A patient monitoring system includes: a biomedical sensor including: a transducer configured to produce a signal corresponding to a biological function; a sensor converter configured to convert the signal to a converted signal; and a transmitter configured to produce a communication, based on the converted signal, that is indicative of one or more values of the biological function, and to send the communication wirelessly; and a base station including: a receiver configured to receive the communication wirelessly and to produce a receiver output signal; a base station interface configured to produce a base station output signal indicative of the one or more values of the biological function; and at least one output port to receive the base station output signal and configured to be hard-wire connected to a display that is configured to display information indicative of the biological function.

Dysglycemia as a risk factor for neurodevelopmental disorder or developmental diabetes. The risk is assessed based on measurement of a glucose control indicator in a blood sample. One particular example of a neurodevelopmental disorder is retinopathy of prematurity in an infant. One particular example of a glucose control indicator is ‘comprehensive glycated hemoglobin fraction’ or ‘comprehensive glycated albumin fraction.’ This is calculated using ‘total whole blood protein’ in the denominator. In the case of chronic hyperglycemia, there is an increased risk of proliferative retinopathy of prematurity. In the case of chronic hypoglycemia, there is an increased risk of non-proliferative retinopathy of prematurity. This ‘total whole blood protein’ technique could also be used to determine the glucose control status in other types of patients.

A system for achieving optimal sensor placement and enhanced signal quality for monitoring maternal and fetal activities is disclosed. The system includes a monitoring device and a computing unit. The monitoring device is configured for monitoring maternal and fetal activities and providing guidance to the user via the computing unit upon detecting a feature of interest. The monitoring device includes a plurality of sensors, a data acquisition and transmission unit, one or more reference electrodes, and a ground electrode. Based on personal data acquired using the computing unit, the system utilizes a statistical or machine learning model which incorporates one or more subsets of the personal data to determine the optimal sensor placement close to the fetal heart position. Following sensor placement, the monitoring device performs a signal quality assessment and selects the optimal sensors to ensure reliable information on maternal and fetal activities is obtained.

Techniques are disclosed for measuring the corpus callosum volume of a fetus using magnetic resonance imaging. A scanogram of a fetus is acquired, and a detection area is determined using the corpus callosum position of the fetus in the scanogram. Magnetic resonance scanning is performed on the detection area to obtain a diffusion weighted image, with a gradient direction that is orthogonal or normal to an extending direction of fiber bundles of the corpus callosum. A fetal head image is cropped in the diffusion weighted image, and a predetermined threshold is applied to obtain an image including pixels having a brightness value that is greater than the threshold. Image processing is performed on the binarized image, with the largest region therein being identified as the corpus callosum, and the sum of voxel dimensions associated with the signal of the largest region being calculated as the corpus callosum volume.

A system for monitoring fetal health data and mother health data comprises a belly-covering garment that is configured to at least partially cover a belly and to hold one or more sensor modules directly adjacent to the belly. One or more sensor modules disposed within the belly-covering garment. The one or more sensor modules comprise a pulse-oximeter sensor that gathers pulse oximetry data from the mother through contact with the belly. The one or more sensor modules also comprise an accelerometer sensor that gathers movement data from the mother. Additionally, the one or more sensor modules comprise a fetal sensor that gathers health data from a fetus within the belly.

This disclosure provides a fetal-blood-oxygen-saturation estimation technique using a deep neural network without performing explicit fetal signal extraction from mixed maternal-fetal photoplethysmogram (PPG) signals. In one aspect, the disclosed fetal-blood-oxygen-saturation estimation technique receives multiple channels of PPG signals from two or more photodetectors detecting transabdominal diffused light from two or more light sources emitting two or more distinct wavelengths, wherein the photodetectors and light sources are positioned on a maternal abdomen. Note that each channel of the multiple channels of PPG signals includes mixed maternal-fetal PPG signals. Next, the disclosed estimation technique processes the received PPG signals using a trained deep neural network to directly estimate fetal-blood-oxygen-saturation by: tagging the PPG signals with a set of signal-quality levels; feeding the tagged PPG signals as inputs to the deep neural network; and directly inferring, by the deep neural network, fetal-blood-oxygen-saturation estimations and associated confidence levels based on the tagged PPG signals.

A system for monitoring fetal wellbeing over time during pregnancy includes a sensor coupled to a pregnant woman; a processor communicatively coupled to the sensor; and a computer-readable medium having non-transitory, processor-executable instructions stored thereon. Execution of the instructions causes the processor to perform a method including: acquiring a signal from a sensor; processing the signal to identify and extract a parameter of interest from the signal; and analyzing the parameter of interest to determine a degree of fetal wellbeing. The parameter of interest may include one or more of: an average fetal heart rate, an average fetal heart rate variability, a fetal kick or movement count, an average placental oxygenation level, an average placental temperature, an average placental pH, an average amount of amniotic fluid, a fetal heart rate profile, a fetal heart rate variability profile, and a fetal movement profile.

Systems, devices, and methods for performing trans-abdominal fetal oximetry and/or trans-abdominal fetal pulse oximetry using physiological characteristics and/or a calibration factor may receive a physiological characteristic of a pregnant mammal and determine one or more potential impact(s) of the physiological characteristic on a behavior of an optical signal projected into the abdomen of the pregnant mammal Then a calibration factor for the optical signal responsively to the impact. The calibration factor may then be used to calibrate a fetal detected electronic signal so that a level of fetal hemoglobin oxygen saturation may be determined.

The present invention is directed to a method for health monitoring using one or more sensors comprising first measuring (100) a body composition via one or more sensors. The measured body composition is then classified (102) into one of a plurality of categories. An at least one setting to be used for the health monitoring is adjusted (104) based on the classified body composition. Then, the health monitoring is performed (106) using the adjusted at least one health monitoring setting, wherein at least one of the sensors used to measure the body composition may also be used to perform the health monitoring.

Provided are a medical diagnosis apparatus and a medical diagnosis method using the same. According to an embodiment, the medical diagnosis apparatus may include: a main body; a chair unit movably supported by the main body and on which an object is positioned; a diagnosis part that is movably connected to the main body and is spaced apart from the chair unit by a preset first distance in one plane; a controller configured to generate a control signal for moving the diagnosis part according to preset information; and a first driving device configured to generate a driving force for moving the diagnosis part according to the control signal.