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510(k) Data Aggregation

    K Number
    K040113
    Manufacturer
    Date Cleared
    2004-02-18

    (29 days)

    Product Code
    Regulation Number
    882.1835
    Reference & Predicate Devices
    Predicate For
    Why did this record match?
    Reference Devices :

    K003154

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The SD20 Amplifier is intended to be used by or under the direction of a physician for acquisition of EEG, polygraphy and polysomnography signals and transmission of these signals to a PC during recording of neurophysiology examinations.

    Device Description

    The Sandman SD20 Amplifer is a fully programmable system which provides a total of 22 analog input channels cach of which can be configured, amplified and converted to digital form (analog to digital conversion). The amplifier receives its power from a dedicated AC/DC adapter, meeting the IEC 601-1 requirements, which feeds a +15VDC. Internally, the +15VDC is further isolated by a dedicated DC/DC CF type converter.

    The Sandman SD20 Amplifier is intended to be used to amplify and filter bioelectric signals captured via a lead or transducer on the surface of the human body. It captures the data, converts it into a digital form and passes it on to a host computer running appropriate amplification software. Typical fields of application will be: Electroencephalograph (EEG), Evoked Potentials (EP), Electromyography (EMG), Polysomnography (Sleep Analysis) and General Polygraphy.

    The Sandman SD20 Amplifier contains a Pulse Oximeter module. The data measured from this module is passed to the host computer together with the other channels' data. The MP100 Pulse Oximeter module, developed by Nellcor Puritan Bennett, is substantially equivalent to Nellcor Puritan Bennett's MP400 Pulse Oximeter module cleared with EB Neuro's Mizar Amplifier via K003154.

    The host computer must use one of the following Operating Systems: Microsoft Windows 98, Microsoft Windows NT or Microsoft Windows XP.

    The Sandman SD20 Amplifier system consists of four interconnected units: the headbox, the amplifier box, the PC interface (BE Net/Sandman eLink) and the AC/DC adapter.

    AI/ML Overview

    The provided text is a 510(k) premarket notification for the Sandman SD20 Amplifier. This document focuses on demonstrating substantial equivalence to a predicate device (EB Neuro Mizar Amplifier K003154) rather than presenting a standalone study with acceptance criteria and performance data in the traditional sense of a clinical trial or performance study for a diagnostic algorithm.

    Therefore, the information requested regarding acceptance criteria, study design, sample sizes, expert ground truth, adjudication methods, MRMC studies, standalone performance, and training set details is largely not present in this type of regulatory submission.

    The acceptance criteria in this context are implicitly that the new device (Sandman SD20 Amplifier) performs at least as well as, or is substantially equivalent to, the predicate device (EB Neuro Mizar Amplifier K003154) based on technological characteristics and intended use.

    Here's an attempt to answer your questions based only on the provided text, highlighting where information is absent:


    1. Table of Acceptance Criteria and Reported Device Performance

    Note: The provided document does not explicitly state "acceptance criteria" as performance metrics with thresholds like sensitivity, specificity, accuracy, or a specific signal quality benchmark. Instead, it presents a comparison of technological characteristics between the proposed device and its predicate, implying that performance is acceptable if these characteristics are substantially equivalent and meet established industry standards for physiological signal amplifiers.

    CharacteristicAcceptance Criteria (Implied by Predicate)Reported Device Performance (Sandman SD20 Amplifier)
    Intended UseAcquisition of EEG, polygraphy and polysomnography signals and transmission of these signals to a PC during recording of neurophysiology examinations (Same as predicate)Acquisition of EEG, polygraphy and polysomnography signals and transmission of these signals to a PC during recording of neurophysiology examinations
    A/D conversion16 bit Sigma-Delta A/D effectively transferred to host16 bit SAR effectively transferred to host
    Sampling rateUser selectable (128, 256, 512...up to 32 KHz/Channel)User selectable (128, 256, 512...up to 8192 Hz/Channel)
    CMMR>100dB>100dB
    Noise< 1.5 $\mu$ Vpp<0.5 $\mu$ Vrms(AC) <7 $\mu$ Vrms(DC)
    Safety StandardsIEC 601-1-1, IEC 601-1-2, IEC 601-1-4IEC 601-1, IEC 601-1-2, IEC 601-1-4
    Pulse Oximetry Module (Function)Provided by Nellcor Puritan Bennett MP404 (with predicate)Provided by Nellcor Puritan Bennett MP100

    Note on "Acceptance Criteria" interpretation: For a 510(k) of a device like a physiological signal amplifier, "acceptance criteria" are generally met by demonstrating that the device meets recognized standards (e.g., IEC 601-1) for electrical safety and EMC, and that its technical specifications (like noise, CMMR, sampling rate) are equivalent or better than a legally marketed predicate device for the same intended use. The table above reflects this comparison of technical specifications, as no specific performance outcomes (e.g., diagnostic accuracy for a specific condition) are being measured or reported in this document. The "Accepatance Criteria" column is essentially reflecting the specifications of the predicate device, which the new device aims to be substantially equivalent to or better than.

    2. Sample size used for the test set and the data provenance

    • Sample Size: Not applicable/Not mentioned. This document is for a physiological signal amplifier, not a diagnostic algorithm that processes patient data to generate a finding. The "test set" would refer to the device itself being tested for its technical specifications and compliance with standards.
    • Data Provenance: Not applicable/Not mentioned. The document describes the device hardware and its intended use for capturing bioelectric signals, not the analysis of pre-existing patient data.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

    • Not applicable/Not mentioned. Ground truth in this context would be related to the accurate measurement of electrical signals, typically assessed through engineering tests and calibration against known standards, not by expert interpretation of patient data.

    4. Adjudication method for the test set

    • Not applicable/Not mentioned. Adjudication methods are typically associated with resolving discrepancies in expert interpretations of diagnostic images or clinical data, which is not the subject of this 510(k).

    5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

    • Not applicable/Not mentioned. This is a medical device (signal amplifier) and not an AI-powered diagnostic system or an aid for human readers. Therefore, an MRMC study is not relevant to this submission.

    6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

    • Not applicable/Not mentioned. The device is a hardware amplifier; it does not contain a standalone algorithm for diagnostic interpretation in the sense of AI. Its function is to acquire and transmit signals to a host computer for further software processing by a physician.

    7. The type of ground truth used

    • Ground Truth: For a physiological signal amplifier, "ground truth" would be the known electrical properties and performance standards against which the device's technical specifications (e.g., noise, Common Mode Rejection Ratio (CMMR), sampling rate accuracy) are measured and verified during engineering and compliance testing. This document does not detail these specific tests but rather presents the device's measured characteristics.

    8. The sample size for the training set

    • Not applicable/Not mentioned. This device does not use machine learning or AI that requires a "training set" of data.

    9. How the ground truth for the training set was established

    • Not applicable/Not mentioned. As there is no training set for an AI/ML algorithm, this question is not relevant.
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