Wearable devices are provided herein including wearable defibrillators, wearable devices for diagnosing symptoms associated with sleep apnea, and wearable devices for diagnosing symptoms associated with heart failure. The wearable external defibrillators can include a plurality of ECG sensing electrodes and a first defibrillator electrode pad and a second defibrillator electrode pad. The ECG sensing electrodes and the defibrillator electrode pads are configured for long term wear. Methods are also provided for using the wearable external defibrillators to analyze cardiac signals of the wearer and to provide an electrical shock if a treatable arrhythmia is detected. Methods are also disclosed for refurbishing wearable defibrillators. Methods of using wearable devices for diagnosing symptoms associated with sleep apnea and for diagnosing symptoms associated with heart failure are also provided.

Wearable devices are provided herein including wearable defibrillators, wearable devices for diagnosing symptoms associated with sleep apnea, and wearable devices for diagnosing symptoms associated with heart failure. The wearable external defibrillators can include a plurality of ECG sensing electrodes and a first defibrillator electrode pad and a second defibrillator electrode pad. The ECG sensing electrodes and the defibrillator electrode pads are configured for long term wear. Methods are also provided for using the wearable external defibrillators to analyze cardiac signals of the wearer and to provide an electrical shock if a treatable arrhythmia is detected. Methods are also disclosed for refurbishing wearable defibrillators. Methods of using wearable devices for diagnosing symptoms associated with sleep apnea and for diagnosing symptoms associated with heart failure are also provided.

An animated electrophysiology map is generated from a plurality of data points, each including measured electrophysiology information, location information, and timing information. The electrophysiology and location information can be used to generate the electrophysiology map, such as a local activation time, peak-to-peak voltage, or fractionation map. Animated timing markers can be superimposed upon the electrophysiology map using the electrophysiology, location, and timing information. For example a series of frames can be displayed sequentially, each including a static image of the electrophysiology map at a point in time and timing markers corresponding to the state or position of an activation wavefront at the point in time superimposed thereon. The visibility or opacity of the timing markers can be adjusted from frame to frame, dependent upon a distance between the timing marker and the activation wavefront, to give the illusion that the timing markers are moving along the electrophysiology map.

The present disclosure relates to a method for measuring multidimensional dynamics of cardiac activity comprising simultaneous measurement of three dimensional ECG, three dimensional impedance rheometry, tissue motion, wherein the tissue motion measurement is performed with a sensor placed outside the patient body selected from among sensors including: membrane, auscultation funnel, accelerometer, microphone, piezoelectric sensor, wherein the impedance rheometry measurement includes simultaneous generation of applied current of different frequencies for three orthogonal axes and an impedance measurement for the same or other three orthogonal axes, and wherein the impedance rheometry measurement and the ECG measurement are carried out for the same three orthogonal axes.

The present disclosure further relates to a device for implementation of the method.

Systems, methods, and devices are described herein for evaluation, adjustment, and delivery of adaptive cardiac therapy. The systems, methods, and devices may utilize electrical heterogeneity information to determine and/or select one or more pacing settings and pacing type or configurations for a plurality of different heart rates. The adaptive cardiac therapy may deliver cardiac therapy at selected pacing settings such as, for example, A-V and/or V-V intervals, according to a presently measured heart rate and switch between left ventricular-only or biventricular cardiac pacing therapy also according to the presently measured heart rate.

Systems and methods are described for implementing a catheter model to estimate shape of a deformable catheter in a three-dimensional space. The catheter model includes two or more model segments that correspond to two or more segments of the deformable catheter. Each model segment includes a length and location of model electrode(s) and/or model magnetic sensor(s) corresponding electrodes and/or magnetic sensors of the deformable catheter. Variable shape parameter define a curvature of the segment. Varying the shape parameters generates a plurality of potential catheter shapes. In conjunction with generating the potential catheter shapes, impedance and/or magnetic responses (e.g., measured responses) are obtained for the physical electrodes and/or physical magnetic sensors of the deformable catheter. Using a selected one (e.g., most likely) of the potential catheter shapes and the measured responses, the shape parameters are updated and a catheter shape is generated and displayed.

A method and medical device for detecting a cardiac event that includes sensing cardiac signals from a plurality of electrodes forming a first sensing vector sensing a first interval of the cardiac signal during a predetermined time period and a second sensing vector simultaneously sensing a second interval of the cardiac signal during the predetermined time period, identifying each of the first interval and the second interval as being one of shockable and not shockable in response to first processing of the first interval and the second interval and in response to second processing of one or both of the first interval and the second interval, the second processing being different from the first processing, and determining whether to deliver therapy for the cardiac event in response to identifying each of the first interval and the second interval as being one of shockable and not shockable in response to both the first processing and the second processing of the first interval and the second interval.

A method and medical device for detecting a cardiac event that includes sensing cardiac signals from a plurality of electrodes forming a first sensing vector sensing a first interval of the cardiac signal during a predetermined time period and a second sensing vector simultaneously sensing a second interval of the cardiac signal during the predetermined time period, identifying each of the first interval and the second interval as being one of shockable and not shockable in response to first processing of the first interval and the second interval and in response to second processing of one or both of the first interval and the second interval, the second processing being different from the first processing, and determining whether to deliver therapy for the cardiac event in response to identifying each of the first interval and the second interval as being one of shockable and not shockable in response to both the first processing and the second processing of the first interval and the second interval.

Systems and methods for determining an arrhythmia type are provided. A method may include: obtaining an electrocardiogram signal, obtaining a feature vector of the electrocardiogram signal, obtaining a trained prediction model, and determining an arrhythmia type of the electrocardiogram signal based on the trained prediction model and the feature vector. The feature vector includes a threshold crossing sample (TCSC) percent feature, an L-Z complexity feature, an empirical mode decomposition (EMD) complex number feature, a sample entropy feature, and a set of wavelet transform energy features.

Systems and methods for determining an arrhythmia type are provided. A method may include: obtaining an electrocardiogram signal, obtaining a feature vector of the electrocardiogram signal, obtaining a trained prediction model, and determining an arrhythmia type of the electrocardiogram signal based on the trained prediction model and the feature vector. The feature vector includes a threshold crossing sample (TCSC) percent feature, an L-Z complexity feature, an empirical mode decomposition (EMD) complex number feature, a sample entropy feature, and a set of wavelet transform energy features.