Abstract: Techniques are disclosed for preparing data for use in artificial intelligence (AI)-based cardiac arrhythmia detection. In accordance with the techniques of this disclosure, a computing system may obtain a cardiac electrogram (EGM) strip that represents a waveform of a cardiac rhythm of a same patient. Additionally, the computing system may preprocess the cardiac EGM strip. The computing system may then apply a deep learning model to the preprocessed cardiac EGM strip to generate arrhythmia data indicating whether the cardiac EGM strip represents one or more occurrences of one or more cardiac arrhythmias.

Abstract: A method of determining signal quality in a patient monitoring device includes acquiring one or more signals using the patient monitoring device. One or more signal quality metrics are determined based on the one or more acquired signals. A noise condition is detected based on the one or more signal quality metrics, and a determination is made whether the noise condition should be classified as intermittent or persistent. One or more actions are taken based on the classification of detected noise as intermittent or persistent.

Abstract: A medical device is utilized to monitor physiological parameters of a patient and capture segments of the monitored physiological parameters. The medical device includes circuitry configured to monitor one or more physiological parameters associated with the patient and an analysis module that includes a buffer and a processor. The buffer stores monitored physiological parameters and the processor analyzes the monitored physiological parameters and triggers capture of segments from the buffer in response to a triggering criteria being satisfied. The analysis module selects a pre-trigger duration based at least in part on the triggering criteria.

Abstract: Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrythmia.

Abstract: A method of determining signal quality in a patient monitoring device includes acquiring one or more signals using the patient monitoring device. One or more signal quality metrics are determined based on the one or more acquired signals. A noise condition is detected based on the one or more signal quality metrics, and a determination is made whether the noise condition should be classified as intermittent or persistent. One or more actions are taken based on the classification of detected noise as intermittent or persistent.

Abstract: Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the 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 one or more sensors configured to generate a signal that indicates a parameter of a patient. Additionally, the medical device system includes processing circuitry configured to receive data indicative of a user indication of an experienced symptom; determine a plurality of parameter values of the parameter based on a portion of the signal corresponding to a period of time including a time before the user indication and a period of time after the user indication. Additionally, the processing circuitry is configured to identify, based on a reference set of parameter values of the plurality of parameter values, the experienced symptom. Additionally, the processing circuitry is configured to save, to a database in memory, a set of data including the experienced symptom and patient parameters.

Abstract: Techniques are disclosed for detecting arrhythmia episodes for a patient. A medical device may receive one or more sensor values indicative of motion of a patient. The medical device may determine, based at least in part on the one or more sensor values, an activity level of the patient. The medical device may determine a heart rate threshold for triggering detection of an arrhythmia episode based at least in part on the activity level of the patient. The medical device may determine whether to trigger detection of the arrhythmia episode for the patient based at least in part on comparing a heart rate of the patient with the heart rate threshold. The medical device may, in response to triggering detection of the arrhythmia episode, collect information associated with the arrhythmia episode.

Abstract: An apparatus comprises an input configured to receive electrocardiogram (ECG) data detected by a patient monitoring device, the ECG data containing a physiologic signal and one or more segments of noise within the ECG data. A scrubber comprises a plurality of scrubbing modules each configured to process the ECG data and noise in a manner differing from other scrubbing modules. The scrubber is configured to filter the one or more noise segments that overlap with the physiologic signal, and consolidate the ECG data to eliminate the one or more noise segments that are non-overlapping with the physiologic signal. An output is configured to output scrubbed ECG data.

Abstract: In some examples, a computing device may receive diagnostic data of a medical device implanted in a patient. The computing device may determine a use case associated with analyzing the diagnostic data out of a plurality of use cases for analyzing the diagnostic data. The computing device may determine, based at least in part on the use case, one or more device characteristics data to be compared against the diagnostic data. The computing device may analyze, based at least in part on comparing the diagnostic data with the one or more device characteristics data, the diagnostic data to determine an operating status of the medical device.

Abstract: Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the episode.

Abstract: Techniques for remote monitoring of a patient and corresponding medical device(s) are described. The remote monitoring comprises determining identification data and identifying implantable medical device (IMD) information, initiating an imaging device and determining an imaging program, receiving one or more frames of image data including image(s) of an implantation site, identifying an abnormality at the implantation site, triggering a supplemental image capture mode, receiving one or more supplemental images of the implantation site, and outputting the one or more supplemental images of the implantation site.

Abstract: Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrythmia.

Abstract: Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

Abstract: Techniques are disclosed for explaining and visualizing an output of a machine learning system that detects cardiac arrythmia in a patient. In one example, a computing device receives cardiac electrogram data sensed by a medical device. The computing device applies a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient and a level of confidence in the determination that the episode of arrhythmia has occurred in the patient. In response to determining that the level of confidence is greater than a predetermined threshold, the computing device displays, to a user, a portion of the cardiac electrogram data, an indication that the episode of arrhythmia has occurred, and an indication of the level of confidence that the episode of arrhythmia has occurred.

Abstract: An apparatus, system, and method directed to detecting a physiological signal during discrete time separated detection windows, deriving one or more respiratory disturbance indices from the physiological signal, detecting a respiratory disturbance state in response to the one or more respiratory disturbance indices deviating from a threshold value, interpolating the one or more respiratory disturbance indices between adjacent time separated detection windows, and declaring a respiratory disturbance episode based on the detected respiratory disturbance state during the detection windows and the interpolation between detection windows.

Abstract: Embodiments relate to a method of monitoring physiological parameters of a patient with renal dysfunction. The method includes electrically connecting one or more medical device electrodes with a measurement site of a patient, generating one or more first stimulation signals sufficient to provide input physiological parameters specific to the patient, measuring one or more first bioimpedance values from the generated signals, analyzing at least one of the input physiological parameters within the one or more first bioimpedance values and generating a personalized dialysis program. The systems and methods can further provide essentially real-time data of patient undergoing treatment and control of treatment to a patient.

Abstract: This disclosure is directed to devices, systems, and techniques for identifying a respiration rate based on an impedance signal. In some examples, a medical device system includes a medical device including a plurality of electrodes. The medical device is configured to perform, using the plurality of electrodes, an impedance measurement to collect a set of impedance values, where the set of impedance values is indicative of a respiration pattern of a patient. Additionally, the medical device system includes processing circuitry configured to identify a set of positive zero crossings based on the set of impedance values, identify a set of negative zero crossings based on the set of impedance values, and determine, for the impedance measurement, a value of a respiration metric using both the set of negative zero crossings and the set of positive zero crossings.

Abstract: Techniques are disclosed for using feature delineation to reduce the impact of machine learning cardiac arrhythmia detection on power consumption of medical devices. In one example, a medical device performs feature-based delineation of cardiac electrogram data sensed from a patient to obtain cardiac features indicative of an episode of arrhythmia in the patient. The medical device determines whether the cardiac features satisfy threshold criteria for application of a machine learning model for verifying the feature-based delineation of the cardiac electrogram data. In response to determining that the cardiac features satisfy the threshold criteria, the medical device applies the machine learning model to the sensed cardiac electrogram data to verify that the episode of arrhythmia has occurred or determine a classification of the episode of arrhythmia.

Abstract: Techniques are disclosed for preparing data for use in artificial intelligence (AI)-based cardiac arrhythmia detection. In accordance with the techniques of this disclosure, a computing system may obtain a cardiac electrogram (EGM) strip that represents a waveform of a cardiac rhythm of a same patient. Additionally, the computing system may preprocess the cardiac EGM strip. The computing system may then apply a deep learning model to the preprocessed cardiac EGM strip to generate arrhythmia data indicating whether the cardiac EGM strip represents one or more occurrences of one or more cardiac arrhythmias.