Disclosed includes a body surface device for diagnosing locations associated with electrical rhythm disorders to guide therapy. The device can sense electrical signals and determine multiple sites that may be operative in that patient. The patch may encompass the heart regions from where the heart rhythm disorder originates. The patch comprises an array of electrodes configured to detect electrical signals generated by a heart. A controller may determine the locations of interest based on detected electrical signals. The controller is configured to locate these regions relative to the surface patch. The system may be coupled to a sensor or therapy device inside the heart, to guide this device to a region of interest. The controller is further configured to instruct the operator to use the trigger or source information to treat the heart rhythm disorder in an individual using additional clinical data and methods for personalization such as machine learning.

An illustrative system includes a stimulation device configured to apply stimulation to a recipient, a sensing device configured to detect a physiological condition of the recipient, and a processing unit communicatively coupled to the stimulation device and the sensing device. The processing unit determines a stimulation strategy that is customized to the recipient and includes stimulation frames and stimulation gaps. The processing unit then directs the stimulation device to apply the stimulation to the recipient in accordance with the stimulation strategy by applying the stimulation only during time that corresponds to the stimulation frames. The processing unit also directs the sensing device to detect the physiological condition of the recipient in accordance with the stimulation strategy by detecting only during time that corresponds to the stimulation gaps. Based on the detected physiological condition, the processing unit performs an action. Corresponding systems, methods, and apparatuses are also disclosed.

An illustrative system includes a stimulation device configured to apply stimulation to a recipient, a sensing device configured to detect a physiological condition of the recipient, and a processing unit communicatively coupled to the stimulation device and the sensing device. The processing unit determines a stimulation strategy that is customized to the recipient and includes stimulation frames and stimulation gaps. The processing unit then directs the stimulation device to apply the stimulation to the recipient in accordance with the stimulation strategy by applying the stimulation only during time that corresponds to the stimulation frames. The processing unit also directs the sensing device to detect the physiological condition of the recipient in accordance with the stimulation strategy by detecting only during time that corresponds to the stimulation gaps. Based on the detected physiological condition, the processing unit performs an action. Corresponding systems, methods, and apparatuses are also disclosed.

This application provides a method and apparatus of analyzing the ECG frequency parameters with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases, which relates to the interdisciplinary field of biomedical and science engineering. The method includes obtaining ECG signals from subjects through the designed electrodes; calculating ECG frequency domain parameters of the subjects based on the proposed power spectrum model and getting the analytical validation results after studying and verifying the parameters; generating indicators based on the analytical validation results, which could be potentially used as alternative indicators for STEMI diagnosis; and alerting when the indicators meet preset abnormal conditions. The present embodiment is a powerful tool to diagnose STEMI diseases faster and more effectively and helps patients receive timely assistance and treatment.

An electroanatomical mapping system graphically represents local activation time (LAT) information contained in data set including a plurality of electrophysiology (EP) data points. The system computes a spatial gradient over a spatial kernel centered at an EP data point and a plurality of temporal gradients for the EP data points set. Using the gradients, the electroanatomical mapping system can detect spatial outlier EP data points and temporal outlier EP data points. These outlier EP data points can then be corrected prior to outputting a graphical representation of the LAT map on a model of a cardiac surface.

An electroanatomical mapping system graphically represents local activation time (LAT) information contained in data set including a plurality of electrophysiology (EP) data points. The system computes a spatial gradient over a spatial kernel centered at an EP data point and a plurality of temporal gradients for the EP data points set. Using the gradients, the electroanatomical mapping system can detect spatial outlier EP data points and temporal outlier EP data points. These outlier EP data points can then be corrected prior to outputting a graphical representation of the LAT map on a model of a cardiac surface.

A plurality of electrophysiology (EP) data points, each including an electrogram signal, can be used to visualize cardiac activity. Each EP data point can be characterized as substrate or healthy, and a cloud map of the substrate EP data points can be generated. A graphical representation of the cloud map can be output in combination with a graphical representation of an electrophysiology map of the healthy EP data points. In alternative embodiments, the electrogram signals can be transformed into the wavelet domain, thereby computing a plurality of scalograms, and computing a wave function of each scalogram, thereby computing a plurality of wave functions. A propagation map, such as a propagation wave map and/or propagation wave trail map, can then be generated from the wave functions and output graphically.

A plurality of electrophysiology (EP) data points, each including an electrogram signal, can be used to visualize cardiac activity. Each EP data point can be characterized as substrate or healthy, and a cloud map of the substrate EP data points can be generated. A graphical representation of the cloud map can be output in combination with a graphical representation of an electrophysiology map of the healthy EP data points. In alternative embodiments, the electrogram signals can be transformed into the wavelet domain, thereby computing a plurality of scalograms, and computing a wave function of each scalogram, thereby computing a plurality of wave functions. A propagation map, such as a propagation wave map and/or propagation wave trail map, can then be generated from the wave functions and output graphically.

Systems and methods for optimal selection of beats for a 3D mapping are disclosed. A method in accordance with the present disclosure may be performed on a processor and may comprise receiving a plurality of beats from a catheter. The catheter may be located at a target mapping site, such as a chamber of the heart. A plurality of dynamic filters may be applied to the plurality of collected beats. The optimal beats may be determined and integrated as a beat in the 3D mapping system. This method enables the selection of more beats for a more comprehensive 3D mapping, as well as the selection of more quality beats that are representative of the target anatomy.

Systems and methods for optimal selection of beats for a 3D mapping are disclosed. A method in accordance with the present disclosure may be performed on a processor and may comprise receiving a plurality of beats from a catheter. The catheter may be located at a target mapping site, such as a chamber of the heart. A plurality of dynamic filters may be applied to the plurality of collected beats. The optimal beats may be determined and integrated as a beat in the 3D mapping system. This method enables the selection of more beats for a more comprehensive 3D mapping, as well as the selection of more quality beats that are representative of the target anatomy.