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510(k) Data Aggregation

    K Number
    K251726
    Device Name
    SignalNED System (Model RE)
    Manufacturer
    Forest Devices, Inc
    Date Cleared
    2025-09-03

    (90 days)

    Product Code
    OMC,GXY,OLT,OLU
    Regulation Number
    882.1400
    Type
    Traditional
    Panel
    Neurology
    Reference & Predicate Devices
    K242306,K171414
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The SignalNED Device is intended to record and store EEG signals for the statistical evaluation of the human electroencephalogram (EEG) and display Quantitative EEG (qEEG) measures intended to help the user analyze the EEG. These measures include relative band power (e.g., alpha, beta, delta, theta) and band power asymmetry (displayed as a z-score compared to a normative database). The SignalNED does not provide any diagnostic conclusion about the patient's condition. The device is intended to be used on adults by qualified medical and clinical professionals.

    Device Description

    The SignalNED Model RE machine uses 10 patient electrodes (4 left, 4 right, 2 midline), which are used to form the 8 channels. The SignalNED machine requires the use of the SignalNED Sensor Cap, and the system includes the following components:

    • Portable EEG machine (Device)
    • Battery & External Battery Charger
    • SignalNED Sensor Cap
    • SignalNED Sensor Cap Cable

    The primary function of the SignalNED Model RE is to rapidly record EEG and derive Quantitative EEG (qEEG) measurements. These measurements include Relative Band Power for multiple bands (e.g., alpha, beta, delta, theta) at each electrode and band power asymmetry (displayed as a z-score compared to a normative database). These measurements are intended to help the user analyze the underlying EEG. The SignalNED Model RE (client) achieves its intended without relying on wireless connectivity. The SignalNED RE does not provide any diagnostic conclusion about the patient's condition.

    AI/ML Overview

    N/A

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