I am not sure it may be the best approach, but you may try to analyze the correlation between your parameters of interest and the age by taking all the subjects at the same time. If this correlation is significant on your data, you may not exclude the role of age. At that point, it may be better to perform a multivariate analysis (e.g. ANOVA, factor analysis) to detect the most influent variable.

Hi Ezequiel, 

Could you please explain what kind of age correction do you want to apply to the spectrum...? The HRV parameter that vary with age is the total power of HRV (squared st_dev), and this is shown by some studies. The total power of HRV is the same in time domain and frequency domain (Parceval theorem) so not really specific to the spectral analysis...unless you want to scale absolute LF-HF values based on the age.       

Dear Vasile Zoicas,

Thanks for your valuable comment. Maybe I was not clear with the question.

I would like to adjust the traditional frequency domain parameters by means of the patient age.

Since age impact , for instance, decreasing (increasing) VLF, LF, HF (LF/HF), etc. How can I correct this values with age?

I have a healthy and a pathological group. However, the healthy group is much younger (age mean and age range differ) than the pathological group. Thus, without any correction, I cannot assure that differences in this parameters (VLF, LF, HF, etc)are due to the pathology, since differences might be originated by age. 

Do you know any paper in which this problem  is considered? What can you recommend me to overcome this issue ?

Regards,

Ezequiel.

I am not sure it may be the best approach, but you may try to analyze the correlation between your parameters of interest and the age by taking all the subjects at the same time. If this correlation is significant on your data, you may not exclude the role of age. At that point, it may be better to perform a multivariate analysis (e.g. ANOVA, factor analysis) to detect the most influent variable.

Hi Ezequiel,

 Assuming you have the mathematical link between age and HRV total power (msecˆ2) you might be able to scale the RR interval signal before applying the FFT. In that case, the absolute VLF, LF, HF and RMSSD will scale automatically.This will have no effect on the ratio LF/HF or on the normalized units for LF and HF since they are relative.Here is a study treating about those subjects:

http://www.ijcte.org/papers/480-G1327.pdf

But please note the mathematical dispersion around the age/power law can be significant: someone who's practicing sport et 50 can maintain his power above someone at 30 that's not doing any sport. So statistically this verified but individual corrections might be difficult.

Alternatively, we developed a tool processing (in top of regular FFT and RMSSD) a dynamic analysis of the ANS (dynamic sympathetic and parasympathetic activities) along with new markers, you can check it out here:

http://www.codesna.com/en/products/codesna_hrv/

 PS I agree with Marianna comment!

 Cheers, Vasile      

Hi Ezequiel, 

Correction of HRV for age (if possible) is not a good solution ! Not only age but also other factors such as gender, obesity, physical activity, psychological trait/state, etc. can affect HRV and you can not correct HRV for all of these factors. Also, it is not easy to match patients and controls for all the possible confounding factors when you do sampling, though matching by age and gender is popular and somehow easy to do. As Marianna mentioned it is better to include factors with possible effects/associations with HRV into the multivariate analyses. This way you can interpret if the association between the pathological condition and HRV is mediated/moderated by other factors.

I am not sure it may be the best approach, but you may try to analyze the correlation between your parameters of interest and the age by taking all the subjects at the same time.May be Selected Frequency Luminescence is a good idea!

Hi Ezequiel, In a study of persons of different ages, we obtained the following results. See. Monograph + Table.

Book Kompleksnyiy podhod k diagnostike sostoyaniya kardiorespirat...

I think you should try to correlate first with size or weight of the heart before trying correct for age, also to separate between genders, ethnics and conditions (ex. normal or disease). I would be really difficult to get a correction for age, if you do not separate and normalize first the data. Of course normalizations should be done after you had check for correlations of the data. So my final comment on this: separate the data as much as possible so that the data can be as close as possible to eliminate factors affecting your results, and then normalize by an another intrinsic factor that could affect your individual parameter, if you can not get heart weight or size use body weight. Hopefully this can help your results.