A method of generating a real-time electrophysiology map, such as a cardiac electrophysiology map, includes displaying a cardiac surface model and receiving a plurality of electrophysiology data points (e.g., using a plurality of intracardiac electrodes). The electrophysiology data from the received electrophysiology data points can be displayed and updated on the cardiac surface model in real-time when it satisfies a preset inclusion criteria, such as a projection distance criterion (e.g., the electrophysiology data point is within a preset distance of the cardiac surface model), a contact force criterion (e.g., the intracardiac catheter is exerting at least a preset contact force on the tissue surface), and/or an electrical coupling criterion (e.g., the electrode-tissue electrical coupling exceeds a preset threshold). New electrophysiology data points can be received when a triggering event, such as a point in the cardiac cycle, occurs, or according to a preset timer interval.

The disclosure relates generally to applications of Orientation Independent Sensing (OIS) and Omnipolar mapping Technology (OT) to various system, device and method embodiments as recited herein. Similarly, systems and methods suitable for supporting OIS and OT systems and methods are disclosed. Further, OIS and OT implementations that provide end user interfaces, diagnostic indicia and visual displays generated, in part, based on measured data or derived from measured data are also disclosed. Embodiments also describe applying optimization techniques to determine the greatest voltage difference of a local electric field associated with an electrode-based diagnostic procedure and a vector representation thereof. Various graphic user interface related features are also described to facilitate orientation and electrode clique signal display.

A cardiac device includes an electrocardiograph (ECG device) (10, 110), an electronic processor (12, 36), and a non-transitory storage medium (14, 38) storing instructions readable and executable by the electronic processor to perform a process including: receiving a location of the ECG device; tuning one or more ECG diagnostic criteria for the received location to generate one or more ECG analysis criteria tuned for the received location; operating the ECG device to acquire one or more ECG traces; and performing cardiac analysis by applying the one or more ECG analysis criteria tuned for the received location to the one or more ECG traces to generate cardiac analysis information. The tuning may include associating the ECG device with a medical department based on the location and tuning the one or more ECG analysis criteria for the associated medical department. Alternatively, the associated medical department may be received directly.

An implantable medical device includes a memory storing criteria for transitioning between states of a cardiac cycle model, the states including a P-wave state. The device also includes sensing circuitry that senses a cardiac signal that varies as a function of a cardiac cycle of a patient, and also includes processing circuitry coupled to the sensing circuitry. The processing circuitry is configured to detect an R-wave in the sensed cardiac signal, to determine an elapsed time since the detection of the R-wave, to determine one or more morphological values of a post-R-wave segment of the cardiac signal to compare the elapsed time and the one or more morphological values to the stored criteria for transitioning between the plurality of states of the cardiac cycle model, and to detect a P-wave in the sensed cardiac signal in response to a transition to the P-wave state of the cardiac cycle model.

Apparatus for monitoring activation in a heart comprises a probe (100), a plurality of electrodes (101, 102) supported on the probe and each arranged to detect electrical potential at a respective position in the heart during a series of activations, and processing means (104) arranged to analyse the detected electrical potentials to identify a propagation direction of the activation, and to generate an output indicative of that direction.

A computation apparatus, a cardiac arrhythmia assessment method thereof and a non-transitory computer-readable recording medium are provided. In the method, electrocardiography (ECG) signal is obtained. Whether the ECG signal is conformed to a first abnormal rhythm symptom is determined. Then, whether the ECG signal is conformed to a second abnormal rhythm symptom different from the first abnormal rhythm symptom is determined based on the determined result of the first abnormal rhythm symptom. Accordingly, multiple abnormal rhythm assessments are integrated, the subsequent assessment is speeded-up and optimized according to the determined result of a previous assessment, so as to enable to implement on a handheld apparatus.

Devices and methods are described that provide improved diagnosis from the processing of physiological data. The methods include use of multiple algorithms and intelligently combing the results of multiple algorithms to provide a single optimized diagnostic result. The algorithms are adaptive and may be customized for particular data sets or for particular patients. Examples are shown with applications to electrocardiogram data, but the methods taught are applicable to many types of physiological data.

The present disclosure provides an atrial fibrillation signal recognition method, apparatus and device. The method comprises: obtaining an electrocardiogram signal to be recognized; inputting the electrocardiogram signal to be recognized to a pre-established atrial fibrillation signal recognition model, and outputting an atrial fibrillation signal recognition result, where the atrial fibrillation signal recognition model is established in the following way: obtaining a specified number of electrocardiogram sample signals and corresponding identifier information; balancing, according to the number of normal signals, atrial fibrillation signals by means of SMOTE; establishing a network structure of multiple convolutional neural networks, each of the convolutional neural networks being provided with a specific receptive field for recognizing the atrial fibrillation signals of a corresponding granularity; and inputting the normal signals and the balanced atrial fibrillation signals to the network structure for training to generate an atrial fibrillation signal recognition model.

A system for providing electrocardiogram (ECG) information to a healthcare provider (HCP) is provided. The system includes a network interface configured to receive drug prescription information from a computing device associated with the HCP and ECG data from a medical device associated with a patient. The system also includes a memory configured to store the drug prescription information and the ECG data. The system can further a processor configured to select a period of time over which to review the received drug prescription information and the received ECG data, retrieve the received ECG data for the selected period of time, derive one or more ECG metrics including at least one heart rate parameter, and generate a graphical representation indicating a trend in the one or more ECG metrics and a relationship between the one or more ECG metrics and the drug prescription information.

A computer-implemented method for direct photoplethysmography or direct PPG comprises obtaining during a time interval plural PPG signals for respective sensors in a wearable device; and combining the plural PPG signals to thereby obtain a multi-sensor PPG signal.