Zoll Medical Corporation

Zoll Medical Corption

Francesco Nicolo

Gary A Freeman

Thomas E Kaib

Steven J Szymkiewicz

Adam Sullivan

Shane S Volpe

Timothy F Stever

Steve Szymkiewicz

Jason T Whiting

Michael Andrew Sipe

Jeffery T Mitchell

Guy Robert Johnson

Gregory R Frank

Binwei Weng

Steve Ringquist

An ECG processing system for identifying noisy ECG data segments is provided. The ECG processing system includes a network interface and a processor. The network interface is configured to receive a first collection of ECG data segments from wearable medical devices associated with a plurality of patients, each wearable medical device of the wearable medical devices being configured to be continuously worn by a patient for an extended period of time. The processor is configured to produce a second collection of ECG data segments from the first collection of ECG data segments that excludes noisy ECG data segments.

Single channel and dual channel noise detection systems and techniques

A system including a medical device is provided. The medical device includes at least one sensor configured to acquire first data descriptive of a patient, first memory storing a plurality of templates, and at least one processor coupled to the at least one sensor and the first memory. The at least one processor is configured to identify a first template of the plurality of templates that is similar to the first data, to determine first difference data based on the first template and the first data, and to store the first difference data in association with the first template. The system may further include the programmable device.

Systems and methods of patient data compression

A system and method for medical premonitory event estimation includes one or more processors to perform operations comprising: acquiring a first set of physiological information of a subject, and a second set of physiological information of the subject received during a second period of time; calculating first and second risk scores associated with estimating a risk of a potential cardiac arrhythmia event for the subject based on applying the first and second sets of physiological information to one or more machine learning classifier models, providing at least the first and second risk scores associated with the potential cardiac arrhythmia event as a time changing series of risk scores, and classifying the first and second risk scores associated with estimating the risk of the potential cardiac arrhythmia event for the subject based on the one or more thresholds.

Medical premonitory event estimation

A system includes an ambulatory medical device and a server. The ambulatory medical device comprises: a digital signal processing module configured to: detect an abnormal rhythm from an electrocardiogram (ECG) signal of a patient using a first signal processing routine; and generate a first flag indicating an abnormal rhythm is detected; and a noise detector module configured to: receive the ECG signal from the digital signal processing module; execute a second signal processing routine to classify the abnormal rhythm as one of an arrhythmia event and a noise event; and, if the abnormal rhythm is classified as a noise event, initiate a preconfirmation period during which the noise detector module continues to evaluate the abnormal rhythm and classify the abnormal rhythm as one of an arrhythmia event and a noise event using the second signal processing routine; and generate a second flag indicating the start of the preconfirmation period; and a server configured to: receive the ECG signal, the first flag indicating the abnormal rhythm, and the second flag indicating the start of the preconfirmation period; and provide a visual indication of the preconfirmation period.

System and method for distinguishing a cardiac event from noise in an electrocardiogram (ECG) signal

A cardiac monitoring device includes: at least one sensing electrode for obtaining an electrocardiogram (ECG) signal from a patient; a processing unit comprising at least one processor operatively coupled to the at least one sensing electrode; and at least one non-transitory computer-readable medium comprising program instructions that, when executed by the at least one processor, causes the cardiac monitoring device to: obtain the ECG signal from the at least one sensing electrode; determine a transformed ECG signal based on the ECG signal; extract at least one value representing at least one feature of the transformed ECG signal; provide the at least one value to determine a score associated with the ECG signal, thereby providing an ECG-derived score; compare the ECG-derived score to a predetermined threshold score determined by machine learning; and provide an indication of a cardiac event if the ECG-derived score is one of above or below the predetermined threshold score determined by the machine learning.

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Oakmont, PA, United States of America

Francesco Nicolo