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 system for respiratory monitoring of a subject comprises: a bioimpedance measurement sensor, which is configured for arrangement in relation to the subject for acquiring a bioimpedance signal; a processing unit, which is configured to receive the acquired bioimpedance signal and receive a reference signal, the reference signal representing a respiratory effort of the subject or a respiratory airflow at a time of bioimpedance signal acquirement, the processing unit being further configured to divide the bioimpedance signal into an effort component representing a respiratory effort of the subject and a flow component representing a respiratory airflow of the subject based on the received bioimpedance signal and the received reference signal.

Abstract: An electronic system for bioimpedance signal acquisition, comprises: a current signal injection module configured for generating a current signal to be applied to a subject; a bioimpedance signal measurement module configured for measuring a bioimpedance signal based on a voltage generated by the current signal; a data quality detection module configured for detecting an AC or a DC level of the measured bioimpedance signal and detecting whether the AC or DC level is within or outside an AC reference value range and a DC reference value range, respectively; and a signal adaptation module configured for modifying at least one parameter of the current signal injection module or the bioimpedance signal measurement module based on said detection of the AC or DC level in relation to the AC reference value range and the DC reference value range, respectively.

Abstract: A method for estimating total body water, TBW, content of a subject, comprises: receiving locally measured bioimpedances for at least two different frequencies of a stimulation current at a body part of the subject; determining a first resistance and a second resistance of the bioimpedance, wherein the first resistance corresponds to a representation of the bioimpedance being purely resistive at a low frequency of the stimulation current and the second resistance corresponds to a representation of the bioimpedance being purely resistive at a high frequency of the stimulation current; and estimating the TBW content of the subject using a ratio between the first resistance and the second resistance.

Abstract: A system for monitoring hydration status of a human body is provided. The system comprises a plurality of sensors adapted to perform bio-impedance measurements at predefined locations on the body, thereby generating respective measurement data. The system further comprises a processing unit adapted to receive and to process the measurement data in order to estimate a body composition parameter for the respective locations on the body. In this context, at least one sensor is adapted to perform bio-impedance measurement at the throat region or esophagus region in order to detect a swallowing or fluid intake event.

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 system for respiratory monitoring of a subject comprises: a bioimpedance measurement sensor, which is configured for arrangement in relation to the subject for acquiring a bioimpedance signal; a processing unit, which is configured to receive the acquired bioimpedance signal and receive a reference signal, the reference signal representing a respiratory effort of the subject or a respiratory airflow at a time of bioimpedance signal acquirement, the processing unit being further configured to divide the bioimpedance signal into an effort component representing a respiratory effort of the subject and a flow component representing a respiratory airflow of the subject based on the received bioimpedance signal and the received reference signal.

Abstract: A method for time domain signal reconstruction for representing heart activity of a subject from a photoplethysmogram (PPG) signal comprises: receiving a PPG signal carrying information of heart activity of the subject; decomposing frequency information of the PPG signal into a plurality of components, each component having a frequency spectrum and a weight; for each component of the plurality of components: comparing the frequency spectrum of the component to a spectrum mask based on an estimate of heart rate of the subject; and adjusting the weight of the component based on the comparing; and reconstructing a time domain signal based on recombination of the plurality of components with adjusted weights.

Abstract: A system for motion artifact reduction in a photoplethysmogram, PPG, signal representing heart activity of a subject, comprises: a processing unit, said processing unit being configured to: receive a PPG signal carrying information of heart activity of the subject; receive a motion reference signal carrying information of a motion of the subject; form a PPG time-frequency representation, based on the PPG signal, and a motion reference time-frequency representation, based on the motion reference signal; and subtract a weighted representation of the motion reference time-frequency representation from the PPG time-frequency representation for reducing a motion artifact component in the PPG time-frequency representation and form a cleaned time-varying frequency spectrum of the PPG signal.

Abstract: An electronic system for bioimpedance signal acquisition, comprises: a current signal injection module configured for generating a current signal to be applied to a subject; a bioimpedance signal measurement module configured for measuring a bioimpedance signal based on a voltage generated by the current signal; a data quality detection module configured for detecting an AC or a DC level of the measured bioimpedance signal and detecting whether the AC or DC level is within or outside an AC reference value range and a DC reference value range, respectively; and a signal adaptation module configured for modifying at least one parameter of the current signal injection module or the bioimpedance signal measurement module based on said detection of the AC or DC level in relation to the AC reference value range and the DC reference value range, respectively.