A medical having a motion sensor is configured to set an atrial event sensing parameter used for sensing atrial event signals from a motion signal produced by the motion sensor. The medical device sets an atrial event sensing parameter by applying a sensing window during each one of multiple ventricular cycles, determining a feature of the motion signal during the sensing window for at least a portion of the ventricular cycles, and setting the atrial event sensing parameter based on the determined features. The medical device may sense the atrial event from the motion signal according to the atrial event sensing parameter.

Provided is a film-type biomedical signal measuring apparatus configured in a such a way that a plurality of metallic thin film electrodes and a circuit unit are formed on a film-type piezoelectric element so as to easily attach the apparatus to the skin and an electrical signal as well as an electrical signal of a human body is simultaneously measured using the plurality of metallic thin film electrodes and the circuit unit. Accordingly, the film-type biomedical signal measuring apparatus simultaneously measures electrocardiogram (ECG) and ballistocardiogram (BCG) from the simultaneously measured electrical signal and vibration signal of the human body and extracts biomedical information of various types of health indexes such as a heart rate, a stress index, BCG, a blood pressure, an amount of physical activity, a respiration rate, and VO.sub.2max from the two different biomedical signals.

Data related to breathing action of a person on a bed is received. Tagging data that defines tags of disease state for the respiratory data is received. A respiratory-disease classifier is generated using respiratory cardiac data and the tagging data, the generating may include: training a convolutional neural network (CNN) configured to use as input i) the respiratory data and ii) the tagging data, the CNN configured to generate intermediate data; and training a recurrent neural network (RNN) configured to use as input the intermediate data, the RNN configured to generate a disease classification, the RNN may include i) a prospective long short-term memory (LSTM) network using as input later disease classifications and ii) a historic LSTM network using as input previous disease classifications.

A system and method for detecting brain concussion includes detecting and measuring of acceleration at one or more points on a subject's head. Sensors, which can be accelerometers placed against the head, detect and measure natural motions of the patient's head due to blood flow in the brain and resultant movement of tissue in the brain. An observation is then made, as compared with data corresponding to non-concussion, of a change in frequency response pattern exhibited when accelerations are plotted as a function of time or frequency, to identify probable concussion. Preferably the observation and comparison are made by a computer using an algorithm.

Cardiac data defining at least inter-beat interval (IBI) sequences is received. Tagging data that defines tags of sleep-states for the IBI sequences is received. A sleep-state classifier is generated using the cardiac data and the tagging data, the generating may include: extracting the IBI sequences from the cardiac data; training a convolutional neural network (CNN) using as input the cardiac data and the tagging data to generate intermediate data; and iteratively training a recurrent neural network (RNN) configured to produce state data as output, the iterative training of the RNN using i) the intermediate data as an initial input and ii) the intermediate data and a previous state data as subsequent input.

Doppler signal processing device for detecting an object according to a received wireless signal. The Doppler signal processing device includes a frequency analysis unit for generating a frequency domain signal vector according to at least one digital signal, an interference suppression unit for performing a suppression operation according to the frequency domain signal vector and a frequency domain interference estimation signal vector to generate an interference suppressed frequency domain signal vector, an interference estimation unit for generating the frequency domain interference estimation signal vector according to the frequency domain signal vector, a detection unit for generating a result signal according to the interference suppressed frequency domain signal vector, an error detection unit for optionally providing an error detection control signal to the interference estimation unit to adjust a rate of updating the frequency domain interference estimation signal vector.

In one embodiment, a Magnetic Resonance Imaging (MRI) apparatus includes: an RF coil configured to perform A/D conversion on a magnetic resonance (MR) signal received from an object and wirelessly transmit the MR signal; a main body configured to wirelessly receive the MR signal and generate a system clock; first communication circuitry configured to transmit the system clock by surface electric field communication using electric field propagation along a body surface of the object; and second communication circuitry provided in the RF coil and configured to receive the system clock transmitted by the surface electric field communication, wherein the RF coil is configured to operate based on the received system clock.

Systems, devices, and methods are disclosed herein for monitoring physiological data of subjects seated on a toilet, including systems, devices, and methods for monitoring loads and forces on a toilet seat. In some embodiments, systems, devices, and methods disclosed herein include a set of sensors that can measure loads and forces present at coupling points between a toilet seat and a base or other components of a toilet.

In an embodiment, a method includes: generating a displacement signal indicative of a distension of a surface of a skin; determining a temperature of the skin using a temperature sensor; during a calibration time interval, collecting a plurality of distension values from the displacement signal, the plurality of distension values associated with a respective plurality of temperature values determined using the temperature sensor, the plurality of temperature values being indicative of a temperature change of the skin; determining compensation coefficients associated with the plurality of temperature values; and after the calibration time interval, collecting a first distension value from the displacement signal, determining a first temperature value using the temperature sensor, and determining a blood pressure based on the first distension value, the first temperature value, and the determined compensation coefficients.

In some examples, a medical device system includes an electrode. The medical device system may include impedance measurement circuitry coupled to the electrode, the impedance measurement circuitry may be configured to generate an impedance signal indicating impedance proximate to the electrode. The medical device system may include processing circuitry that may be configured to identify a first component of the impedance signal. The first component of the impedance signal may be correlated to a cardiac event. The processing circuitry may be configured to determine that the cardiac event occurred based on the identification of the first component of the impedance signal.