Sensing circuitry of an implantable medical device (IMD) system may sense a cardiac signal that varies according to a cardiac cycle of a patient. Processing circuitry of the IMD system may determine a series of consecutive cardiac cycle length metric values based on the sensed cardiac signal, identify a plurality of pairs of the cardiac cycle length metrics, each of the pairs of cardiac cycle length metrics separated by an integer n of the cardiac cycle length metrics, and construct a distribution of the pairs of cardiac cycle length metrics based on values of the cardiac cycle length metrics for each of the pairs. The processing circuitry may detect a sleep apnea episode of the patient based on one or more characteristics of the constructed distribution, and control communication circuitry of the IMD system to transmit an indication of the detected sleep apnea episode to the external computing device.

An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine a representative signal based on the cardiac signal, the representative signal having a single polarity, and determine an end of a T-wave of the cardiac signal based on an area under the representative signal.

Methods, systems and devices for providing cardiac resynchronization therapy (CRT) to a patient using a leadless cardiac pacemaker (LCP) and an extracardiac device (ED). The LCP is configured to deliver pacing therapy at a pacing interval. Illustratively, the ED may be configured to analyze the cardiac cycle including a portion preceding the pacing therapy delivery for one or several cardiac cycles, and determine whether an interval from the P-wave to the pace therapy in the cardiac cycle(s) is in a desired range. In an example, if the P-wave to pace interval is outside the desired range, the ED communicates to the LCP to adjust the pacing interval.

A medical monitoring device for monitoring electrical signals from the body of a subject is described. The medical monitoring device monitors electrical signals originating from a cardiac cycle of the subject and associates each cardiac cycle with a time index. The medical monitoring device applies a forward computational procedure to generate a risk score indicative of hyperkalemia, hypokalemia or arrhythmia of the subject. The medical monitoring device can adjust the forward computational procedure based upon clinical data obtained from the subject.

Methods and systems are provided for determination and mapping of vector fields which characterize wavefront motion through space and time. The inventive methods and systems utilize data from spatially-distributed locations and maps wavefront vector flow fields in an entirely automated manner. These maps can be used to characterize the activation as planar, centrifugal, or rotational. Further, the strength of rotation or divergence is determined from these fields and can be used to select spatial points of significantly increased rotational or focal activity. As applied to electrophysiological data recorded during heart rhythm disorders in patients, the inventive method provides a means of visual interpretation of complex activation maps. The information related to the strength and location of rotation and centrifugal activity during episodes of arrhythmia can guide therapies designed to treat such disorders.

A handheld device measures all vital signs and some hemodynamic parameters from the human body and transmits measured information wirelessly to a web-based system, where the information can be analyzed by a clinician to help diagnose a patient. The system utilizes our discovery that bio-impedance signals used to determine vital signs and hemodynamic parameters can be measured over a conduction pathway extending from the patient's wrist to a location on their thoracic cavity, e.g. their chest or navel. The device's form factor can include re-usable electrode materials to reduce costs. Measurements made by the handheld device, which use the belly button as a fiducial marker, facilitate consistent, daily measurements, thereby reducing positioning errors that reduce accuracy of standard impedance measurements. In this and other ways, the handheld device provides an effective tool for characterizing patients with chronic diseases, such as heart failure, renal disease, and hypertension.

Techniques are disclosed for monitoring a patient for the occurrence of cardiac arrhythmias. A computing system obtains a cardiac electrogram (EGM) strip for a current patient. Additionally, the computing system may apply a first cardiac rhythm classifier (CRC) with a segment of the cardiac EGM strip as input. The first CRC is trained on training cardiac EGM strips from a first population. The first CRC generates first data regarding an aspect of a cardiac rhythm of the current patient. The computing system may also apply a second CRC with the segment of the cardiac EGM strip as input. The second CRC is trained on training cardiac EGM strips from a smaller, second population. The second CRC generates second data regarding the aspect of the cardiac rhythm of the current patient. The computing system may generate output data based on the first and/or second data.

Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrhythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrhythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrhythmia.

Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the episode.