agreement with the independent activity indicator (see the reference below for details).

The input to activity is a time series of instantaneous heart rate measurements, such as can be produced by tach. For example:

    tach -r record -a annotator | activity [-m] [len]

Each value of the activity index is derived from len values in the input heart rate time series; if len is omitted, 600 samples (5 minutes, at tach's default sampling rate) of input data are used to produce each output value. The input windows overlap by 50%, so that the interval between output values is half of that specified by len, or 2.5 minutes by default.

Use activity's -m option to find and output only the interval for which the activity index is minimum.

Here are:

Address for correspondence:

George B. Moody
MIT Room E25-505A
Cambridge, MA 02139 USA


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Updated Thursday, 9 July 2015 at 11:06 EDT

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.