Systems and methods for assessing sympathetic nervous system (SNS) tone for renal neuromodulation therapy are disclosed herein. A system configured in accordance with embodiments of the present technology can include, for example, a detector attached to or implanted in a patient and a receiver communicatively coupled to the detector. The detector can measure cardiac data and the receiver and/or a device communicatively coupled thereto can analyze the cardiac data to provide one or more SNS tone indicators. The SNS tone indicators can be used to determine whether a patient will be responsive to a neuromodulation therapy and/or whether a neuromodulation therapy was effective.

Systems and methods for pacing cardiac conductive tissue are described. An embodiment of a medical system includes an electrostimulation circuit to generate His-bundle pacing (HBP) pulses to stimulate a His bundle, and a cardiac event detector to detect a His-bundle activity within a time window following an atrial activity. The cardiac event detector may use a cross-chamber blanking, or an adjustable His-bundle sensing threshold, to avoid or reduce over-sensing of far-field atrial activity and inappropriate inhibition of HBP therapy. The electrostimulation circuit may deliver HBP in the presence of the His-bundle activity. The system may further recognize the detected His-bundle activity as either a FFPW or a valid inhibitory event, and deliver or withhold HBP therapy based on the recognition of the His-bundle activity.

A medical device is configured to sense an electrical signal and determine that signal to noise criteria are met based on electrical signal segments stored in response to sensed electrophysiological events. The medical device is configured to determine an increased gain signal segment from one of the stored electrical signal segments in response to determining that the signal to noise criteria are met. The medical device determines a noise metric from the increased gain signal segment. The stored electrical signal segment associated with the increased gain signal segment may be classified as a noise segment in response to the noise metric meeting noise detection criteria.

A sensor on an earpiece is used to attempt to detect a signal corresponding to a heartbeat. If a heartbeat is detected, it can be determined that the earpiece is being worn by a user. The sensor may be an acoustic transducer on a surface of the earpiece that is located within the wearer's ear canal, while the earpiece is being worn normally.

A sensor on an earpiece is used to attempt to detect a signal corresponding to a heartbeat. If a heartbeat is detected, it can be determined that the earpiece is being worn by a user. The sensor may be an acoustic transducer on a surface of the earpiece that is located within the wearer's ear canal, while the earpiece is being worn normally.

In an embodiment, an electrogram (EGM) processing system provides, for display by a head-mounted display (HMD) worn by a user, a holographic rendering of intracardiac geometry. The HMD also displays an electrogram waveform. The EGM processing system determines a gaze direction of the user by processing sensor data from the HMD. The HMD displays a marker overlaid on the electrogram waveform at a location based on an intersection point between the gaze direction and the electrogram waveform. The EGM processing system determines a measurement of the electrogram waveform using the location of the marker. The HMD displays the measurement of the electrogram waveform.

In an embodiment, an electrogram (EGM) processing system provides, for display by a head-mounted display (HMD) worn by a user, a holographic rendering of intracardiac geometry. The HMD also displays an electrogram waveform. The EGM processing system determines a gaze direction of the user by processing sensor data from the HMD. The HMD displays a marker overlaid on the electrogram waveform at a location based on an intersection point between the gaze direction and the electrogram waveform. The EGM processing system determines a measurement of the electrogram waveform using the location of the marker. The HMD displays the measurement of the electrogram waveform.

The present application describes a Personal Hand-Held Monitor (PHHM) of the type described in WO 2013/002165, WO 2014/125431, and International Patent Application No. PCT/EP2015/079888, with improved aspects to find indicators of health, and other improvements that facilitate its construction and calibration.

An automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence, comprising: processing a received original electrocardiogram digital signal to obtain heartbeat time sequence data and lead heartbeat data; cutting the lead heartbeat data according to the heartbeat time sequence data to generate lead heartbeat analysis data; performing data combination on the lead heartbeat analysis data to obtain a one-dimensional heartbeat analysis array; performing data dimension amplification and conversion according to the one-dimensional heartbeat analysis array to obtain four-dimensional tensor data; and inputting the four-dimensional tensor data to a trained LepuEcgCatNet heartbeat classification model, to obtain heartbeat classification information. The method overcomes the defect that the conventional method only depends on single lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.

An automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence, comprising: processing a received original electrocardiogram digital signal to obtain heartbeat time sequence data and lead heartbeat data; cutting the lead heartbeat data according to the heartbeat time sequence data to generate lead heartbeat analysis data; performing data combination on the lead heartbeat analysis data to obtain a one-dimensional heartbeat analysis array; performing data dimension amplification and conversion according to the one-dimensional heartbeat analysis array to obtain four-dimensional tensor data; and inputting the four-dimensional tensor data to a trained LepuEcgCatNet heartbeat classification model, to obtain heartbeat classification information. The method overcomes the defect that the conventional method only depends on single lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.