In some examples, a method includes receiving a signal indicative of a blood pressure of a patient and identifying at least one first portion of the signal comprising a first characteristic of the signal exceeding a first threshold. The method also includes identifying at least one first portion of the signal comprising a second characteristic of the signal exceeding a second threshold, the first characteristic being different than the second characteristic. The method further includes determining a filtered signal indicative of the blood pressure of the patient by excluding the at least one first portion and the at least one second portion from the signal. The method includes determining a set of mean arterial pressure values based on the filtered signal and determining an autoregulation status of the patient based on the set of mean arterial pressure values.

A processor-implemented method comprises obtaining current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition.

In some examples, a method includes receiving a signal indicative of a blood pressure of a patient and identifying at least one first portion of the signal comprising a first characteristic of the signal exceeding a first threshold. The method also includes identifying at least one first portion of the signal comprising a second characteristic of the signal exceeding a second threshold, the first characteristic being different than the second characteristic. The method further includes determining a filtered signal indicative of the blood pressure of the patient by excluding the at least one first portion and the at least one second portion from the signal. The method includes determining a set of mean arterial pressure values based on the filtered signal and determining an autoregulation status of the patient based on the set of mean arterial pressure values.

A method for determining the respiratory rate R of at least one user includes at least the following steps including acquisition of at least one photoplethysmogram S(t), estimation of the heart rate Fc from at least one photoplethysmogram S(t) acquired from the at least one user, obtainment (130) of at least three signals, generation of a signal S8 from the at least three obtained signals, algorithmic processing (140) of each obtained signal and of S8 by at least two algorithms Ai and Aj so as to obtain at least two estimated respiratory rates for each processed signal, computation (150) of at least two intermediate respiratory rates rm and rn from the estimated respiratory rates (Rx.Ai, Rx.Aj), determination (160) of the respiratory rate R by computing the median of the at least two intermediate respiratory rates.

A blood pressure measuring apparatus for measuring blood pressure of a subject using a cuff attached to the subject includes a processor and a memory to store instructions that are readable by the processor. As the instructions are executed by the processor, the apparatus generates a pulsation signal of the subject while increasing an inner pressure of the cuff; generates a first-order differential signal by differentiating the pulsation signal. The apparatus then detects an inappropriate attachment state of the cuff based on at least one of: a bottom value of the first-order differential signal; a relationship between a peak value and the bottom value of the first-order differential signal; an amplitude amount of the first-order differential signal including the bottom value in a unit time; and a relationship between the amplitude amount and an amplitude amount of the first-order differential signal including the peak value in a unit time.

An apparatus and a method for monitoring a bio-signal measuring condition are disclosed. The apparatus includes a bio-signal receiver configured to receive a bio-signal that is measured from a user, and a processor configured to extract any one or any combination of a waveform feature, a period feature, and an amplitude feature, from the received bio-signal, determine whether the extracted any one or any combination of the waveform feature, the period feature, and the amplitude feature are normal, using at least one predetermined determination reference corresponding to the extracted any one or any combination of the waveform feature, the period feature, and the amplitude features, and monitor a measuring condition of the received bio-signal, based on whether the extracted any one or any combination of the waveform feature, the period feature, and the amplitude feature are determined to be normal.

An apparatus and method for detecting a bio-signal feature are provided. The apparatus according to one aspect may include: a bio-signal acquirer configured to acquire a bio-signal; and a processor configured to generate an envelope signal of the bio-signal, and detect at least one feature of the bio-signal based on a difference between the envelope signal and the bio-signal.

Various examples are provided for non-contact vital sign acquisition. Information can be provided regarding vibrations of a target using a radar signal such as, e.g., non-contact vital sign measurement. Examples include estimation of heart rate, change in heart rate, respiration rate, and/or change in respiration rate, for a human or other animal. Implementations can produce one or both rates of vibration and/or change in one or both rates of vibration for a target other than an animal or human experiencing two vibrations at the same time, such as a motor, a vehicle incorporating a motor, or another physical object. Some implementations can estimate the respiration movement in the radar baseband output signal. The estimated respiration signal can then be subtracted from radar signals in the time domain and, optionally, can be further enhanced using digital signal processing techniques, to produce an estimate of the heartbeat pulses.

Systems and methods for identifying and removing artifacts from electrodermal activity (EDA) data are described herein. A method includes identifying artifacts in segments of EDA data using unsupervised machine learning based on feature vectors extracted from segments of the data. After the artifacts are identified, they can be removed from the EDA data. Artifact-free EDA data can be used to estimate a patient's nociceptive state, which in turn can be used to modify a dosage of anesthetic drugs administered to the patient based on this estimation.

A biological information analyzing device includes: an indicator extraction unit that extracts an indicator pertaining to a characteristic of a blood pressure waveform using data of the blood pressure waveform obtained by a sensor, which is worn on a user's body and can non-invasively measure the blood pressure waveform for each of heartbeats, continuously measuring the blood pressure waveform; and a processing unit that carries out a process based on the extracted indicator.