A method of operating a radar sensor system that is configured to determine range and velocity information from radar waves reflected by a scene in an interior of a vehicle for vital sign detection. The method includes steps to decompose reflected and received signals into range and velocity information, to measure the movement over time in specified range gates and to evaluate the similarities between them. Based on the characteristics of similar behaving range bins, it can be decided whether any detected movement is related to an internal or external disturbance or by vital signs.

A method of generating a model for generating a synthetic electrocardiography (ECG) signal comprises: receiving subject-specific training data for machine learning, said training data comprising a photoplethysmography (PPG) signal acquired from the subject and an ECG signal acquired from the subject, wherein the ECG signal provides a ground truth of the subject for associating the ECG signal with the PPG signal; using associated pairs of a time-series of the PPG signal and a corresponding time-series of the ECG signal as input to a deep neural network, DNN; and determining, through the DNN, a subject-specific model relating the PPG signal of the subject to the ECG signal of the subject for converting the PPG signal to a synthetic ECG signal using the subject-specific model.

Embodiments herein relate to reflective optical medical sensor devices. In an embodiment, a reflective optical medical sensor device including a central optical detector and a plurality of light emitter units disposed around the central optical detector is provided. A plurality of peripheral optical detectors can be disposed to the outside of the plurality of light emitter units. Each of the plurality of peripheral optical detectors can form a channel pair with one of the plurality of light emitter units. The reflective optical medical sensor device can also include a controller in electrical communication with the central optical detector, the light emitter units, and the peripheral optical detectors. The controller can be configured to measure performance of channel pairs; select a particular channel pair; and measure a physiological parameter using the selected channel pair. Other embodiments are also included herein.

A method for machine learning based artifact rejection is provided. The method may include applying a machine learning model to identify artefactual independent components in transcranial magnetic stimulation electroencephalogram data collected during a transcranial magnetic stimulation procedure. Clean transcranial magnetic stimulation electroencephalogram data is generated by removing, from the transcranial magnetic stimulation electroencephalogram data, the artefactual independent components. Real-time adjustments to parameters of the transcranial magnetic stimulation procedure may be performed based on the clean transcranial magnetic stimulation electroencephalogram data. Related systems and articles of manufacture, including computer program products, are also provided.

A PPG PRV device for generating a PRV parameter of a PPG signal (20) as an estimation of a HRV parameter of an ECG signal. The PPG PRV device employs a PPG probe (700) and a PPG PRV controller (710). In operation, the PPG probe (700) generate a PPG signal (20). In response thereto, the PPG PRV controller (710) generates a normalized PPG signal (20′) including a plurality of pulses of the PPG signal (20) designated as normal pulses by the PPG PRV controller (710) and excluding at least one pulse of the PPG signal (20) designated at least one abnormal pulse by the PPG PRV controller (710), wherein the normalized PPG signal (20′) is HRV comparable to the ECG signal. The PPG PRV controller (710) derives the PRV parameter from a HRV measurement of the normalized PPG signal (20′).

Methods, systems, and computer programs encoded on a computer storage medium, for improving EEG measurements by identifying artifacts present in EEG measurements and providing a real-time indication to a user of likely artifacts in EEG measurements are described. EEG measurements of a patient can be obtained by placing a wearable device or EEG cap on a patient's head. Sensors in the cap provide EEG data to a computing device that processes the data to identify one or more artifacts in the EEG data. The artifacts can be identified by conducting one or more operations of determining the signal to noise ratio of the line noise, calculating mutual information between sensor pairs, and applying the p-welch method. Based on the types of artifacts identified, the computing device can output an indicator that provides feedback to the technician performing an EEG test to make adjustments to the test setup.

The invention provides a system and method for measuring vital signs (e.g. SYS, DIA, SpO2, heart rate, and respiratory rate) and motion (e.g. activity level, posture, degree of motion, and arm height) from a patient. The system features: (i) first and second sensors configured to independently generate time-dependent waveforms indicative of one or more contractile properties of the patient's heart; and (ii) at least three motion-detecting sensors positioned on the forearm, upper arm, and a body location other than the forearm or upper arm of the patient. Each motion-detecting sensor generates at least one time-dependent motion waveform indicative of motion of the location on the patient's body to which it is affixed. A processing component, typically worn on the patient's body and featuring a microprocessor, receives the time-dependent waveforms generated by the different sensors and processes them to determine: (i) a pulse transit time calculated using a time difference between features in two separate time-dependent waveforms, (ii) a blood pressure value calculated from the time difference, and (iii) a motion parameter calculated from at least one motion waveform.

A motion vector coding unit executes processing including a neighboring block specification step of specifying a neighboring block which is located in the neighborhood of a current block; a judgment step of judging whether or not the neighboring block has been coded using a motion vector of another block; a prediction step of deriving a predictive motion vector of the current block using a motion vector calculated from the motion vector of the other block as a motion vector of the neighboring block; and a coding step of coding the motion vector of the current block using the predictive motion vector.

A defibrillator and method for using a defibrillator which adopts an ECG analysis algorithm that can detect a cardiac arrhythmia in the presence of noise artifact induced by cardio pulmonary resuscitation (CPR) compressions. The apparatus and method offers guidance throughout a cardiac rescue protocol involving both defibrillation shocks and CPR that improves the effectiveness of the rescue, resulting in more CPR “hands-on” time, better treatment of refibrillation, and reduced transition times between CPR and electrotherapy.

A technology for a wearable medical device for monitoring medical parameters. Medical measurement data can be received at the wearable medical device from a medical measurement sensor attached to the wearable medical device or a medical measurement sensor in communication with the wearable medical device. A calibration coefficient can be determined for calibrating the wearable medical device based on the medical measurement data. The wearable medical device can be calibrated based on the calibration coefficient.