The present invention relates to a quantitative electroencephalogram (QEEG) monitor and system capable of monitoring and displaying simultaneously neuropathological characteristic and activity of both sides of a subject's brain. The methods include various indices and examination of differences in these indices by which neurophysiological conditions or problems can be identified and treated. These methods, and the systems and devices using these methods preferably can be used for identifying these neurophysiological conditions or brain dysfunction with monitors and methods for seizure detection, for sedation monitoring, for anesthesia monitoring, and the like. These bilateral brain monitoring methods and systems, and the devices using these methods can be used by individuals or clinicians with little or no training in signal analysis or processing. These bilateral monitoring methods can also be used in a range of applications.

The present invention relates to a method and system for estimating blood pressure of a subject. In particular, but not exclusively, the method involves receiving a photoplethysmogram (PPG) signal from a light-based Pulse-Plethysmography sensor applied to the skin of a subject and reconstructing a pulse blood pressure waveform between systolic and diastolic blood pressure of the subject. Additionally, but not exclusively, the method involves processing the pulse blood pressure waveform and reconstructing an absolute blood pressure waveform of the subject.

A method for generating stimulation parameters, an electrical stimulation control apparatus and an electrical stimulation system are provided. After receiving a brainwave signal, the brainwave signal is decomposed to obtain a first sub-signal and a second sub-signal. Then, the first sub-signal is analyzed to obtain an intrinsic frequency series, and the second sub-signal is converted to a Boolean signal. Subsequently, the intrinsic frequency series and the Boolean signal, which serve as a set of stimulation parameters, are outputted to the stimulator, enabling the stimulator to generate a stimulus signal.

The present disclosure discloses an intelligent screening algorithm and automatic upgrading system for congenital heart diseases of newborns based on big data. The key point of the technical solution is as follows: The intelligent screening algorithm and automatic upgrading system includes a heart sound data module, a heart sound data processing module, a blood oxygen data module, a blood oxygen data processing module, a network upgrading module, a database, an intelligent analysis module and a congenital heart disease evaluation module; the heart sound data module is configured to acquire various data of heart sounds of a newborn for centralized processing; and the heart sound data processing module is configured to process the data in the heart sound data module and extract heart sound feature parameters.

An electronic device for providing biometric information is provided. The electronic device includes a sensor module, a memory, and a processor electrically connected to the sensor module and the memory. The processor obtains a biometric signal from the sensor module at a predetermined time interval, determines whether a user is in a first state on the basis of the obtained biometric signal, in case the user is in a first state, obtains a representative value for a respective of the at least one biometric signal, defines the obtained representative value for the respective of the at least one biometric signal as a candidate reference value for a corresponding biometric signal, determines a candidate reference value satisfying a predetermined condition as a first reference value for the corresponding biometric signal, and updates a second reference value previously configured for the corresponding biometric signal on the basis of the first reference value.

The invention relates to an auscultation device (1) for capturing and evaluating body sounds, having an auscultation element (2) having a housing (4, 4a) and arranged therein: an audio system (5), a power supply (6), a computing unit (7), means for data transmission (7a) and a control interface (8), and having a data processing system having a user interface (10), means for data transmission (14a), a memory (13) and a computing unit (14), wherein the audio system (5) comprises at least one microphone unit (5a, 5b) for capturing the body's own sounds and this at least one microphone unit (5a, 5b) comprises a microphone (i) and a connection element (ii) between microphone (i) and housing (4, 4a).

Determining low power frequency range information from spectral data. Raw signal data can be adjusted to increase dynamic range for power within low power frequency ranges as compared to higher-power frequency ranges to determine adjusted source data valuable for acquiring low power frequency range information. Low power frequency range information can be used in the analysis of a variety of raw signal data. For example, low power frequency range information within electroencephalography data for a subject from a period of sleep can be used to determine sleep states. Similarly, automated full-frequency spectral electroencephalography signal analysis can be useful for customized analysis including assessing sleep quality, detecting pathological conditions, and determining the effect of medication on sleep states.

A method, apparatus, and computer program monitor a user's heart activity during a physical exercise. A heart activity measurement signal representing the user's heart activity is acquired and a phase component of the heart activity measurement signal is monitored. On the basis of the monitoring of the phase component, one or more actions are carried out.

Implementations described herein disclose an artificial intelligence (AI) based method for generating an oxygen saturation level output signal using the trained neural network. In one implementation, the method includes receiving a photoplethysmographic (PPG) signal, the PPG signal including a red PPG signal and an infrared PPG signal, generating an input feature matrix by performing time-frequency transform of the PPG signal, training a neural network using the input feature matrix and an oxygen saturation level input signal, and generating an oxygen saturation level output signal using the trained neural network.

A MRS (magnetic resonance spectroscopy or nuclear magnetic resonance NMR)-based PTSD (post-traumatic stress disorder) and mTBI (mild traumatic brain injury) diagnostic system and method uses MRS signals, already pre-processed by the MRS scanner software. The signals are collected in vivo from specific regions of the brain. A wavelet decomposition is applied to the MRS signals, and the amplitude of the wavelet coefficients and their location in the MRS signals are used as features for training diagnostic classifiers of disease states. These classifiers are identified through analysis of features of individuals whose health status is known. Once the classifiers are trained, patients can be diagnosed by using the same wavelet features extracted from in vivo MRS scans of their brain regions.