Analysis of temporal correlation in heart rate variability through maximum entropy principle in a minimal pairwise glassy model
In this work we apply statistical mechanics tools to infer cardiac pathologies over a sample of M patients whose heart rate variability has been recorded via 24 h Holter device and that are divided in different classes according to their clinical status (providing a repository of labelled data). Considering the set of inter-beat interval sequences { r(i) } = { r1(i) , r2(i) , … , } , with i= 1 , … , M, we estimate their probability distribution P(r) exploiting the maximum entropy principle.