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510(k) Data Aggregation
(190 days)
The Digital NeuroPort Biopotential Signal Processing System supports recording, processing, and display of biopotential signals from user-supplied electrodes. Biopotential signals include: Electrocorticography (ECoG), electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), electroculography (EOG), and Evoked Potential (EP).
The Digital NeuroPort Biopotential Signal Processing System is used to acquire, process, visualize, archive/record signals as acquired from user-supplied electrodes for biopotential monitoring. Signals are acquired using a headstage relay that attaches to the pedestal interface and digitizes the signal through the hub. The Digital NeuroPort System uses preamplifiers, analog to digital converters, a signal processing unit, and software running on a personal computer to visualize and record biopotentials from electrodes in contact with the body.
The document describes the Digital NeuroPort Biopotential Signal Processing System, which is a physiological signal amplifier. The device's substantial equivalence to a predicate device (K090957, NeuroPort Biopotential Signal Processing System) is affirmed based on various performance data.
Here's an analysis of the acceptance criteria and the supporting studies:
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Table of Acceptance Criteria and Reported Device Performance:
Test / Characteristic Acceptance Criteria Reported Device Performance NeuroPlex E Functional Testing Mating Screws down on pedestal and LED turns green Pass Crosstalk Isolation resistance of 1kΩ at 500 V DC Pass Label Durability IEC 60601-1:2005/A1:2012, Edition 3.1 7.1.3 Pass Digital Accuracy Appropriate voltages for different filters (0.02-10 kHz Wide, 0.3-7.5 kHz Standard); Peak-to-peak of 500mV ±10% Pass Input Impedance ≥10MΩ Pass Impedance Measurement 820 ± 15% kOhms and 170 ± 15% kOhms Pass Current Rating <1A Pass Stability All channels have neural data from a simulator after 90 attachments and detachments Pass Attachment Two-Finger Tightness Pass Input Noise ≤3 RMS Pass Crosstalk <44mV Pass Leakage IEC 60601-1:2005/A1:2012, Edition 3.1 Pass Breakaway <14lbf Pass Digital Hub Functional Testing Input Power Supply External, medical-grade Pass FPGA Testing from Headstage Accommodates up to 128 channels and channel priority starts with first channel and ends with fourth channel. Pass Output Power Supply to Headstage 4.8V Pass Full-Scale Analog Input ±8.192mV. Pass Burn in Test Hub can run continuously for 12 hours. Pass Compatibility Test Validated data packets received at hub and NeuroPlex E is powered. Pass Digital Neural Signal Simulator (DNSS) Functional Testing Rechargeable Battery Battery life is ≥10 hours. Pass Power Charge battery by Digital Data Cable or USB Pass Digital Digital Hub recognizes DNSS connected through Data Cable. Pass System Functional Testing Synchronization Timestamps aligned within 100 microseconds with maximum capacity of four 128-channel Es, four 128-channel hubs (only one digital data cable from one E to one hub), and two 256 NSPs. Pass Channel Count Facilitates up to 512 channels. Pass Usability Testing IFU Readability Users are able to configure intended settings, assemble the system, and perform maintenance activities all from instruction in the IFU. Pass Impedance, Reference, and Ground Switching Users are able to achieve each possible configuration prompted by the facilitator. Pass Cleaning Users do not damage the device during cleaning. Users identify the proper cleaning solutions. Users indicate that the instructions are sufficiently clear. Pass Other Performance Data Electrical Safety/EMC Compliance with IEC 60601-1:2012, Ed 3.1 and IEC 60601-1-2:2014, Ed 4.0 Compliant Biocompatibility Endpoints assessed: cytotoxicity, irritation, or sensitization, per ISO 10993-1:2018 (for NeuroPlexE and pedestal) Achieved Sterility Sterilized with 100% EtO to a SAL of 10^-6, per ISO 11135-1:2014/07/15 Achieved Residuals (EtO) EO levels < 4mg/24hr, ECH levels < 9mg/24hr; both < 60mg/30 days, per ISO 10993-7:2008/10/15 Achieved Shelf Life (Sterile) 18 months, validated by accelerated aging per ASTM F1980-16 for representative device Patient Cable. Packaging conforms to ISO 11607-1:2019, ASTM D4332-14, ASTM D4169-16, ASTM F1886-09/(R)2013, ASTM F2096-11, F88/F88M-15. 18 months achieved -
Sample size used for the test set and the data provenance:
The document focuses on engineering performance testing, usability testing, and bench testing, rather than studies involving patient data or clinical datasets. Therefore, information about a "test set" in the context of clinical data (e.g., medical images, physiological recordings from patients for algorithm evaluation) is not provided. The samples used for testing are the physical components of the device (NeuroPlex E, Digital Hub, Digital Neural Signal Simulator (DNSS), and the full system). The provenance of these test samples is not explicitly stated beyond being the components of the "Digital NeuroPort Biopotential Signal Processing System." The types of tests conducted are typically performed retrospectively using manufactured device units in a lab setting. -
Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
This information is not applicable to the type of testing described. The document describes engineering, electrical, mechanical, and usability tests for a physiological signal processing system. Ground truth in this context is established by engineering specifications, international standards (e.g., IEC, ISO, ASTM), and predefined functional requirements, not by expert interpretation of patient data. For usability testing, "users" are involved, but their specific qualifications (e.g., specific clinical experience) or number are not detailed beyond "users" being able to interact with the device and its instructions. -
Adjudication method (e.g. 2+1, 3+1, none) for the test set:
Not applicable. Adjudication methods are typically used in clinical studies when establishing ground truth from multiple expert interpretations of patient data. The current document reports on bench testing, functional verification, and safety compliance, where results are measured against objective criteria from standards or specifications. -
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. The Digital NeuroPort Biopotential Signal Processing System is a device for recording, processing, and display of biopotential signals. It is not described as an AI-powered diagnostic or interpretive tool that assists human "readers" (e.g., radiologists, cardiologists) in making diagnoses. Therefore, an MRMC study or AI-assisted performance improvement analysis is outside the scope of this submission. -
If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
Not applicable. As noted above, the device is a signal processing system, not an AI algorithm intended for standalone diagnostic performance evaluation. The "performance data" presented is about the physical and electrical functioning of the device components and the system as a whole, not about an algorithm's diagnostic accuracy. -
The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
For the functional and safety tests, the "ground truth" or reference values are defined by:- International Standards: e.g., IEC 60601-1, IEC 60601-1-2, IEC 60601-2-26, ISO 10993-1, ISO 11135-1, ISO 10993-7, ISO 11607-1, ASTM D4169-09, ASTM D4332-14, ASTM F88/F88M-15, ASTM F1886-09/(R)2013, ASTM F1980-16, ASTM F2096-11.
- Engineering Specifications: e.g., isolation resistance (1kΩ), digital accuracy (500mV ±10%), input impedance (≥10MΩ), current rating (<1A), input noise (≤3 RMS), Synchronization within 100 microseconds, channel count (up to 512), battery life (≥10 hours), output power supply (4.8V), full-scale analog input (±8.192mV).
- Functional Demonstrations: e.g., "Screws down on pedestal and LED turns green" for mating, "All channels have neural data from a simulator after 90 attachments and detachments" for stability.
- Usability Objectives: Users' ability to configure settings, assemble the system, perform maintenance, or use cleaning solutions correctly.
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The sample size for the training set:
Not applicable. This device is not an AI/ML algorithm that requires a training set of data. The "training" in this context refers to the manufacturing and testing of hardware and software against pre-defined specifications. -
How the ground truth for the training set was established:
Not applicable, as there is no "training set" in the context of AI/ML for this device. The development and validation of the device components and system are based on engineering principles and regulatory standards.
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