A control unit (12) and method for deriving a measure of arterial compliance based on an acquired arterial volume variation signal and measured diastolic and systolic blood pressure measurements. An oscillometric blood pressure measurement device is used to obtain a first signal representative of arterial volume variations and to obtain blood pressure measurements. Both are measured as an applied pressure to an artery is varied by the oscillometric blood pressure measurement device. The first signal is processed to compile a dataset of values, ΔV, representative of the change in the arterial volume for set step changes, ΔP, in applied pressure, at different transmural pressure values. This set of values is numerically integrated to derive a function of arterial volume with transmural pressure. This function is differentiated to thereby derive a function of arterial compliance with transmural pressure.

A hemodynamic monitoring system monitors arterial blood pressure of a patient and provides a warning to medical personnel of a predicted future hypotension event of the patient. Sensed hemodynamic data representative of an arterial pressure waveform of the patient are received by a hemodynamic monitor. The received hemodynamic data is offset, based on a difference between a standard mean arterial pressure (MAP) threshold for hypotension and an adjusted MAP threshold for hypotension, to produce adjusted hemodynamic data. Waveform analysis of the adjusted hemodynamic data is performed, and a risk score representing a probability of a future hypotension event for the patient is determined based on the waveform analysis. A sensory alarm is invoked to produce a sensory signal in response to the risk score satisfying a predetermined risk criterion.

Systems and methods are provided for detecting and analyzing changes in a body. For example, a system includes an electric field generator configured to produce an electric field. The system includes an external sensor device configured to detect physical changes in the electric field, where the physical changes affect amplitude and frequency of the electric field. The system includes a quadrature demodulator configured to detect changes of the frequency of the output of the electric field generator. The system includes an amplitude reference source and an amplitude comparison switch configured to detect changes of the amplitude of the output of the electric field generator. The system includes a signal processor configured to analyze the changes of the amplitude and frequency of the output of the electric field generator.

This disclosure relates generally to assessment of cognitive workload using breathing pattern of a person, where cognitive workload is the amount of mental effort required while doing a task. The method and system provides assessment of cognitive workload based on breathing pattern extracted from photoplethysmograph (PPG) signal, which is collected from the person using a wearable device. The PPG signal collected using the wearable device are processed in multiple stages that include breathing signal extraction to extract breathing pattern. The extracted breathing pattern is used for assessment of cognitive workload using a generated personalized training model, wherein the personalized training model is generated and dynamically updated for each person based on selection of a sub-set of breathing pattern features using feature selection and classification techniques that include maximal information coefficient (MIC) techniques. Finally based on personalized training model, the extracted breathing pattern is classified as high cognitive workload or low cognitive workload.

A system and method for managing the care of a patient includes receiving (410) physiological signals of a patient; extracting (440) respiration information from the physiological signals; determining a vital sign of the patient by: using (450, 460) the respiration information to determine portions of the physiological signals, or of vital sign information extracted from the physiological signals, that correspond to the expiration phase of the respiratory cycle; determining (470) a vital sign of the patient using only the portions of the physiological signals, or of the vital signal information, that correspond to an expiration phase of the respiratory cycle; and displaying an indication of the determined vital sign at an output device.

The present disclosure provides systems and method of analyzing whether a sleep epoch is a REM sleep epoch or a wake epoch. In accordance with aspects of the present disclosure, a computer-implemented method includes accessing cardiopulmonary coupling data spanning a sleep period for a person, identifying an epoch in the sleep period corresponding to very-low frequency coupling in the cardiopulmonary coupling data, accessing high-frequency coupling data and/or low-frequency coupling data in the cardiopulmonary coupling data corresponding to the epoch, and designating the epoch as a REM sleep epoch or as a wake epoch based on the high-frequency coupling data and/or the low-frequency coupling data corresponding to the epoch, where the epoch is designated based on the cardiopulmonary coupling data without using non-cardiopulmonary coupling physiological data.

The present disclosure provides a non-contact heart rhythm category monitoring system, which includes steps as follows. Facial images are continuously captured through an image sensor; images of a continuous target area for a predetermined duration are extracted from the facial images; non-contact physiological signal related to heartbeats are captured from the images of the continuous target area; the non-contact physiological signal are classified into a normal heart rhythm, an atrial fibrillation and a non-atrial fibrillation arrhythmia.

The present disclosure provides methods of measuring of an analyte in a subject to remove a measurement artifact by using a forecasting model to determine the true analyst concentration in a subject. Also herein, the present disclosure provides parameters and models to estimate the true analyte concentration in a subject.

The inventive concept relates to a body composition analysis system. A body composition analysis system according to an embodiment of the inventive concept includes a sinusoidal signal generator, a synchronous detector, and a bioimpedance analyzer. The sinusoidal signal generator converts a digital sinusoidal signal having a target frequency into an analog sinusoidal signal. The synchronous detector extracts a target frequency component of a bioelectrical signal generated in response to an analog sinusoidal signal based on the digital sinusoidal signal. The bioimpedance analyzer calculates the bioimpedance based on the target frequency component of the bioelectrical signal. According to the inventive concept, it is possible to improve the selectivity for extracting the target frequency component of the bioelectrical signal and to reduce the area and variations of characteristics for the implementation of the integrated circuit.

This disclosure relates generally to a Parkinson's disease detection system. Parkinson's disease is a neuro-degenerative disorder affecting motor and cognitive functions of subjects. Since symptom manifestation is limited in Parkinson's disease, identifying Parkinson's disease in the early stage is a challenging task. The present disclosure overcomes the limitations of the conventional methods for detecting Parkinson's disease by utilizing a graph theory approach. Here, each pressure sensor attached to an insole corresponding to a plurality of pressure points associated with a foot of the subject is considered as a node of a connectivity graph. The foot dynamics analysis is performed based on a metric known as mediolateral stability index and the mediolateral stability index is calculated by utilizing a betweenness centrality associated with each node of the connectivity graph. Further, the mediolateral stability index is compared with standard values to detect the intensity of the Parkinson's disease.