Abstract: This disclosure is directed to techniques for identifying false detection of asystole in a cardiac electrogram that include determining whether at least one of a plurality of false asystole detection criteria are satisfied. In some examples, the plurality of false asystole detection criteria includes a first false asystole detection criterion including a reduced amplitude threshold for detecting cardiac depolarizations in the cardiac electrogram, and a second false asystole detection criterion for detecting decaying noise in the cardiac electrogram.

Abstract: A method and apparatus is provided for generating an augmented sample set for enriching a first training dataset for training a model. The method comprises: using data augmentation and corresponding labeling or using label augmentation to add a first augmented sample set to the first training dataset, wherein the data augmentation and corresponding labeling, or the label augmentation purposely puts a first distinguishing characteristic of a first part-of-interest or an associated label into the first training dataset to cause the first distinguishing characteristic of the first part-of-interest to be emphasized to enable the model to learn a generalizable principle of the first distinguishing characteristic, wherein the first distinguishing characteristic is for differentiating the first part-of-interest from a second part-of-interest. Methods for training a model, using a model to differentiate part-of-interests and using a model to infer a dataset are also provided.

Abstract: Medical device systems include processing circuitry configured to acquire sensed cardiac signals associated with cardiac activity of a heart of a patient, and to analyze the sensed cardiac signals to determine if a noise signal is present within the cardiac signals.

Abstract: This disclosure is directed to systems and techniques for detecting change in patient health and if a change in patient health is detected, direct a medical device to generate for display output indicating the detection of the change in patient health. An example medical system or technique applies a model to values of configurable settings that are programmed into detection logic of a medical device; based on the application, determine whether modified values of the configurable settings, when implemented by the detection logic, would change a determination, by the medical device, regarding whether sensed physiological activity is indicative of cardiac episode for a patient; and in response to a determination that the modified values would change the determination regarding whether the sensed physiological activity is indicative of the cardiac episode for the patient, generate output data indicative of the modified values for the configurable settings for the medical device.

Abstract: This disclosure is directed to a medical system and technique for a filter-based approach to arrhythmia detection. In one example, the medical system comprises one or more sensors configured to sense physiological parameter(s); sensing circuitry configured to generate patient data based on the sensed physiological parameter(s), the patient data comprising signal data to represent cardiac activity of the patient; and processing circuitry configured to: detect a cardiac arrhythmia for the patient based on a classification of the signal data in accordance with a machine learning model, wherein the machine learning model comprises filter(s) for at least one portion of the signal data, wherein the at least one filter corresponds to a feature set that maps to the cardiac activity represented by the portion(s) of the signal data; and generate for display output data indicative of a positive detection of the cardiac arrhythmia.

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: A medical system includes communication circuitry configured to receive episode data for an episode sensed by a medical device of a patient, wherein the episode data comprises a cardiac electrogram sensed by the medical device during a period of time; and processing circuitry configured to generate an image based on the episode data, wherein the image is associated with an interval within the period of time; apply, by the processing circuitry, one or more machine learning models to the image, the one or more machine learning models configured to determine whether the image corresponds to an arrythmia type; and output an indication of whether the image corresponds to the arrythmia type.

Abstract: This disclosure is directed to techniques for identifying false detection of asystole in a cardiac electrogram that include determining whether at least one of a plurality of false asystole detection criteria are satisfied. In some examples, the plurality of false asystole detection criteria includes a first false asystole detection criterion including a reduced amplitude threshold for detecting cardiac depolarizations in the cardiac electrogram, and a second false asystole detection criterion for detecting decaying noise in the cardiac electrogram.

Abstract: Medical device systems include processing circuitry configured to acquire sensed cardiac signals associated with cardiac activity of a heart of a patient, and to analyze the sensed cardiac signals to determine if a noise signal is present within the cardiac signals.

Abstract: This disclosure is directed to techniques for identifying false detection of asystole in a cardiac electrogram that include determining whether at least one of a plurality of false asystole detection criteria are satisfied. In some examples, the plurality of false asystole detection criteria includes a first false asystole detection criterion including a reduced amplitude threshold for detecting cardiac depolarizations in the cardiac electrogram, and a second false asystole detection criterion for detecting decaying noise in the cardiac electrogram.

Abstract: Medical device systems include processing circuitry configured to acquire sensed cardiac signals associated with cardiac activity of a heart of a patient, and to analyze the sensed cardiac signals to determine if a noise signal is present within the cardiac signals.

Abstract: This disclosure is directed to techniques for identifying false detection of asystole in a cardiac electrogram that include determining whether at least one of a plurality of false asystole detection criteria are satisfied. In some examples, the plurality of false asystole detection criteria includes a first false asystole detection criterion including a reduced amplitude threshold for detecting cardiac depolarizations in the cardiac electrogram, and a second false asystole detection criterion for detecting decaying noise in the cardiac electrogram.

Abstract: The exemplary systems and methods may be configured to generate a dispersion signal from a plurality of cardiac signals and determine a QRS onset time value and a QRS offset time value from the plurality of cardiac signals. The QRS onset and offset time values may be used to measure, or capture, activation times.

Abstract: Examples described herein include a medical device system comprising an accelerometer circuitry configured to output a signal indicative of variations in accelerations along a single axis of movement of patient; and processing circuitry configured to receive the output signal from the accelerometer, and to rectify the output signal to generate a rectified signal, wherein rectification of the output signal comprises generating a rectified value for each of a plurality of moving windows imposed over the output signal, wherein generating the rectification value for each of the plurality of moving windows comprises determining a current value of the output signal for the window, determining a maximum value for a portion of the output signal enclosed by the window, and subtracting the current value from the maximum value; and analyze the rectified signal to detect the occurrence of a step taken by a patient based on the rectified signal.

Abstract: Medical device systems include processing circuitry configured to acquire sensed cardiac signals associated with cardiac activity of a heart of a patient, and to analyze the sensed cardiac signals to determine if a noise signal is present within the cardiac signals.

Abstract: The exemplary systems and methods may be configured to generate a dispersion signal from a plurality of cardiac signals and determine a QRS onset time value and a QRS offset time value from the plurality of cardiac signals. The QRS onset and offset time values may be used to measure, or capture, activation times.

Abstract: Examples described herein include a medical device system comprising an accelerometer circuitry configured to output a signal indicative of variations in accelerations along a single axis of movement of patient; and processing circuitry configured to receive the output signal from the accelerometer, and to rectify the output signal to generate a rectified signal, wherein rectification of the output signal comprises generating a rectified value for each of a plurality of moving windows imposed over the output signal, wherein generating the rectification value for each of the plurality of moving windows comprises determining a current value of the output signal for the window, determining a maximum value for a portion of the output signal enclosed by the window, and subtracting the current value from the maximum value; and analyze the rectified signal to detect the occurrence of a step taken by a patient based on the rectified signal.

Abstract: A medical device system that includes accelerometer circuitry configured to generate signals including a sagittal axis signal, a vertical axis signal and a transverse axis signal, and processing circuitry configured to calculate a patient-specific functional status parameter associated with a Sit-To-Stand test from at least one of the sagittal axis signal, the vertical axis signal and the transverse axis signal.