Systems and methods for control and signal processing in variable inductance, resonant circuit monitoring devices are disclosed, including improved techniques for energizing the sensor resonant circuit using excitation signal frequency sweeps, techniques for validating sensor readings and characterizing sensor frequency outputs to measured physical parameters and improved techniques for isolating background electromagnetic noise and distinguishing knows from sensor measurement signals.

Provided is a stress measurement system and a stress measurement method that are capable of measuring the stress level of a subject without taking time and effort. A stress measurement system 1 includes a sensor unit 31 that detects a plurality of detection target gases based on substances contained in a specimen of a subject and outputs a plurality of detection values corresponding to respective detection results of the plurality of detection target gases, and a control unit that determines a stress level of the subject, based on a combination of the plurality of detection values. In addition, the substances contained in the specimen may include a substance serving as a raw material for a brain neurotransmitter.

A system and a method include an implantable medical device (IMD) having one or more inputs configured to receive one or more sensed signals from one or more electrodes. A plurality of filters are configured to filter the one or more sensed signals and output a plurality of filtered signals. Memory is configured to store program instructions. A processor, when executing the program instructions, is configured to receive the plurality of filtered signals, and analyze the plurality of filtered signals to determine a desired one of the plurality of filters.

A system (101) for monitoring a physiological condition of a user (104) is disclosed herein. The system (101) includes a receiving module (110) configured to receive a plurality of short-term segments of Heart Rate Variability (HMI) (302) or short-term electrocardiogram (ECG) segments (402) or short voice recordings (602) from the user (104) recorded at different time points. The system includes a stitching module (114) for stitching the plurality of short-term segments and creating a stitched segment. The system further includes an extracting module (116) extracting feature from the stitched segment and a predicting module (118) for predict the physiological condition, based on the feature.

In some embodiments, an electric field generator generates an electric field at a nominal frequency. A detector measures, at multiple time points during a measuring period, one or more properties of the generated electric field. In various embodiments, the one or more properties of the electric field change over time due to interactions with a human body in a reactive near-field region of the electric field. From the measured one or more properties, a computation unit determines one or more periodic behaviors (such as a respiration or heartbeat) and one or more non-periodic behaviors (such as movement of a limb). The computation unit also computes, from at least one of the periodic and non-periodic behaviors, one or more physiological parameters of the human body. From the one or more physiological parameters, the computation unit detects one or more symptoms of a condition of the human body.

A personal monitoring system includes one or more passive biometric sensors and a communication device. A passive biometric sensor is operable to sense a body condition of a body in accordance with a sense signal at a sense frequency to produce sensed data of a body condition. The passive biometric sensor is further operable to transmit an e-field signal via the body regarding the sensed data, wherein the e-field signal is in accordance with an e-field transmit/receive frequency. The communication device is operable to receive the e-field signal via the body. The communication device is further operable to recover the sensed data from the received e-field signal.

An ECG signal acquisition system includes a first amplifier which has a non-inverting input adapted to be coupled to a first differential input, an inverting input adapted to be coupled to a second differential input, and an output. The system includes first and second biasing resistors coupled between the non-inverting and inverting inputs of the first amplifier. The system includes an average estimation circuit which has a first input coupled to the non-inverting input of the first amplifier and a second input coupled to the inverting input of the first amplifier. The system includes a driver amplifier which has an inverting input coupled to the output of the average estimation circuit, a non-inverting input coupled to receive a reference common-mode voltage, and an output. The system includes a low-pass filter coupled between the output of the driver amplifier and the biasing resistors.

The present disclosure is related to methods, systems and apparatus for performing electrophysiological measurements utilizing three or more electrodes attached to a patient. The system in various embodiments may include three or more electrodes attached to the patient and at least one analog-to-digital converter with external circuitry electrically coupled to the electrodes. The system may further include a microprocessor for driving the analog-to-digital conversion process, various inputs and variable frequency current outputs electrically coupled to the microprocessor for receiving signals from the electrodes and sending driven current signals to the electrodes.

An exemplary wearable brain interface system includes a head-mountable component and a control system. The head-mountable component includes an array of photodetectors that includes a photodetector comprising a single-photon avalanche diode (SPAD) and a fast-gating circuit configured to arm and disarm the SPAD. The control system is for controlling a current drawn by the array of photodetectors.

An artificial intelligence self-learning-based automatic electrocardiography analysis method and apparatus, the method comprising data preprocessing, heartbeat feature detection, interference signal detection and heartbeat classification based on deep learning, signal quality evaluation and lead combination, heartbeat verification, analysis and calculation of electrocardiography events and parameters, and finally automatic output of reporting data, realizing an automated analysis method having a complete and rapid flow. The automatic electrocardiography analysis method may also record modification information of an automatic analysis result, collect modified data, and feed same back to the depth learning model to continue training, thereby continuously making improvements and improving the accuracy of the automatic analysis method.