Cardiac Pacemakers, Inc.

Boston Scientific Neuromodulation Corporation

Gezheng Wen

Pramodsingh Hirasingh Thakur

Qi An

John D Hatlestad

Deepa Mahajan

Bin Mi

Jonathan Bennett Shute

Krzysztof Z Siejko

Yi Zhang

Rosana Esteller

Tianhe Zhang

Keith L Herrmann

Rezwan Ahmed

Systems and methods are disclosed to determine a measure of patient weight using existing medical device sensors, comprising receiving acceleration information of a patient and, if a value of the acceleration information exceeds an activity threshold over a measurement window, detecting patient steps in the measurement window using the acceleration information, determining a patient step rate over the measurement window using the detected patient steps, determining a measure of patient step force for the measurement window, and determining a measure of patient weight using the determined patient step rate and measure of patient step force.

Acceleration based patient weight determination

Systems and methods for monitoring heart failure (HF) status in a patient are discussed. An exemplary system includes a gait analyzer circuit than can receive gait or balance information, and generate a gait feature such as gait speed or a gait pattern. The system includes a HF detector circuit that can detect patient HF status, or to predict patient risk of a future worsening heart failure (WHF) event, using the gait feature. In some examples, the system may trigger sensing physiologic information according to the detected gait, and detect patient HF status using the sensed physiologic information. The system can initiate or adjust a heart failure therapy according to the HF status or the WHF risk.

Heart failure monitor using gait information

Methods and systems for providing closed loop control of stimulation provided by an implantable stimulator device are disclosed herein. The disclosed methods and systems use a neural feature prediction model to predict a neural feature, which is used as a feedback control variable for adjusting stimulation. The predicted neural feature is determined based on one or more stimulation artifact features. The disclosed methods and systems can be used to provide closed loop feedback in situations, such as sub-perception therapy, when neural features cannot be readily directly measured.

Systems and methods for using electrospinogram signals for closed loop control in Spinal Cord Stimulation therapy

Systems and methods to are disclosed to determine a sleep disordered breathing parameter of a patient, including receiving respiration information of the patient and temperature information of the patient and to determine the sleep disordered breathing parameter of the patient using the received respiration information and temperature information of the patient.

Temperature monitoring for sleep disordered breathing

Systems and methods for classifying a cardiac arrhythmia are discussed. An exemplary system includes a correlator circuit to generate autocorrelation sequences using information of cardiac activity of a subject, including signal segments taken from a cardiac signal at respective elapsed time with respect to reference time. The correlator circuit can generate a correlation image using the autocorrelation sequences. The correlation image may be constructed by stacking the autocorrelation sequences according to the elapsed time of signal segments. An arrhythmia classifier circuit can classify the cardiac activity of the subject as one of arrhythmia types using the correlation image.

Arrhythmia classification using correlation image

Systems, devices, and methods for monitoring and assessing immunotherapy toxicity are discussed. An exemplary system receives physiologic information from a patient using an ambulatory medical device. In response to an immunotherapy such as CAR T-cell therapy, the system determines a toxicity indication using the received physiologic information. A therapy can be initiated or adjusted using the toxicity indication.

Systems and methods for monitoring immunotherapy toxicity

Systems and methods for sensing respiration from a subject are discussed. An embodiment of a respiration monitoring system may include a respiration analyzer circuit to select a physiologic signal from a plurality of signals of different types indicative of respiration, such as between first and second physiologic signals that are respectively detected using first and second detection algorithms, and to compute one or more respiration parameters using the selected signal. The system may select or adjust a respiration detection algorithm for detecting the respiration parameters. The physiologic signal, or the respiration detection algorithm, may each be selected based on a signal characteristic or a patient condition. A cardiopulmonary event may be detected using the computed respiration parameter.

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Shoreview, MN, United States of America

Gezheng Wen