Cardiac Pacemakers, Inc.

Biotronik Se Co. Kg

Jon Peterson

Robert J Sweeney

Kevin John Stalsberg

Shelley Marie Cazares

Haresh G Sachanandani

Krzysztof Z Siejko

Yi Zhang

Gerrard Merrill Carlson

Viktoria A Averina

Ramesh Wariar

Abhilash Patangay

John D Hatlestad

Kent Lee

Eric A Mokelke

Hannes Kraetschmer

Kenneth C Beck

Garth Garner

R Hollis Whittington

Shayan Guhaniyogi

Martin Li

Hannes Kretschmer

An implantable medical device includes an electronic processing device configured for processing a physiological signal, a memory, and a communication device for communicating with an external device. The processing device includes a coding module for coding the physiological signal to obtain an output signal for transmission by the communication device to the external device. The coding module is configured to encode the physiological signal using at least one fixed Huffman code table stored in the memory to obtain the output signal.

Implantable medical device having a coding module

Various embodiments are directed toward a system and method for determining an orientation of an implantable medical device ('IMD') and automatically adjusting a subcutaneous electrocardiogram ('ECG') signal based on the determined orientation. The method can further include tagging generated SECG signals so as to identify whether a SECG signal had been generated from an implantable monitoring device with its first electrode being superior or inferior relative to its second electrode. Further embodiments can include automatically adjusting the orientation of the generated SECG signals to match that of a preferred orientation.

Automatic orientation of subcutaneous ECG in implantable monitoring devices

This document discusses, among other things, systems and methods to enable physician labels on a remote server and use labels to verify and improve algorithm results. A method comprises using patient data in an automated analysis to obtain a result; receiving a message from the user, wherein the message is related to the result; and using at least a portion of the message to automatically modify the analysis.

Method and system to enable physician labels on a remote server and use labels to verify and improve algorithm results

Systems and methods for detecting heart failure by monitoring the time-sequence of physiological changes of a subject using a state machine circuit configured to receive information about physiological characteristics of the subject is described. The current state transitions between a first and a second state in response to a first transition trigger. The current state transitions between the second and first states in response to at least one of the expiration of a first timer or ceasing of the first transition trigger. The current state transitions between the second and third states in response to a second transition trigger. The current state transitions between the third and second states in response to at least one of expiration of a second timer or ceasing of the second transition trigger.

Detecting heart failure by monitoring the time sequence of physiological changes

This document discusses, among other things, systems and methods for predicting heart failure decompensation using within-patient diagnostics. A method comprises detecting an alert status of each of one or more sensors; calculating an alert score by combining the detected alerts; and calculating a composite alert score, the composite alert score being indicative of a physiological condition and comprising a combination of two or more alert scores.

Within-patient algorithm to predict heart failure decompensation

This document discusses, among other things, systems and methods for using one or more inter-relations between within-patient detection methods and between-patient stratifier methods. A method comprises obtaining results of a first physiological assessment, wherein the first physiological assessment includes a between-patient assessment; and using the results to adjust one or more parameters of a second physiological assessment, wherein the second physiological assessment includes a within-patient assessment.

Inter-relation between within-patient decompensation detection algorithm and between-patient stratifier to manage HF patients in a more efficient manner

An apparatus comprises a physiologic sensing circuit and a control circuit. The physiologic sensing circuit is configured to sense an electrical respiration signal representative of respiration of a subject. The control circuit includes a respiration monitor circuit and a therapy circuit. The respiration monitor circuit is configured to extract a respiration parameter from the respiration signal and detect that a value of the respiration parameter is outside of a target value range for the respiration parameter. The therapy circuit is configured to deliver neural stimulation to the carotid sinus of the subject to stimulate respiration and to adjust respiration to maintain the value of the respiration parameter within the target value range.

Neural control of central sleep apnea

This document discusses, among other things, systems and methods for using between-patient comparisons for risk stratification of future heart failure decompensation. A method comprises receiving patient data of a current patient, the patient data collected by a patient monitoring device; determining a reference group related to the patient; determining a reference group dataset selected from the reference group, wherein the dataset includes patient data that is of a similar type received from the patient monitoring device; generating a model of the reference group dataset; and automatically comparing the received physiological data to a model to derive an index for the patient.

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Mahtomedi, MN, United States of America

Jon Peterson