Koninklijke Philips Corporation N.V.

Xi Long

Ronaldus Maria Aarts

Reinder Haakma

Pedro Miguel Fonseca

Nicolaas Gregorius Petrus Den Teuling

Rick Bezemer

Wouter Herman Peeters

Pedro Miguel Ferreira Dos Santos Da Fonseca

Shaoxiong Sun

In a sensor system (), output signals of at least one PPG sensor () are processed to derive a modified pulse amplitude variation (PAV) value, being modified to take account of a baseline variation of the PPG sensor output signal. In particular, the modified PAV is derived through performing a modification step () in which either: a baseline variation of the PPG sensor output is derived and combined with a previously derived PAV, or, a PPG sensor output is first processed to perform baseline variation compensation, in advance of then deriving a PAV from the compensated signal.

Sensor system and sensing method for use in assessment of circulatory volume

The present disclosure pertains to a system configured to detect slow wave sleep and/or non-slow wave sleep in a subject during a sleep session based on a predicted onset time of slow wave sleep and/or a predicted end time of slow wave sleep that is determined based on changes in cardiorespiratory parameters of the subject. Cardiorespiratory parameters in a subject typically begin to change before transitions between non-slow wave sleep and slow wave sleep. Predicting this time delay between the changes in the cardiorespiratory parameters and the onset and/or end of slow wave sleep facilitates better (e.g., more sensitive and/or more accurate) determination of slow wave sleep and/or non-slow wave sleep.

System and method for slow wave sleep detection

The present disclosure pertains to a system () configured to determine spectral boundaries () for sleep stage classification in a subject (). The spectral boundaries may be customized and used for sleep stage classification in an individual subject. Spectral boundaries determined by the system that are customized for the subject may facilitate sleep stage classification with higher accuracy relative to classifications made based on static, fixed spectral boundaries that are not unique to the subject. In some implementations, the system comprises one or more of a sensor (), a processor (), electronic storage (), a user interface (), and/or other components.

System and method for determining spectral boundaries for sleep stage classification

The present disclosure pertains to a system configured to determine one or more parameters based on cardiorespiratory information from a subject and determine sleep stage classifications based on a discriminative undirected probabilistic graphical model such as Conditional Random Fields using the determined parameters. The system is advantageous because sleep is a structured process in which parameters determined for individual epochs are not independent over time and the system determines the sleep stage classifications based on parameters determined for a current epoch, determined relationships between parameters, sleep stage classifications determined for previous epochs, and/or other information. The system does not assume that determined parameters are discriminative during an entire sleep stage, but maybe indicative of a sleep stage transition alone. In some embodiments, the system comprises one or more sensors, one or more physical computer processors, electronic storage, and a user interface.

System and method for cardiorespiratory sleep stage classification

An actigraphy method includes receiving a physiological parameter signal as a function of time for a physiological parameter other than body motion (such as electrocardiography or a respiration monitor), computing a body motion artifact (BMA) signal as a function of time from the physiological parameter signal (for example, using a local signal power signal, a local variance signal, a short-time Fourier transform, or a wavelet transform over epochs of duration on order a few minutes or less), and computing an actigraphy signal as a function of time from the BMA signal, for example by applying a linear transform to the BMA signal and optionally applying filtering such as median removal and/or high-pass filtering.

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Eindhoven, Netherlands

Xi Long