Abstract: A method for detecting a respiratory event of a subject comprises: receiving a bio-impedance measurement signal (S2) dependent on respiratory action from the subject; extracting (306) at least one time-sequence of the bio-impedance measurement signal (S2); and for each extracted time-sequence: comparing (308) the bio-impedance measurement signal (S2) with each of a plurality of machine learning models in an ensemble of machine learning models to form a set of predictions of occurrence of a respiratory event, wherein each prediction is based on comparing the bio-impedance measurement signal (S2) with one machine learning model, wherein each model correlates features of time-sequences of a bio-impedance measurement signal (S2) with presence of a respiratory event and wherein each model is trained on a unique data set of training time-sequences; deciding (310) whether a respiratory event occurs in the extracted time-sequence based on the set of predictions.

Abstract: A method for detecting a respiratory event of a subject comprises: receiving a bio-impedance measurement signal (S2) dependent on respiratory action from the subject; extracting (306) at least one time-sequence of the bio-impedance measurement signal (S2); and for each extracted time-sequence: comparing (308) the bio-impedance measurement signal (S2) with each of a plurality of machine learning models in an ensemble of machine learning models to form a set of predictions of occurrence of a respiratory event, wherein each prediction is based on comparing the bio-impedance measurement signal (S2) with one machine learning model, wherein each model correlates features of time-sequences of a bio-impedance measurement signal (S2) with presence of a respiratory event and wherein each model is trained on a unique data set of training time-sequences; deciding (310) whether a respiratory event occurs in the extracted time-sequence based on the set of predictions.