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 wearable sensing device (100) for sensing one or more physiological signals of a subject, comprising: a sensor unit (200), including a housing, a connection receptacle (210) and electronic circuitry configured for acquiring one or more physiological signals received via the connection receptacle; a patch unit (300), including a connection plug (310) connected via conductive tracks (341) to a plurality of electrodes (342) configured for sensing the one or more physiological signals. The connection plug (310) is configured for being connectable with the connection receptacle (210) such that the one or more physiological signals sensed by the electrodes (342) are transmitted to the electronic circuitry of the sensor unit (200).