In this talk I will introduce a robust method for filling in short missing segments in multiparameter Intensive Care Unit cardiovascular data. This work was inspired by the “PhysioNet/Computing in Cardiology Challenge 2010: Mind the Gap”.

The interconnections between the signals were identified in the form of composite IIR transfer functions using the signals’ history. A genetic algorithm was applied for inferring the filter coefficients. Assuming that the connections do not vary in time, we managed to reconstruct the missing signals using the yet available parallel measured signals and the transfer functions.

The results are promising, as this method achieved the 5th place among 53 participants of the challenge. We concluded that this approach can be efficient in reconstructing and even detecting missing or corrupted cardiovascular signals or other type of datasets with several modalities and strong interconnections between them.