Which antialiasing filter before equispaced sampling I am using MIT-BIH arrhythmical database where I have a digital signal of 1200 Hz so 1200 samples per second.
This means that analog filters have already applied to remove the frequencies over Nyquist frequency, so no aliasing.

However, I want to take equispaced sampling, every two sample, simply by the following in Matlab

data([0:2:1200]);


I am reading Andre Quinquils' book Digital Signal Processing Using Matlab 2008:


  It is always necessary to use an anti-aliasing filter before the
  sampling stage in order to avoid any spectral aliasing risk and to set
  an appropriate sampling frequency. In practice, a causal approximation
  of this ideal filter is used. Thus, depending on the chosen filter
  synthesis method, some imperfections are introduced, such as a
  passband amplitude ripple, a transition band and a stopband finite
  attenuation.


Does this mean that I need to apply a new anti-aliasing filter before the sampling stage in oder to avoid aliasing?
I think the sampling stage here is the equalspaced sampling.
I have not applied any new special anti-aliasing filter.

Which antialiasing filter can you use before equalspaced sampling stage?
Yes, you have to apply another filter before downsampling in order to avoid aliasing. Your original signal has been acquired with a sampling rate $f_\mathrm{s} = 1.2\,\text{kHz}$. Taking every second sample effectively means that you divide the sampling rate by two, so your new sampling rate $f_\mathrm{s}'= f_\mathrm{s}/2 = 600\, \text{Hz}$. To avoid aliasing you have to make sure that the signal contains no frequencies above $f_\mathrm{s}'/2 = 300\, \text{Hz}$ before you apply the downsampling. If it is certain that your signal does not contain any frequencies above $f_\mathrm{s}'/2$ then you don't need a filter.

The anti-aliasing filter can be implemented by an FIR filter. Its properties like stopband attenuation and width of transition band depend on your signal: if you have spectral components near to 300 Hz, the filter needs to be quite steep and it will consequently have a high order. If hardware implementation is an issue, have a look at Cascaded Integrator-comb filters.