A system and method for classifier-based atrial fibrillation detection with the aid of a digital computer are provided. Electrocardiography (ECG) features and annotated patterns of the features are maintained in a database, at least some of the patterns associated with atrial fibrillation. A classifier is trained based on the annotated patterns. A representation of an ECG signal recorded by one or more ambulatory monitors is received. ECG features in the representation falling within each of the temporal windows are detected. The trained classifier is used to identify patterns of the ECG features. At least one matrix with weights for the patterns are generated. A value indicative of whether portions of the representation are associated the patient experiencing atrial fibrillation is calculated. That one or more of the portions are associated with the patient experiencing atrial fibrillation is determined. An action is taken based on one or more of the determinations.

A method including calculating, at multiple intra-cardiac locations, respective average atrial fibrillation cycle-length (AFCL) values. A determination is made as to whether the calculated average AFCL values are indicative of a regular atrial fibrillation (AF) activity. Gradients between pairs of the average AFCL values are calculated for a plurality of average AFCL values that are determined to be indicative of regular AF activity. The calculated AFCL gradients are presented to a user, overlaid on a map of at least a portion of the heart.

A defibrillator (AED) and method for using a defibrillator using two different ECG analysis algorithms which work sequentially to improve the accuracy of AED shock decisions. A first algorithm, such as (ART), is particularly suited for analysis in the presence of CPR periods. A second algorithm, such as (PAS), is particularly suited for analysis during hands-off periods. The AED switches algorithms depending on the period and on the current analysis of the cardiac rhythm. The inventions thus provide an optimized ECG analysis scheme in a manner that improves the effectiveness of the rescue, resulting in more CPR hands-on time, better treatment of refibrillation, and reduced transition times between CPR and electrotherapy.

A method of detecting atrial fibrillation includes detecting a pulse signal to obtain a time pulse waveform and converting it to an energy spectrum waveform via Fast Fourier Transform. The energy spectrum waveform includes a first frequency region, a second frequency region, and a third frequency region. The number of spikes in each frequency region was calculated and the heart indexes of the first, second, and third frequency regions were obtained, which were the first heart index, the second heart index, and the third heart index. And by the sum of the three heart index values and the first heart index to determine the possibility of atrial fibrillation. An apparatus for detecting atrial fibrillation is also provided, whereby the user can determine the possibility and predicting atrial fibrillation by simple measurement of blood pressure at home.

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 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.

Cardiac activity (e.g., a cardiac electrogram) is analyzed for local abnormal ventricular activity (LAVA), such as by using a LAVA detection and analysis module incorporated into an electroanatomical mapping system. The module transforms the electrogram signal into the wavelet domain to compute as scalogram; computes a one-dimensional LAVA function of the scalogram; detects one or more peaks in the LAVA function; and computes a peak-to-peak amplitude of the electrogram signal. If the peak-to-peak amplitude does not exceed a preset amplitude threshold, then the module can compute one or more of a LAVA lateness parameter for the electrogram signal using one of the one or more peaks detected in the LAVA function and a LAVA probability parameter for the electrogram signal.

A wearable cardioverter defibrillator (WCD) comprises a plurality of electrocardiography (ECG) electrodes, a right-leg drive (RLD) electrode, and a plurality of defibrillator electrodes to contact the patient's skin when the WCD is delivering therapy to the patient, a preamplifier coupled to the ECG electrodes and the RLD electrode to obtain ECG data from the patient as one or more ECG vectors, a high voltage subsystem to provide a defibrillation voltage to the patient through the plurality of defibrillator electrodes, and an impedance measurement circuit to measure an impedance across a first pair of ECG electrodes, wherein the impedance measurement circuit is to apply a balancing impedance across a second pair of ECG electrodes when an impedance of the second pair of ECG electrodes is not being measured.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Apparatus for monitoring activation in a heart comprises a probe 101, a plurality of electrodes 102 supported on the probe and extending over a detection area of the probe, the detection area being arranged to contact a detection region of the heart. Each of the electrodes 102 is arranged to detect electrical potential at a respective position in the heart during movement of a series of activation wave fronts across the detection region. A processor is arranged to analyse the detected electrical potentials to identify a propagation direction of at least one of the wave fronts, and to generate an output indicative of that direction.