Abstract: An example medical system includes a plurality of electrodes configured to sense a cardiac electrogram of a patient; and processing circuitry configured to: perform feature extraction of the sensed cardiac electrogram to extract a plurality of features from the sensed cardiac electrogram; convert one or more of the plurality of extracted features to a respective feature image; apply a machine learning model, trained using cardiac electrogram data for a plurality of patients, to an image of the sensed cardiac electrogram and at least the respective feature image to determine whether one or more particular heartbeats in the sensed cardiac electrogram indicate a premature ventricular contraction (PVC) beat; and in response to the determination that the one or more particular heartbeats indicates a PVC beat, output a classification that the one or more particular heartbeats is a PVC beat.

Abstract: An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

Abstract: This disclosure is directed to techniques for recording and recognizing physiological parameter patterns associated with symptoms. A medical device system includes a medical device including an accelerometer configured to collect an accelerometer signal that indicates one or more patient movements that occur during a cough. Additionally, the medical device system includes processing circuitry configured to: determine whether the accelerometer signal satisfies a set of criteria corresponding to a cough pattern comprising a smooth increase from a baseline, then a sharp decrease, a peak within the sharp decrease, then a gradual return to the baseline; and identify a cough based on the determination that the accelerometer signal satisfies the set of criteria.

Abstract: A method of detecting sleep apnea includes generating a cardiac signal indicating activity of a heart of a patient. The method further includes determining a short-term average heart rate and a long-term average heart rate. The method further includes determining a start and end of a heart rate cycle based on the short-term average heart rate and the long-term average heart rate. The method further includes determining physiological parameter values occurring during the heart rate cycle. The method further includes determining whether patient has or has not experienced a sleep apnea event based on whether one or more conditions are satisfied by one or more parameter values for one or more heart rate cycles and responsively generating an indication that patient has or has not experienced a sleep apnea event.

Abstract: Techniques for triggering the storage or transmission of cardiac electrogram (EGM) signals associated with a premature ventricular contractions (PVC) include sensing a cardiac EGM signal of a patient via a plurality of electrodes, detecting a premature ventricular contraction (PVC) within the cardiac EGM signal, determining whether PVC storage criteria is met, in response to a determination that the PVC storage criteria is met, storing a portion of the cardiac EGM signal associated with the PVC, and in response to a determination that the PVC storage criteria is not met, eschewing storing the portion of the cardiac EGM signal associated with the PVC.

Abstract: This disclosure is directed to techniques for recording and recognizing physiological parameter patterns associated with symptoms. A medical device system includes a medical device including an accelerometer configured to collect an accelerometer signal that indicates one or more patient movements that occur during a cough. Additionally, the medical device system includes processing circuitry configured to: determine whether the accelerometer signal satisfies a set of criteria corresponding to a cough pattern comprising a smooth increase from a baseline, then a sharp decrease, a peak within the sharp decrease, then a gradual return to the baseline; and identify a cough based on the determination that the accelerometer signal satisfies the set of criteria.

Abstract: Techniques for triggering the storage or transmission of cardiac electrogram (EGM) signals associated with a premature ventricular contractions (PVC) include sensing a cardiac EGM signal of a patient via a plurality of electrodes, detecting a premature ventricular contraction (PVC) within the cardiac EGM signal, determining whether PVC storage criteria is met, in response to a determination that the PVC storage criteria is met, storing a portion of the cardiac EGM signal associated with the PVC, and in response to a determination that the PVC storage criteria is not met, eschewing storing the portion of the cardiac EGM signal associated with the PVC.

Abstract: An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

Abstract: A method of detecting sleep apnea includes generating a cardiac signal indicating activity of a heart of a patient. The method further includes determining a short-term average heart rate and a long-term average heart rate. The method further includes determining a start and end of a heart rate cycle based on the short-term average heart rate and the long-term average heart rate. The method further includes determining physiological parameter values occurring during the heart rate cycle. The method further includes determining whether patient has or has not experienced a sleep apnea event based on whether one or more conditions are satisfied by one or more parameter values for one or more heart rate cycles and responsively generating an indication that patient has or has not experienced a sleep apnea event.

Abstract: An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine a previous RR interval of the cardiac signal and a current RR interval of the cardiac signal based on the determined R-wave. The processing circuitry is further configured to determine a search window based on one or more of the current RR interval or the previous RR interval, determine a T-wave of the cardiac signal in the search window, and determine a QT interval based on the determined T-wave and the determined R-wave.

Abstract: An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

Abstract: This disclosure describes techniques for bypassing an algorithm configured to determine a likelihood of episode data being a false indication of a cardiac episode. A medical device system includes processing circuitry configured to receive episode data and determine, based on satisfaction of one or more bypass conditions of a set of bypass conditions, whether to bypass the algorithm. Responsive to bypassing the algorithm, the processing circuitry stores the episode data as a true indication of the cardiac episode.

Abstract: This disclosure is directed to techniques for recording and recognizing physiological parameter patterns associated with symptoms. A medical device system includes a medical device including an accelerometer configured to collect an accelerometer signal that indicates one or more patient movements that occur during a cough. Additionally, the medical device system includes processing circuitry configured to: determine whether the accelerometer signal satisfies a set of criteria corresponding to a cough pattern comprising a smooth increase from a baseline, then a sharp decrease, a peak within the sharp decrease, then a gradual return to the baseline; and identify a cough based on the determination that the accelerometer signal satisfies the set of criteria.

Abstract: A method of detecting sleep apnea includes generating a cardiac signal indicating activity of a heart of a patient. The method further includes determining a short-term average heart rate and a long-term average heart rate. The method further includes determining a start and end of a heart rate cycle based on the short-term average heart rate and the long-term average heart rate. The method further includes determining physiological parameter values occurring during the heart rate cycle. The method further includes determining whether patient has or has not experienced a sleep apnea event based on whether one or more conditions are satisfied by one or more parameter values for one or more heart rate cycles and responsively generating an indication that patient has or has not experienced a sleep apnea event.

Abstract: This disclosure is directed to techniques for identifying false detection of pause-triggered episode in cardiac ECG data. In some examples, a medical system is configured to receive a cardiac electrogram of a pause-triggered episode, the cardiac electrogram sensed by a medical device via a plurality of electrodes, determine whether one or more of false pause detection criteria are satisfied based on the cardiac electrogram, wherein the one or more of false pause detection criteria comprise: at least one criterion for relative flatness of amplitude values of the cardiac electrogram in a time interval between a last pre-pause beat and a pause detection time, classify the pause-triggered episode as one of a plurality of classifications based on the determination of whether the false pause detection criterion is satisfied, and output an indication of the classification of the pause-triggered episode to a user display.

Abstract: An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine a previous RR interval of the cardiac signal and a current RR interval of the cardiac signal based on the determined R-wave. The processing circuitry is further configured to determine a search window based on one or more of the current RR interval or the previous RR interval, determine a T-wave of the cardiac signal in the search window, and determine a QT interval based on the determined T-wave and the determined R-wave.

Abstract: An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

Abstract: Techniques for triggering the storage or transmission of cardiac electrogram (EGM) signals associated with a premature ventricular contractions (PVC) include sensing a cardiac EGM signal of a patient via a plurality of electrodes, detecting a premature ventricular contraction (PVC) within the cardiac EGM signal, determining whether PVC storage criteria is met, in response to a determination that the PVC storage criteria is met, storing a portion of the cardiac EGM signal associated with the PVC, and in response to a determination that the PVC storage criteria is not met, eschewing storing the portion of the cardiac EGM signal associated with the PVC.

Abstract: Techniques for determining whether a ventricular depolarization is a premature ventricular contraction (PVC) depolarization may include processing circuitry of a medical system identifying an interval from a maximum slope point to a minimum slope point for each of a plurality of ventricular depolarizations and, for each of the plurality of ventricular depolarizations as a current ventricular depolarization, determining that the intervals from the maximum slope point to the minimum slope point for the current ventricular depolarization, a preceding adjacent ventricular depolarization of the plurality of ventricular depolarizations, and a subsequent adjacent ventricular depolarization of the plurality of ventricular depolarizations satisfy one or more slope criteria. The processing circuitry determines that the current ventricular depolarization is a PVC depolarization based on the intervals from the maximum slope point to the minimum slope point satisfying the one or more slope criteria.