An example of a system may include a sensing circuit and an atrial fibrillation (AF) detection circuit. The sensing circuit may be configured to sense a cardiac signal indicative of atrial and ventricular depolarizations. The AF detection circuit may be configured to detect AF using the cardiac signal and may include a detector and a detection enhancer. The detector may be configured to detect the ventricular depolarizations using the cardiac signal, to measure ventricular intervals each between two successively detected ventricular depolarizations, and to detect the AF using the ventricular intervals. The detection enhancer may include a respiratory sinus arrhythmia (RSA) detector configured to detect RSA using the cardiac signal and may be configured to verify each detection of the AF based on whether the RSA is detected.

An apparatus includes a sensing circuit configured to generate a sensed physiological signal that includes physiological information of a subject, a detection circuit, and a control circuit. The detection circuit detects a physiological condition of a subject using the physiological signal. The control circuit stores sampled values of a segment of the physiological signal in temporary memory storage; and stores the sampled values in non-temporary storage in response to receiving an indication of continued detection of the physiological condition.

Methods for treating cardiac complex rhythm disorder in a patient can include receiving a plurality of electrical signals from a sensor system, wherein each electrical signal corresponds with a separate location on a cardiac wall of the heart of the patient, and wherein each electrical signal comprises an electrogram waveform; and ranking the electrical signals relative to each other based on at least a uniformity and a frequency of the electrogram waveform of each electrical signal.

This document discusses, among other things, apparatus, systems, and methods to determine a first atrial fibrillation (AF) indication using received information about a heart over a first period, to cluster depolarization information about the heart over the first period, and to discriminate between an atrial fibrillation (AF) event and a non-AF event in the first period using the determined first AF indication the clustered depolarization information.

Systems, methods, and computer program product embodiments are disclosed for performing electrophysiology (EP) signal processing. An embodiment includes an electrocardiogram (ECG) circuit board configured to process an ECG signal. The embodiment further includes a plurality of intracardiac (IC) circuit boards, each configured to process a corresponding IC signal. The embodiment further includes a communications interface communicatively coupled to a remote device, and a processor, coupled to the ECG circuit board, the plurality of IC circuit boards, and the communications interface. The processor is configured to receive, via the communications interface, feedback from the remote device. The processor is further configured to control, via the communication interface, the remote device based on the ECG signal, the IC signals, or the feedback from the remote device.

A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform an atrial fibrillation (AF) detection method (100). The method includes: generating a time-frequency representation of an electrocardiogram (ECG) signal acquired over a time interval; processing the time-frequency representation using a neural network (NN) (32) to output probabilities for rhythms of a set of rhythms including at least atrial fibrillation; assigning a rhythm for the ECG signal based on the probabilities for the rhythms of the set of rhythms output by the neural network; and controlling a display device (24) to display the rhythm assigned to the ECG signal.

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.

System and method for providing patient-specific models to distinguish between epochs of electrocardiograms (ECGs) located far away from atrial fibrillation rhythms and those located just prior to the onset of those episodes, to provide for the prediction of the onset of an occurrence of atrial fibrillation (AF) in the patient.

This document describes methods and materials for mapping and modulating repolarization. For example, this document relates to methods and devices for mapping and modulating repolarization to target atrial and ventricular arrhythmias to deliver electrical stimulation pacing, ablation and/or electroporation.

A system for processing ECG recordings from multiple patients includes a preprocessing database containing unprocessed ECG records from multiple patients, a reporting database containing processed ECG records from multiple patients, a processor, and a triage module executable on the processor to assess each of the unprocessed ECG recordings from the multiple patients. The triage module is executable to detect a presence or absence of one or more known abnormalities and determines at least one abnormality identifier based on the detected known abnormality. One or more abnormality groups are then identified based on the abnormality identifiers. A normal group of ECG recordings from multiple patients is then identified from those ECG recordings that are not in the abnormality group. The normal group of ECG recordings is then stored in the reporting database then associated a normal identifier.