From an experiment, I have a dataset of beat-to-beat heart rate data: a list of the time between each heart beat in [ms].
The data is measured using an infrared optic sensor at the finger tip. The sensor frequently misinterprets a slight movement of the finger as an heart beat. Data therefore often looks somewhat like this:

    {1000, 1000, 1000, 1000, 500, 500, 1000, 1000, 1000, 600, 400, 1000}

In this example, one can easily see that the 5th and 6th element should be one; same for the 10th and 11th. However, in real life the data looks more like this:

    data = {981, 870, 1099, 1105, 650, 397, 920, 917, 1015, 1085, 210, 344, 457,
    950}

where the 5-6 (`650, 397`) and 11-12-13 (`210, 344, 457`) should be taken together.
It is easy to just delete the incorrect data by using something like:

    DeleteCases[data,
    x_ /; x < Mean[data] - StandardDeviation[data] || 
    x > Mean[data] + StandardDeviation[data]]

...but I want to make a function that recognizes when multiple elements should be added to one.

One could just add every two, three or four (=`length`) elements and select the `Cases` where the result lies (for example) in the range `Mean[data]±StandardDeviation[data]`:

    length = 2;
    Position[Total[data[[# ;; # + length]]] & /@ 
      Range[Length[data] - length], 
     x_ /; x > Mean[data] - StandardDeviation[data] && 
       x < Mean[data] + StandardDeviation[data]]

Result:

    {{5}, {11}, {12}}

This gives me an idea of where the incorrect data is. Unfortunately, after having this result, I don't know what to do with it... For example, I get confused by the fact that elements 11-12-13 return 2 cases of incorrect data when I use `length=1`. And maybe there are more (simple) ways to filter this data.

**Question: can anyone give me a kick-start?**

Edit: You can download an example of actual data [here][1]. Just `Flatten[Import[filename,"Table"]]`

  [1]: https://www.dropbox.com/s/z98rp0i4364nfdy/beattobeatdata.txt?dl=0