A system includes a memory storing a user profile for a user of the system and machine-readable instructions and a control system including one or more processors configured to execute the machine-readable instructions to receive physiological data associated with the user during a sleep session, determine, based at least in part on the received physiological data, a set of sleep-related parameters for the sleep session, subsequent to the sleep session, select one of the set of sleep-related parameters as a targeted parameter, the selection of the targeted parameter being based at least in part on the stored user profile, the set of sleep-related parameters, or both, and cause information to be communicated to the user via a user device, the information being indicative of the targeted parameter, a recommendation associated with improving the targeted parameter for the user in one or more subsequent sleep sessions, or both.

Patient sleep is staged using personalized circadian models built with data collected by wearable devices over daytime and nighttime hours, thus capturing a patient's personal circadian rhythms. The circadian model is used to identify sleep intervals in incoming nightly data for the patient. The identified sleep intervals are analyzed by the machine learning system which stages epochs of sleep. Methods include receiving patient heart rate data from over a plurality of circadian cycles; creating a circadian model for the patient with a defined operation for applying sleep labels to new data from the wearable device; applying the circadian model to nightly test data from the device to identify a sleep interval; and assigning, with a classifier, sleep stages to epochs of the sleep interval.

A medical device is configured to detect an atrial tachyarrhythmia episode. The device senses a cardiac signal, identifies R-waves in the cardiac signal attendant ventricular depolarizations and determines classification factors from the R-waves identified over a predetermined time period. The device classifies the predetermined time period as one of unclassified, atrial tachyarrhythmia and non-atrial tachyarrhythmia by comparing the determined classification factors to classification criteria. A classification criterion is adjusted from a first classification criterion to a second classification criterion after at least one time period being classified as atrial tachyarrhythmia. An atrial tachyarrhythmia episode is detected by the device in response to at least one subsequent time period being classified as atrial tachyarrhythmia based on the adjusted classification criterion.

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′).

A method for uterine activity monitoring may include: acquiring a plurality of signals from a plurality of sensors during uterine activity; processing the plurality of signals to extract a plurality of uterine electrical activity characteristics; analyzing the plurality of uterine electrical activity characteristics; and classifying the uterine activity as one of: a preterm labor contraction, a labor contraction, a Braxton-Hicks contraction, and a state of no contraction. A method of assessing over time a pre-term birth risk of a pregnant female may include: calculating a baseline pre-term birth risk score based on a user input; acquiring, over time, a signal from a sensor; analyzing the signal to extract a parameter of interest, such that the parameter of interest comprises a physiological parameter; and calculating an instant pre-term birth risk score based, at least in part, on the parameter of interest and the user input.

There is provided a learning device, including a learning unit that learns output related to a target feature point to be observed in a repetition section observed periodically, with the use of the first sensor data being acquired by the first system and having a time length corresponding to the repetition section, as learning data, and of teacher data based on the second sensor data acquired by the second system at a time point when a specific period of time has elapsed since a start time point of the time length related to the first sensor data, the second system being less affected by noises than the first system, in which the specific period of time is set on the basis of a time length from a start time point of the repetition section to a time point at which the target feature point is expected to appear.

A smart ring includes a battery, memory, processing circuitry, a plurality of sensors, a plurality of antennas, and a battery, each coupled to one another and all enclosed in a casing, wherein the processing circuitry is configured to conserve the battery by any of sending data to the cloud service when an application is open on the user device, sending data to the cloud service when a threshold is crossed, waking up processing or communicating when there is a change in motion detected by the accelerometer.

A system monitors an individual for conditions indicating a possibility of occurrence of irregular heart events. A database includes a plurality of combinations of at least a first signature and a second signature. A first portion of the plurality of combinations is associated with a normal heartbeat and a second portion of the plurality of combinations is associated with an irregular heart event. A wearable heart monitor that is worn on a body of the patient includes a heart sensor for generating a heart signal responsive to monitoring a beating of a heart of the individual. The monitor further includes a processor for receiving the heart signal from the heart sensor. The processor is configured to analyze the heart signal using a plurality of different processes. Each of the plurality of different processes generates at least one of the first signature and the second signature. The plurality of different processes provide a unique combination including at least the first signature and the second signature for the generated heart signal. The processor compares the unique combination with the plurality of combinations in the database, locates a combination of the plurality of combinations that substantially matches the unique combination and generates a first indication if the unique combination substantially matches one of the first portion of the plurality of combinations and a second indication if the unique combination substantially matches one of the second portion of the plurality of combinations.

An implantable vagus nerve stimulation (VNS) system includes a sensor configured to measure ECG data for a patient, a stimulation subsystem configured to deliver VNS to the patient, and a control system configured to perform a heart rate variability analysis with the ECG data. In some aspects, performing the heart rate variability analysis includes measuring R-R intervals between successive R-waves for the ECG data measured during a stimulation period and a baseline period, plotting each R-R interval against an immediately preceding R-R interval for each of the stimulation period and the baseline period, and determining at least one of a standard deviation from an axis of a line perpendicular to an identity line for each of the stimulation period plot and the baseline period plot or a centroid of each of the stimulation period plot and the baseline period plot.

Computer implemented biometric methods and systems incorporate sensing biophysical phenomena, translating the phenomena into digital data and transmitting the data to a series of servers operating in an open feedback loop to generate a module. A biometric networking system can include a biometric monitoring cloud computing platform with AI/machine learning augmented models are generated to make user assessments, programs and confidence scores to the healthcare provider systems. The AI/machine learning models can be used by the biometric monitoring network to generate health-related AI processes that analyze relationships treatment techniques and outcomes. AI techniques can be used to calculate movement modeling and confidence scoring including support vector machines, neural networks, and decision trees. The biophysical phenomena may include biometric parameters based on data, such as medical history, exertion, sleep, temperature, cardiovascular events, respiratory events, and muscle and blood pH.