A device includes an optical circuit that obtains a first signal at a first wavelength using light reflected from skin tissue of a patient, wherein the first wavelength has a high absorption coefficient for nitric oxide (NO) in blood flow and a second PPG signal at a second wavelength using light reflected from skin tissue of the patient, wherein the second wavelength has a low absorption coefficient for NO in blood flow. A measurement of a level of NO in blood flow is determined using the first PPG signal and the second PPG signal. A health condition is determined using the measurement of the level of NO, such as hyperglycemia or hypoglycemia.

A multi-target vital sign detection system includes a transmitter, a receiver and a processor. The transmitter is configured to transmit a millimeter wave signal to a detection area, and the receiver is configured to receive a reflecting millimeter wave signal reflected by a plurality of targets in the detection area. The processor is configured to: generate signal strength versus distance data by analyzing the received reflecting millimeter wave signal; perform an extreme value reserving process to generate signal extreme value versus distance data; perform a peak search algorithm to obtain a peak list including a plurality of peak values and a plurality of corresponding peak distances; generate a distance array including a plurality of distance variables; and perform a vital sign detection algorithm to generate multiple sets of vital sign data.

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.

A signal quality index evaluation circuit, comprises: a surrounding sensor; a zero-phase filter; and an evaluation circuit. The surrounding sensor senses its surrounding to generate a reference correction signal. The zero-phase filter is configured to generate a clean biological signal according to a biological signal and the reference correction signal, wherein the clean biological signal includes a plurality of period signals, and each one of the period signals has a biological value. The evaluation circuit is configured to calculate norm range according to the clean biological signal and one or more of the biological values of the period signals, and determine a difference between each one of the biological values corresponding to each one of the period signals and the norm range, the evaluation circuit is further configured to calculate and output a signal quality index according to the differences.

An apparatus for estimating bio-information, may include: a main body; a photoplethysmogram (PPG) sensor disposed in the main body and configured to measure a PPG signal from an object of a user; an internal pressure sensor disposed in a closed space formed in the main body, and configured to measure a pressure applied to the closed space when the object applies force to a surface of the main body; and a processor configured to estimate the bio-information of the user based on the PPG signal and the pressure applied to the closed space.

A medical device having a motion sensor is configured to sense a motion signal, generate ventricular pacing pulses in a non-atrial tracking ventricular pacing mode and detect atrial event signals from the motion signal during the non-atrial tracking ventricular pacing mode. The medical device may be configured to determine atrial event intervals from the detected atrial event signals, determine a frequency distribution of the determined atrial event intervals and determine an atrial rate based on the frequency distribution of the detected atrial event intervals.

A device may identify a peak timestamp and a trough timestamp based on a first principal component of photoplethysmography (PPG) data associated with a plurality of wavelength channels. The device may generate a peak absorption spectrum based on the peak timestamp and the PPG data, and may generate a trough absorption spectrum based on the trough timestamp and the PPG data. The device may determine a measured arterial blood spectrum based on the peak absorption spectrum and the trough absorption spectrum. The device may fit the arterial blood spectrum model to the measured arterial blood spectrum to determine a set of values, each value in the set of values corresponding to a variable in a set of variables. The device may calculate an arterial oxygen saturation value based on one or more values of the set of values.

An analysis method for the causal inference of human physiological network in multiscale time series signals includes the following steps: S1: decomposing physiological signals u.sub.1, u.sub.2, . . . , u.sub.m to be analyzed by using a noise-assisted multivariate empirical mode decomposition (NA-MEND) algorithm; S2: carrying out a causal analysis between two different physiological signals u.sub.i, u.sub.j, where i=1, 2, . . . , m, j=1, 2, . . . , m, and i≠j, to obtain a causality between the two signals; and S3: repeating step S2 for any two signals in u.sub.1, u.sub.2, . . . , u.sub.m until a causality between each two signals in u.sub.1, u.sub.2, . . . , u.sub.m is obtained to form the causal network. The present invention can effectively analyze the causal network of the physiological signals, thereby facilitating the application of the physiological signals.

A first input interface receives measured values of non-invasive blood pressure of a subject intermittently at a first frequency. A second input interface receives measured values of pulse wave transit time of the subject at a second frequency higher than the first frequency. A processor executes regression analysis processing based on the measured values received until when a measured value acquired by Nth (N is an integer that is no less than 2) measurement of the non-invasive blood pressure is received to acquire a regression equation in which the measured value is set as an explanatory variable. An information presenting device visibly presents estimated values of the non-invasive blood pressure calculated with the regression equation and the measured values, until a measured value acquired by (N+1)th measurement of the non-invasive blood pressure is received.

Provided is an identification device for identifying an individual on the basis of gait irrespective of the type of footwear, the identification device comprising a detection unit that detects a walking event on the basis of a walking waveform of a user, a waveform processing unit that normalizes the walking waveform on the basis of the detected walking event and generates a normalized waveform, and an identification unit that identifies the user on the basis of the normalized waveform.