A portable wireless neuromonitoring device can be used to diagnose and/or treat conditions of the brain and vision system. The device includes a sensor unit mountable on the head of a human subject and capable of recording signals from the brain in EEG and/or EFEG (electric field encephalography) mode, and the device can be used for simultaneous stimulus display and recording with latency of less than 1 millisecond. The device also includes electrodes that allow rapid set-up and measurement with low impedance contact with the scalp. The device can also be used in conjunction with virtual reality or alternate reality environments.

A method for determining a lowest stimulation threshold current level in a group of channels of a neuromonitoring device. The method includes stimulating tissue at a current level from a predetermined range of current levels as a sequence of pulses delivered at a frequency. The stimulating includes increasing the current level of each pulse in the sequence of pulses from an immediately preceding pulse by a first current increment. The method includes determining that a first evocation pulse from the sequence of pulses evokes a first muscular response. The method includes stimulating the tissue with a second evocation pulse from the sequence of pulses to evoke a second muscular response. The stimulating includes decreasing the frequency of the delivery of each pulse in the sequence of pulses and increasing the current level of each pulse in the sequence of pulses from the immediately preceding pulse by a second current increment. The method includes determining that the second evocation pulse from the sequence of pulses evokes the second muscular response.

Medical device systems include processing circuitry configured to acquire sensed cardiac signals associated with cardiac activity of a heart of a patient, and to analyze the sensed cardiac signals to determine if a noise signal is present within the cardiac signals.

Methods, systems and wearable devices for real-time mental stress assessment are provided. The methods and systems employ deep learning using a Gated Recurrent Unit (GRU) gating mechanism in a recurrent neural network with a sliding window approach applied to raw EEG data.

For patient weight estimation in a medical imaging system, a patient model, such as a mesh, is fit to a depth image. One or more feature values are extracted from the fit patient model, reducing the noise and clutter in the values. The weight estimation is regressed from the extracted features.

A process for measuring heart beats fiducial points and classifying heart beats includes sampling a raw ECG signal,—providing a first filtered signal,—providing a second filtered signal, detecting left and right limits data for the beats in said second filtered signal, and receiving the first filtered signal and receiving said left and right limits data, sampling and storing ECG curve data of beats from said first filtered signal synchronized by said left and right limits data and extracting fiducial points of said beats from said ECG curve data, said fiducial points including at least the QRS points values of the beats, and classifying each of said beats in classes based on a correlation of its ECG curve data with respect to average ECG curves data of classes of previously averaged classified beats.

Methods and systems for remote sleep monitoring are provided. Such methods and systems provide non-contact sleep monitoring via remote sensing or radar sensors. In this regard, when processing backscattered radar signals from a sleeping subject on a normal mattress, a breathing motion magnification effect is observed from mattress surface displacement due to human respiratory activity. This undesirable motion artifact causes existing approaches for accurate heart-rate estimation to fail. Embodiments of the present disclosure use a novel active motion suppression technique to deal with this problem by intelligently selecting a slow-time series from multiple ranges and examining a corresponding phase difference. This approach facilitates improved sleep monitoring, where one or more subjects can be remotely monitored during an evaluation period (which corresponds to an expected sleep cycle).

A method for predicting the presence of a malady of the respiratory system in a subject comprising: operating at least one electronic processor to transform one or more sounds of the subject that are associated with the malady into corresponding one or more image representations of said sounds; applying said one or more representations to at least one pattern classifier trained to predict the presence of the malady; and operating said processor to predict the presence of the malady in the subject based on at least one output of the at least one pattern classifier.

A system may obtain a set of features characterizing a segment of inertial measurement unit (IMU) data generated by an IMU of an ear-wearable device. The system may apply a machine learning model (MLM) that takes the features characterizing the segment of the IMU data as input. The system may determine, based on output values produced by the MLM, whether a user of the ear-wearable device has potentially been subject to physical abuse. The system may then perform an action in response to determining that the user of the ear-wearable device has potentially been subject to physical abuse.

An excreta identification device includes: a sound data acquisition unit that acquires sound data collected by a microphone arranged in a toilet; an excreta identification unit that identifies which of defecation, urination, and farting has been performed by inputting the acquired sound data to an identification model subjected to machine learning where sound data indicating any of defecation sound, urination sound, and farting sound is an input value, and which of defecation, urination, and farting has been performed is an output value; and an identification result output unit that outputs an identification result.