An ambulatory medical device configured to analyze heart rates in different operating modes includes a plurality of ECG sensing electrodes, a plurality of therapy electrodes and at least one processor configured to in a default operating mode, perform a default heart rate calculation for determining a heart rate of the patient for use in detecting a cardiac arrhythmia condition of the patient. The at least one processor is configured to change a device operating mode from a default mode based on detecting patient activity to an activity operating mode, and in the activity operating mode, perform a different heart rate calculation from the default heart rate calculation for determining the heart rate for use in detecting the cardiac arrhythmia condition of the patient during the activity operating mode. The at least one processor is configured to deliver the treatment in response to detecting the cardiac arrhythmia condition.

A method of automatically determining which type of treatment is most appropriate for a cardiac arrest victim, the method comprising transforming one or more time domain electrocardiogram (ECG) signals into a frequency domain representation comprising a plurality of discrete frequency bands, combining the discrete frequency bands into a plurality of analysis bands, wherein there are fewer analysis bands than discrete frequency bands, determining the content of the analysis bands, and determining the type of treatment based on the content of the analysis bands.

Apparatus and methods remove a voltage offset from an electrical signal, specifically a biomedical signal. A signal is received at a first operational amplifier and is amplified by a gain. An amplitude of the signal is monitored, by a first pair of diode stages coupled to an output of the first operational amplifier, for the voltage offset. The amplitude of the signal is then attenuated by the first pair of diode stages and a plurality of timing banks. The attenuating includes limiting charging, by the first pair of diode stages, of the plurality of timing banks and setting a time constant based on the charging. The attenuating removes the voltage offset persisting at a threshold for a duration of at least the time constant. Saturation of the signal is limited to a saturation recovery time while the saturated signal is gradually pulled into monitoring range over the saturation recovery time.

The disclosed methods pertain to diagnosing whether a non-ablative, gene therapy is needed for reducing AF fibrosis in a subject, and if so, methods of reducing AF fibrosis in a subject using gene therapy with a dominant negative TGF- R2 cDNA expression vector. Kits and computer program products are also described, wherein the kits provide materials for diagnosing and treating AF fibrosis, and the computer program products include a computer readable medium having computer readable program code for monitoring the efficacy of therapeutic ablation of fibrosis in a subject using a gene therapy method.

Systems and methods for detecting cardiac arrhythmia are discussed. An exemplary arrhythmia detection system can receive physiologic information of the patient, measure a first signal metric using a first portion of the received physiologic information, and determine an arrhythmia detection duration using a comparison between the measured first signal metric and a reference signal metric value. The system includes an arrhythmia detector to detect an AT episode using a second portion of the physiologic information corresponding to the determined arrhythmia detection duration.

A system for identifying arrhythmias based on cardiac waveforms includes a storage system storing a trained deep neural network system, wherein the trained deep neural system includes a trained representation neural network and a trained classifier neural network. A processing system is communicatively connected to the storage system and configured to receive cardiac waveform data for a patient, identify a time segment in the cardiac waveform data, and transform the time segment into a spectrum image. The processing system is further configured to generate, with the representation neural network, a latent representation from the spectrum image, and then to generate, with the classifier neural network, an arrhythmia classifier from the latent representation.

An implantable device for analyzing a high frequency (HF) electrogram signal received from subcutaneous, above-rib pickup locations, the device including an implantable electrode for use inside a living body, and a can for subcutaneous implantation, the can including a signal pickup configured to pick up an electrogram signal including a high frequency (HF) component, a signal filter connected to the signal pickup and configured to measure a high frequency (HF) component from the electrogram signal, and an analyzer for analyzing the HF component of the electrogram signal, wherein the analyzer is configured to analyze at least one time-varying parameter of the HF component of the electrogram signal, and the signal filter is configured to measure the electrogram signal by using a signal picked up from at least two pickup locations which are both subcutaneous and above-rib. Related apparatus and methods are also described.

The present disclosure relates to a wearable monitor device and methods and systems for using such a device. In certain embodiments, the wearable monitor records cardiac data from a mammal and extracts particular features of interest. These features are then transmitted and used to provide health-related information about the mammal.

Detecting dislodgement of a ventricular lead coupled to an implantable medical device comprises sensing a near-field cardiac EGM via a first electrode of the ventricular lead and a far-field cardiac EGM via a second electrode of the ventricular lead, identifying, R-waves in the near-field cardiac EGM and the far-field cardiac EGM. determining a near-field value of one or more R-wave amplitude metrics based on amplitudes of R-waves identified in the near-field cardiac EGM and a far-field value of the one or more R-wave amplitude metrics based on amplitudes of R-waves identified in the far-field cardiac EGM, detecting dislodgement of the ventricular lead based on at least one of the near-field value or the far-field value of the one or more R-wave amplitude metrics; and providing a lead dislodgment alert in response to detecting the dislodgement of the ventricular lead.

Disclosed herein are methods of treating cardiac arrhythmia with electronic monitoring in a timely manner. Also disclosed herein are systems for electronic monitoring of cardiac arrhythmia.