An apparatus includes a sensing circuit configured to generate a sensed physiological signal representative of cardiac activity of a subject, an arrhythmia detection circuit, a control circuit, and a memory. The arrhythmia detection circuit detects an episode of atrial fibrillation (AF) in the sensed cardiac signal using a first AF detection criterion, and detects the episode of AF using a second AF detection criterion. The first AF detection criterion has greater sensitivity to AF detection than the second AF detection criterion, and the second AF detection criterion has greater specificity to AF detection than the first AF detection criterion. The control circuit initiates storing of sampled values of a segment of the cardiac signal that includes the episode of AF when the episode of AF is detected by both the first AF detection criterion and the second AF detection criterion.

Example electronic devices, including but not limited to implantable medical devices, and methods employing dynamic announcing for creation of wireless communication connections are disclosed herein. In an example, an electronic device includes a wireless communication interface to transmit announcement signals for creating a wireless communication connection with the external device. The electronic device also includes a sensor to detect a characteristic of an environment external to the electronic device, and a control circuit including an announcement timing control module to dynamically control timing of the announcement signals based on the detected characteristic.

Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator (other-factors) to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

A method of mapping arrhythmic activity, such as premature ventricular contraction (PVC) activity, using an electroanatomical mapping system includes defining at least two arrhythmia template signals. Electrophysiology data points, each including an electrophysiological signal, are collected. A morphological similarity between the electrophysiological signal and a first arrhythmia template signal is computed; if this exceeds a preset threshold, then the electrophysiology data point is added to a corresponding arrhythmia map. If it does not, a morphological similarity between the electrophysiological signal and a second arrhythmia template signal is computed. If this exceeds the preset threshold, then the electrophysiology data point is added to a corresponding arrhythmia map. If neither exceeds the preset threshold, then the electrophysiology data point can be used to establish an additional arrhythmia map by defining an additional arrhythmia template signal.

A patient-worn arrhythmia monitoring and treatment device weight between 250 grams and 2,500 grams includes at least one contoured pad configured to be adhesively coupled to a patient's torso, a plurality of therapy electrodes, at least one of which is integrated with the at least one contoured pad, and a plurality of ECG sensing electrodes, at least one of which is integrated with the at least one contoured pad. At least one housing configured to form a watertight seal with the at least one contoured pad extends no more than 5 cm from the contoured pad. A processor disposed within the housing is coupled to a therapy delivery circuit and configured to detect one or more treatable arrhythmias based on at least one ECG signal and cause a therapy delivery circuit to deliver at least one defibrillation pulse on detecting the one or more treatable arrhythmias.

Techniques and devices for implementing the techniques for adjusting atrial arrhythmia detection based on analysis of one or more P-wave sensing windows associated with one or more R-waves. An implantable medical device may determine signal characteristics of the cardiac signal within the P-wave sensing window, determine whether the cardiac signal within the sensing window corresponds to a P-wave based on the determined signal characteristics, determine a signal to noise ratio of the cardiac signal within the sensing window, update the arrhythmia score when the P-wave is identified in the sensing window and the determined signal to noise ratio satisfies a signal to noise threshold.

Described herein are user-wearable devices, and methods for use therewith, for monitoring for one or more types of arrhythmias based on a photoplethysmography (PPG) signal obtained using an optical sensor of a user-wearable device. A PPG based statistical and/or machine learning model is used to analyze a PPG signal, obtained using the optical sensor, to monitor for one or more types of arrhythmias including atrial fibrillation (AF). In response to detecting an arrhythmia based on the PPG signal, an electrocardiogram (ECG) signal is obtained using an ECG sensor of the user-wearable device. An ECG based statistical and/or machine learning model is used to analyze the ECG signal obtained using the ECG sensor of the user-wearable device to confirm or reject the arrhythmia detected based on the PPG signal and/or to perform arrhythmia discrimination. Obtained PPG and/or ECG signal segments can be provided to the model(s) to update the model(s).

We disclose herein a method of detecting abnormalities in electrocardiogram (ECG) signals, the method comprising receiving a set of ECG signals from an ECG device; amplifying only the peaks of at least some of the set of ECG signals to produce ECG beat markings from which a heart rate is derivable to detect an irregular rhythm between at least two ECG beats; extracting a single ECG beat from the set of ECG signals from the ECG device by using said ECG beat markings; feeding the extracted single ECG beat into a first neural network; producing, at the first neural network, a compact representation of the extracted single ECG signal so as to generate a feature extraction output; and using, at a second neural network, the feature extraction output from the first neural network to generate a score associated with the abnormalities in the ECG signals.

A system for reporting cardiologic data includes a patient-portable monitoring device and circuitry. The monitoring device is configured to detect electrocardiogram (ECG) data and patient-initiated event data. The circuitry is configured to receive the ECG data and the patient-initiated event data; detect atrial fibrillation (AF) events in the ECG data; calculate the duration of each AF event by subtracting the respective start time from the respective stop time of each AF event; compare the duration of each AF event to a first duration threshold; store each AF event having a duration exceeding the first duration threshold; calculate a monitoring time period duration by subtracting the monitoring start time from the monitoring stop time; calculate, based on the stored AF events, AF burden; and output a graphical presentation of the patient-initiated event data, AF burden, and stored AF events. The first duration threshold is less than 30 seconds.

Methods and systems methods for continuously monitoring a patient for cardiac electrical abnormalities including atrial fibrillation, asystole, ventricular fibrillation and tachycardia.