A system and method for display of subcutaneous cardiac monitoring data are provided. Cutaneous action potentials of a patient and other sensed data associated with the patient are recorded as electrocardiogram (EGC) data over a set time period using a subcutaneous insertable cardiac monitor. A set of R-wave peaks is identified within the ECG data and an R-R interval plot is constructed. A difference between recording times of successive pairs of the R-wave peaks in the set is determined. A heart rate associated with each difference is also determined. The pairs of the R-wave peaks and associated heart rate are plotted as the R-R interval plot. A diagnosis of cardiac disorder is facilitated based on patterns of the plotted pairs of the R-wave peaks, the associated heart rates in the R-R interval plot, and background data based on the other sensed data.

A bio-signal quality assessment apparatus may include: a bio-signal obtainer configured to obtain a bio-signal; and a processor configured to extract periodic signals from the obtained bio-signal, and determine a signal quality index based on at least one of similarity between the extracted periodic signals and signal variability of the obtained bio-signal.

Techniques are disclosed for using a cardiac signal sensed via a plurality of electrodes disposed on one or more leads implanted within an epidural space of a patient to control spinal cord stimulation (SCS) therapy. In one example, an implantable medical device (IMD) senses an electrical signal via a plurality of electrodes disposed on one or more leads implanted within an epidural space of a patient. Processing circuitry determines, from the electrical signal, one or more cardiac features indicative of activity of a heart of the patient. The processing circuitry controls, based on the one or more cardiac features, delivery of SCS therapy to the patient.

A method of determining a fused sensor measurement is disclosed including: obtaining sensor measurements from sensors detecting a same type of physiological measurement; determining a signal quality index (SQI) of each sensor including determining an extent to which a sensor measurement differs from others among the sensor measurements obtained from each sensor; determining a weightage of each sensor based on the SQI of each sensor; and determining a fused sensor measurement from the plurality of sensors based on the weightage of each sensor and filtered sensor measurements of each sensor obtained from a Kalman filter operation. A vehicle safety system includes: a vehicle electronic control unit configured to: determine the sensor measurement extent, to determine the SQI of each sensor, determine the weightage of each sensor, determine the fused sensor measurement, determine the occupant's physiological condition, and if the physiological condition is abnormal, perform at least one vehicle operation.

Disclosed are a method and apparatus for sleep monitoring, an electronic device and a computer readable medium, relating to the field of health monitoring technology, the method comprises: a screen state of a target terminal is obtained; physiological parameters of a user are obtained, the physiological parameters including at least one of body motion parameters and heart rate parameters; a sleep state of the user is determined based on the screen state and the physiological parameters. Since the screen state can reflect the user's operation of the user terminal, and the operation can further reflect the sleep state of the user, and the physiological parameters can also reflect the sleep state of the user, the combination of the screen state and the physiological parameters can make the determination of sleep state more accurate.

A new system and method is provided for improving the accuracy of pulse rate detection. Various aspects contribute to the greater accuracy, including but not limited to pre-processing of the camera output/input, extracting the pulsatile signal from the preprocessed camera signals, followed by post-filtering of the pulsatile signal. This improved information may then be used for such analysis as HRV determination, which is not possible with inaccurate methods for optical pulse rate detection.

Methods and systems are provided for predicting cardiac arrhythmias based on multi-modal patient monitoring data via deep learning. In an example, a method may include predicting an imminent onset of a cardiac arrhythmia in a patient, before the cardiac arrhythmia occurs, by analyzing patient monitoring data via a multi-arm deep learning model, outputting an arrhythmia event in response to the prediction, and outputting a report indicating features of the patient monitoring data contributing to the prediction. In this way, the multi-arm deep learning model may predict cardiac arrhythmias before their onset.

Methods, systems and devices are provided for motion-activated display of messages on an activity monitoring device. In one embodiment, method for presenting a message on an activity monitoring device is provided, including the following method operations: downloading a plurality of messages to the device; detecting a stationary state of the device; detecting a movement of the device from the stationary state; in response to detecting the movement from the stationary state, selecting one of a plurality of messages, and displaying the selected message on the device.

A method of predicting a healthcare event includes: receiving via an input device, classifying personal information for each of a plurality of persons; collecting measurements of at least one health indicator during a predefined learning period; creating a personal physiological pattern profile, based on the collected data; associating each of the plurality of persons to a physiological cluster based on each person's personal physiological pattern profile and based on the classifying personal information of each of the plurality of persons; creating, for each physiological cluster, a health indicator deviation pattern for the healthcare event; continuously monitoring values of the health indicator of the person; and determining an occurrence probability of the healthcare event when the monitored indicators deviate from the personal physiological pattern profile. A system for predicting a healthcare event is also disclosed.

An illustrative hearing system may be configured to receive, from an inertial sensor included in a hearing device configured to be worn by a user, inertial sensor data representative of at least one of motion of the hearing device or orientation of the hearing device. The hearing system may further be configured to determine, based on the inertial sensor data, an activity state of the user and to determine, based on the activity state, a cardiorespiratory quality index representative of a quality level of a measurement of a cardiorespiratory property of the user.