A monitor recorder-implemented method for electrocardiography value encoding and compression is provided. A plurality of codes are maintained, each associated with an electrocardiography value range. A series of electrocardiography values is obtained. A serial accumulator is set to a predetermined value; each of the obtained electrocardiography values from the series of the obtained electrocardiography values is processed, including: performing a mathematical operation on that obtained electrocardiography value and the serial accumulator; identifying the code within the range associated with which a result of the mathematical operation falls; representing that obtained electrography value with the identified code; and adjusting the serial accumulator by a value derived from the range associated with the identified code. Each of the represented codes are into a sequence in a non-volatile memory.

Intra-cardiac voltage data display systems display a plurality of data sets derived at least from intra-cardiac voltage data sampled by an electrode. In some embodiments, at least some of the data sets are derived from a portion of the intra-cardiac voltage data that excludes an excludable portion of the intra-cardiac voltage data having a relationship with an occurrence of a particular cardiac event to facilitate identification of the existence of a transmural lesion in tissue adjacent the electrode. In some embodiments, the particular cardiac event is the occurrence of an R wave in the cardiac cycle, and the excludable portion is a V wave in the cardiac cycle.

An apparatus and method is provided that identifies the presence or absence of a P-wave within a set of ECG data. A computation processor identifies the R-wave and then analyses a section of the waveform within a predetermined time window preceding the detected R-wave peak. The waveform within the window is analysed to identify a candidate P-wave, and in response to identifying the candidate P-wave a first and second feature associated therewith is measured. A composite feature value is calculated from the first and second measures, and compared to a classification threshold value. In an exemplary embodiment, the first feature represents a height between a highest peak of the candidate P-wave and a trough of the Q-wave, and the second feature represents a time between the peak of the candidate P-wave and a peak of the R-wave.

A system and method of locating a source of a heart rhythm disorder are provided in which a first pair of cardiac signals is processed to define a first coefficient associated with variability of the first pair of signals at a first region of the heart. A second pair of cardiac signals is processed to define a second coefficient associated with variability of the second pair of signals at a second region of the heart. Thereafter, the first coefficient of variability is compared to the second coefficient of variability to determine a direction towards the source of the rhythm disorder.

A cardiac monitoring device includes: at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to: obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event if the ECG-derived score is one of above or below the predetermined threshold score determined by the machine learning.

Methods for displaying physiological mapping data are disclosed. An example method may include storing a set of three-dimensional positional data on a memory, storing a set of metric data on the memory, and storing a set of electrogram data on the memory. The method may also include outputting the set of three-dimensional positional data, the set of two-dimensional metric data, and the set of electrogram data from the memory to a display unit and displaying the set of three-dimensional positional data, the set of two-dimensional metric data, and the set of electrogram data on the display unit as a dynamic display.

A wearable medical device comprising: a plurality of patient interface components each configured to interface with a patient; a first patient interface component of the plurality of patient interface components, the first patient interface component comprising a first sensing electrode for receiving one or more signals from a first patient, and a first therapy electrode for delivering a first treatment to the first patient; a second patient interface component of the plurality of patient interface components, the second patient interface component comprising a second sensing electrode for receiving one or more signals from a second patient; and one or more processors in communication with the plurality of patient interface components, the one or more processors configured to detect a first condition of the first patient based at least in part on the one or more signals from the first patient, cause the first treatment to be delivered to the first patient, wherein the first treatment is based at least in part on the detected first condition of the first patient, and detect a second condition of the second patient based at least in part on the one or more signals from the second patient.

An authentication apparatus includes one or more processors configured to temporally implement a neural network, used to extract a feature value from hidden nodes, that is connected to input nodes to which an electrocardiogram (ECG) signal is input so as to share a weight set with the input nodes, and to match the ECG signal and the extracted feature value to a user for registration.

An arrangement may include a first system provided by a mobile device for processing physiological data representative of a beating heart. The mobile device may be adapted to execute a process for using at least one pattern to detect a notable finding in the physiological data and for sending the notable finding to a second system. The second system may be adapted to execute a process for analyzing the notable finding, for determining at least one new pattern to send to the mobile device, and for sending the at least one new pattern to the mobile device. The at least one new pattern may also include a rule that includes a set of conditions and an action to perform if the set of conditions is met.

Medical devices, software and methods are provided, for making a decision as to whether to pause patient chest compression treatment in connection with administering electric shock therapy to the patient. The decision is made depending whether signal spikes identified in the ECG data are determined to be QRS complexes, or merely likely impulsive artifact caused by the chest compressions.