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510(k) Data Aggregation

    K Number
    K161245
    Device Name
    EP-TRACER System
    Manufacturer
    Date Cleared
    2016-08-05

    (94 days)

    Product Code
    Regulation Number
    870.1425
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The EP-TRACER System is an electrophysiology measurement system used to acquire, filter, digitize, amplify, display, and record signals obtained during electrophysiological studies and related procedures.

    The system allows the user to monitor, display and record the signals. The system incorporates a stimulator intended to be used for diagnostic cardiac stimulation during electrophysiological testing of the heart.

    Device Description

    The EP-TRACER System is a computerized electrophysiology measurement system designed for both regular and experimental EP studies.

    The EP-TRACER System is comprised of these major components,

    1. EP-TRACER hardware – Amplifier/stimulator
    2. EP-TRACER Software – Software pre-installed
    AI/ML Overview

    The EP-TRACER System is an electrophysiology measurement system. The acceptance criteria and supporting study are described below.

    Acceptance Criteria and Reported Device Performance

    TestAcceptance CriteriaReported Performance
    Radiated emission, Conducted emission, Radiated immunity, Conducted immunityPassPass
    Conducted emission (harmonics), Radiated immunity (PMF), Conducted immunityPassPass
    IEC 60601-1:2005 + CORR. 1:2006 + CORR. 2:2007 + AM1:2012PassPass
    Amendment - IEC 60601-1:2005 (Third Edition) + CORR. 1:2006 + CORR. 2:2007 + A1:2012PassPass
    IEC 60601-1-6:2010 (Third Edition) + A1:2013PassPass
    IEC 62366:2007 (First Edition) + A1: 2014 for use in conjunction with IEC 60601-1-6: 2010PassPass
    IEC 60601-1-27:2011 (Third Edition) for use in conjunction with IEC 60601-1:2005 + CORR. 1 (2006) + CORR. 2 (2007)PassPass
    IEC 60601-2-34 (Third Edition): 2011PassPass

    Study Information

    1. Sample size used for the test set and data provenance: Not explicitly stated. The document indicates that "Testing has been conducted through software and/or user verification/validation protocols" and electrical safety and electromagnetic compatibility testing was performed. The data provenance (e.g., country of origin, retrospective/prospective) is not provided.

    2. Number of experts used to establish the ground truth for the test set and their qualifications: Not applicable. The testing primarily focuses on the device's adherence to electrical safety and electromagnetic compatibility standards, and software functionality, rather than diagnostic performance against a medical ground truth established by experts.

    3. Adjudication method for the test set: Not applicable. The testing involves standardized engineering and software validation protocols with pass/fail criteria.

    4. Multi-reader multi-case (MRMC) comparative effectiveness study: No, a MRMC comparative effectiveness study was not done. The device is an electrophysiology measurement system, not an AI-assisted diagnostic tool.

    5. Standalone (algorithm only without human-in-the-loop performance) study: Yes, the performance testing described appears to be a standalone evaluation of the device's compliance with safety and performance standards. The "results" provided in the table indicate whether the device "Passed" tests against specific IEC standards for electrical safety and electromagnetic compatibility.

    6. Type of ground truth used: The ground truth for the performance testing is based on the requirements and standards set forth by the listed IEC and other electrical safety and EMC standards (e.g., IEC 60601-1, IEC 60601-1-2, etc.).

    7. Sample size for the training set: Not applicable. This device is an electrophysiology measurement system with software (V2.0) that "maintains the capabilities in the previous version, adding user interface enhancements." It's not described as a machine learning or AI-driven system that would typically require a training set in the sense of predictive modeling.

    8. How the ground truth for the training set was established: Not applicable, as there is no mention of a training set for machine learning.

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