A neural analysis and treatment system includes a computing device with a memory for storing an application that is executable on a processor to receive amplitude-integrated electroencephalography (aEEG) and range-EEG (rEEG) measurements associated with a patient. The systems determine a spectral edge frequency (SEF) measurement from the received EEG measurements, and determine one or more neural characteristics of the patient according to the determined SEF, aEEG, and rEEG measurements. These neural characteristics may then be used to identify and implement an appropriate therapeutic treatment.

Respiratory variables are estimated on a per-breath basis from airway pressure and flow data acquired by airway pressure and flow sensors (20, 22). A breath detector (28) detects a breath interval. A per-breath respiratory variables estimator (30) fits the airway pressure and flow data over the detected breath interval to an equation of motion of the lungs relating airway pressure, airway flow, and a single-breath parameterized respiratory muscle pressure profile (40, 42) to generate optimized parameter values for the single-breath parameterized respiratory muscle pressure profile. Respiratory muscle pressure is estimated as a function of time over the detected breath interval as the single-breath parameterized respiratory muscle pressure profile with the optimized parameter values, and may for example be displayed as a trend line on a display device (26, 36) or integrated (32) to generate Work of Breathing (WoB) for use in adjusting settings of a ventilator (10).

In respiratory monitoring, a breathing cycle detector (44) detects a breath interval in airway pressure and/or flow data. A respiratory parameters estimator and validator (30) asynchronously fits the airway pressure and airway flow data to an equation of motion of the lungs relating airway pressure and airway flow to generate asynchronously estimated respiratory parameters for the breath interval, using a sliding time window that is not synchronized with the breath interval. The asynchronously estimated respiratory parameters for the breath interval are validated using at least one physiological plausibility criterion defined with respect to the breath interval. Responsive to failure of the validation, the airway pressure and airway flow data are synchronously fitted to the equation of motion of the lungs to generate synchronously estimated respiratory parameters for the breath interval. The synchronous fitting is performed in a time window aligned with the breath interval.

Disclosed herein are methods, systems, and media for pressure sensing for physiological measurements. In some embodiments, a method comprises: obtaining sensor data from one or more sensors disposed in or on a wrist-worn device worn on a wrist of a user. The method may involve determining, based on the sensor data, a measure of a contact pressure of a bottom portion of the wrist-worn device on the wrist of the user. The method may involve based on the measure of the contact pressure, providing one or more instructions to the user to adjust the wrist-worn device to bring about a change in the contact pressure toward a target contact pressure or a target contact pressure range. The method may involve initiating a physiological measurement of the user using other sensors disposed in or on the wrist-worn device.

Parametric model based computer implemented methods for determining the stiffness of a bone, systems for estimating the stiffness of a bone in vivo, and methods for determining the stiffness of a bone. The computer implemented methods include determining a complex compliance frequency response function Y(f) and an associated complex stiffness frequency response function H(f) and fitting a parametric mathematical model to Y(f) and to H(f). The systems include a device for measuring the stiffness of the bone in vivo and a data analyzer to determine a complex compliance frequency response function Y(f) and an associated complex stiffness frequency response function H(f). The methods for determining the stiffness include fitting a parametric model to stiffness of the skin-bone complex as a function of frequency H(f) and the compliance of the skin-bone complex as a function of frequency Y(f).

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.

A diagnostic Electrochemical Impedance Spectroscopy (EIS) procedure is applied to measure values of impedance-related parameters for one or more sensing electrodes. The parameters may include real impedance, imaginary impedance, impedance magnitude, and/or phase angle. The measured values of the impedance-related parameters are then used in performing sensor diagnostics, calculating a highly-reliable fused sensor glucose value based on signals from a plurality of redundant sensing electrodes, calibrating sensors, detecting interferents within close proximity of one or more sensing electrodes, and testing surface area characteristics of electroplated electrodes. Advantageously, impedance-related parameters can be defined that are substantially glucose-independent over specific ranges of frequencies. An Application Specific Integrated Circuit (ASIC) enables implementation of the EIS-based diagnostics, fusion algorithms, and other processes based on measurement of EIS-based parameters.

System comprises a first sensing unit, attached to a proximal portion of a human body with a joint of the human body as a reference, for measuring an acceleration of the proximal portion or an angular velocity of the proximal portion; a second sensing unit, attached to a distal end portion of the human body, for measuring an acceleration of the distal end portion or the angular velocity of the distal end portion; a processing unit for determining an angle of the joint between the proximal portion and the distal end portion on the basis of the measured acceleration or the measured angular velocity and determining a spasticity time point at which resistance to motion of the distal end portion is received; and a display unit for displaying spasticity evaluation information for a spasticity evaluation on the basis of the angle of the joint and the spasticity time point.

According to the present disclosure, a heart rate detection method includes measuring an ECG waveform, calculating a heart rate from the ECG waveform, calculating a difference value of a potential of the ECG waveform between sampling time points, calculating an integrated value obtained by integrating the difference value between a measurement time point and any time point before measurement, and determining whether the heart rate at the measurement time point is to be displayed, by comparing the integrated value to a reference value.

Blood pressure signals are reconstructed from PhotoPlethysmoGraphy (PPG) signals by: receiving PPG signals including systolic, diastolic and dicrotic phases; and determining first and second derivatives of the PPG signals and: a first set of values indicative of lengths of the signal paths of the PPG signal, the first derivative and the second derivative thereof in the systolic, diastolic and dicrotic phases; a second set of values indicative of relative durations of the PPG signal and the first and second derivatives thereof in the systolic, diastolic and dicrotic phases; and a third set of values indicative of the time separation of peaks and/or valleys in subsequent waveforms of the PPG signal. Reconstruction also includes applying artificial neural network processing to the first, second and third set of values. The artificial neural network processing includes artificial neural network training as a function of blood pressure signals to produce reconstructed blood pressure signals.