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.

Abstract: In order to generate a broadband transfer function of complex characteristics of a linear time-invariant (LTI) system, data characterising properties of the system are acquired. A set of poles in the complex plane are defined to characterize the system, and then an iterative process is performed to: define a set of orthonormal rational basis functions incorporating the defined poles, use the orthonormal rational basis functions to estimate transfer function coefficients, and derive revised values for the complex poles, until a desired level of accuracy of the transfer function coefficients is attained. The revised complex poles are used to determine parameters of the broadband transfer function.

Abstract: The present invention relates to a method (200) for generating a multivariate system model of a multivariate system which has a plurality of parameters and the multivariate system model is representative for the system response to changes in the parameters. The method (200) involves performing a plurality of measurements and/or simulations on the multivariate system to obtain reference data (201). The method further involves the selection of rational base functions (202) and combining these base functions (203) into a model of the multivariate system. The multivariate system model is obtained (208) by minimizing a cost function (205) between the model and the reference data. Explicit weight factors which are related to the reference data are used. The respective values for the explicit weight factors in the cost function are iteratively determined (205) in order to approximate the reference data with the model and the cost function is updated accordingly in each iteration.

Abstract: In order to generate a broadband transfer function of complex characteristics of a linear time-invariant (LTI) system, data characterising properties of the system are acquired. A set of poles in the complex plane are defined to characterise the system, and then an iterative process is performed to: define a set of orthonormal rational basis functions incorporating the defined poles, use the orthonormal rational basis functions to estimate transfer function coefficients, and derive revised values for the complex poles, until a desired level of accuracy of the transfer function coefficients is attained. The revised complex poles are used to determine parameters of the broadband transfer function.