Techniques are disclosed for a multi-tier system for predicting cardiac arrhythmia in a patient. In one example, a computing device processes parametric patient data and provider data for a patient to generate a long-term probability that a cardiac arrhythmia will occur in the patient within a first time period. In response to determining that the cardiac arrhythmia is likely to occur within the first time period, the computing device causes a medical device to process the parametric patient data to generate a short-term probability that the cardiac arrhythmia will occur in the patient within a second time period. In response to determining that the cardiac arrhythmia is likely to occur within the second time period, the medical device performs a remediative action to reduce the likelihood that the cardiac arrhythmia will occur.

An apparatus for monitoring a sleep parameter of a user includes an adhesive pad configured to conform to a surface of the user and a flexible element coupled to the adhesive pad. The flexible element includes a conductive fabric, and exhibits a modified electrical property in response to an applied force. The apparatus also includes a power source electrically coupled to the flexible element, and an electrical circuit electrically coupled to the power source and the flexible conductive element. The electrical circuit is configured to detect, during use, a change in an electrical property of the flexible element.

Systems and methods for monitoring patients with respiratory diseases are described. A system may include a sensor circuit to sense a respiration signal and at least one hemodynamic signal. The system may detect a specified respiratory phase from the respiration signal, and generate from the hemodynamic signal one or more signal metrics that are correlative to at least one of a systolic blood pressure, a blood volume, or a cardiac dimension. The system may detect a restrictive or obstructive respiratory condition when the hemodynamic signal metric indicates hemodynamic deterioration during a specified respiratory phase. The system may additionally classify the detected restrictive or obstructive respiratory condition into one of two or more categories, and deliver a therapy based on the detection or the classification.

In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

Methods, techniques, apparatuses, and systems for setting up and tracking sleep consistency goals of users are provided. In one example, a computing system for setting a sleep schedule of a user of a biometric monitoring device may obtain sleep data derived from sensor data generated by the biometric monitoring device, store the sleep data in a sleep log data store as one or more sleep logs associated with an account assigned to the user, and calculate a target bedtime based on a scheduled waketime of the user and a sleep efficiency derived, at least in part, from the sleep data for one or more users stored in the sleep log data store. The computing system may also be configured to provide a number of personalized user interfaces to an individual for the purposes of setting a sleep schedule. Such interfaces may include parameters that are tailored to the individual sleep needs and/or characteristics of the individual's sleep.

Methods and apparatus infer or indicate sleep stage(s) of a patient from a respiratory flow rate signal of the patient. The method may include applying a plurality of detection pathways to a signal representing a respiratory flow rate of the patient, wherein each detection pathway is configured to generate start events and end events indicating start times and end times of episodes respectively of a corresponding sleep stage, wherein each start event and each end event has a priority; and combining the start events and end events based on their priorities to produce an indication of the sleep stage of the patient. The apparatus may include a sensor configured to generate a signal representing a property of a flow of air within a patient interface; and a processor configured to implement a method of inferring a sleep stage of the patient from the signal.

A system for remote transdermal alcohol monitoring includes and/or interfaces with a transdermal alcohol sensing device. Additionally or alternatively, the system can include and/or interface with any or all of: a user device; a supplementary alcohol sensing device; a set of supplementary sensors; a computing subsystem; a user interface; and/or any other components. A method for remote transdermal alcohol monitoring includes: receiving a set of inputs; determining a set of outputs; and triggering an action based on the set of outputs.

The present disclosure generally relates to systems and methods for monitoring and/or the sleep stage of an individual using one or more sensors, and methods of treating medical conditions related thereto (e.g., obstructive sleep apnea).

Techniques for configuring one or more applications based on a detected wakefulness state of a user are disclosed. A system trains and applies a machine learning model to wakefulness data to compute a wakefulness state of a user. The system obtains the wakefulness data from wearable devices worn by the user and environmental devices in a user's environment. The system configures applications and/or devices based on the computed wakefulness state of the user. The system configures the ability of devices or applications to generate visual, audible, or tactile notifications in response to determining that a user is awake or asleep.

An apparatus and method for evaluating obstructive sleep apnea with PPG signal are provided. Measured heartbeat interval signal is used to evaluate whether there is obstructive apnea. The system includes a motion sensor for detecting whether a user is in a stationary state; an optical sensor for measuring the heartbeat interval signal of the user in a stationary state; a microprocessor for processing the heartbeat interval signal of the user to obtain an apnea parameter; an alert module for receiving the apnea parameter and feeding them back to the user; and a memory module for storing the apnea parameter after the signal processing. The system and method are used to determine whether the heartbeat interval signal is an obstructive sleep apnea signal, and further determine whether the user is in an obstructive sleep apnea situation.