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510(k) Data Aggregation
(28 days)
The BE Plus / AURA-LTM64 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.
The BE Plus / AURA-LTM64 Amplifier is a fully programmable system which provides a total of 64 analog input channels each 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 60601-1 requirements, which feeds a +15VDC. Internally, the +15VDC is further isolated by a dedicated DC/DC CF type converter. The BE Plus / AURA-LTM64 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), Polysomnography (Sleep Analysis) and General Polygraphy. The BE Plus / AURA-LTM64 Amplifier does not contains a Pulse Oximeter module. The host computer must use one of the following Operating Systems: Microsoft Windows 98, Microsoft Windows NT or Microsoft Windows XP. The BE Plus / AURA-LTM64 Amplifier system consists of three interconnected units: the amplifier box, the PC interface (PCMCIA interface or BE Net) and the AC/DC adapter; optionally the system may be completed by a led visual stimulator.
The provided text is a 510(k) Summary for a medical device (BE Plus / AURA-LTM64 Amplifier) seeking FDA clearance. It focuses on demonstrating substantial equivalence to predicate devices based on technological characteristics and intended use. This type of submission document does not typically include detailed information about specific acceptance criteria or a dedicated study proving performance against those criteria in the way a clinical trial or performance study report would.
Therefore, I cannot extract the full scope of requested information from the provided text. However, I can provide what is explicitly mentioned or can be reasonably inferred from the document regarding "acceptance criteria" through a comparison to predicate devices.
Here's an analysis based on the available text:
The "acceptance criteria" for this 510(k) submission are fundamentally based on demonstrating substantial equivalence to legally marketed predicate devices, specifically the EB Neuro MIZAR Amplifier (K003154) and the EB Neuro Nic36 Amplifier (K041198). The study that "proves" the device meets these criteria is the comparison of technological characteristics presented in the 510(k) summary itself. The FDA's acceptance (issuance of a substantial equivalence determination) is the "proof" that these criteria have been met for market clearance.
1. A table of acceptance criteria and the reported device performance
The acceptance criteria are not explicitly stated as quantitative thresholds like in a performance study. Instead, they are implicitly defined by the characteristics of the predicate devices. The "reported device performance" is the description of the new device's characteristics.
| Characteristic (Implicit Acceptance Criterion by Predicate) | Predicate Devices (Accepted Performance) | BE Plus / AURA-LTM64 Amplifier (Reported Performance - Must be substantially equivalent) |
|---|---|---|
| Intended Use | Acquisition of EEG, polygraphy and polysomnography signals and transmission of these to a PC during recording of neurophysiology examinations | Acquisition of EEG, polygraphy and polysomnography signals and transmission of these to a PC during recording of neurophysiology examinations |
| EEG/Polygraphic channels | 32/40 (MIZAR), 36 fixed (Nic36) | 64 monopolar (128 with double Amplifier configuration expansion) |
| DC channels | 32/40 (MIZAR), 4 (Nic36) | 4 |
| A/D conversion | 16 bit Sigma-Delta (MIZAR), 16 bit SAR (Nic36) | 16 bit SAR effectively transferred to host |
| Sampling rate | User selectable (up to 32KHz/Channel for MIZAR, up to 8192 Hz/Channel for Nic36) | User selectable (128, 256, 512... up to 4096 Hz/Channel) |
| CMRR (Common Mode Rejection Ratio) | >100dB (Both Predicates) | >100dB |
| Noise | <1.5μVpp (0.5μVrms) (MIZAR), <0.5μVrms (AC) / <7μVrms (DC) (Nic36) | <0.5μVrms (AC) / <7μVrms (DC) |
| Power Supply | External IEC 60601-1 Mains (Both) | External IEC 60601-1 Mains |
| Internal storage | N/A (Both) | Auxiliary FLASH memory to accumulate data when optical link is temporary unconnected. Real time clock on board. Precision of hundreds of seconds. Coin battery auxiliary supply. (This is a new feature that does not detract from substantial equivalence, as it is an enhancement). |
| Amplifier - PC Interface | PCMCIA or BE Net (Both) | PCMCIA or BE Net |
| Other Interfaces | 128x64 graph LCD, 5 push buttons (MIZAR); Power On LED / LED matrix Ohm Meter (Nic36) | Power On LED (bicolor) / LED matrix Ohm Meter |
| Use standard sensors and electrodes | Yes (Both) | Yes |
| Dimensions | 250 (L) x 170 (W) x 65 (H) (MIZAR); 203 (L) x 135 (W) x 38 (H) (Nic36) | 194 (L) x 125 (W) x 37 (H) |
| Case material | Polycarbonate plastic (Both) | Polycarbonate plastic |
| Total weight | 1.5 Kg (MIZAR), 0.55 Kg (Nic36) | 0.48 Kg |
| Isolation | Fiber optic link; Patient isolation BF type (MIZAR); Patient isolation CF type (Nic36) | Fiber optic link; Patient isolation CF type |
| Safety Standard | IEC 60601-1, IEC 60601-1-2, IEC 60601-2-26, IEC 60601-1-4 (MIZAR); + IEC 60601-2-40 (Nic36) | IEC 60601-1, IEC 60601-1-2, IEC 60601-2-26, IEC 60601-2-40, IEC 60601-1-4 |
| Firmware | Resident and Runtime downloadable (Both) | Resident and Runtime downloadable |
| Patient connection and inputs | Various configurations of Monopolar, Bipolar, Reference, ISO GROUND inputs. (Both) | 64 Monopolar inputs - 64 plugs; 2 References inputs - 2 plugs; 2 ISO GROUND inputs - 2 plugs |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the 510(k) summary. 510(k) submissions demonstrating substantial equivalence, especially for devices like physiological signal amplifiers, typically rely on engineering testing to confirm specifications, not patient data in the sense of a clinical trial. The "test set" here refers to the device itself being tested against its specifications and then compared to the predicate.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
This information is not provided and is not applicable to a 510(k) submission for a physiological signal amplifier based on substantial equivalence. There is no "ground truth" in the clinical sense established by experts as would be for an AI diagnostic device. The performance specifications are engineering parameters.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided and is not applicable for this type of submission.
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
No, an MRMC comparative effectiveness study was not done. This device is a physiological signal amplifier, not an AI-assisted diagnostic tool for human readers.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
No, a standalone algorithm performance study was not done. This device is hardware and associated firmware/software for signal acquisition, not a standalone algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The "ground truth" for this device's performance, in the context of a 510(k) submission for substantial equivalence, is essentially the engineering specifications and demonstrated performance of the predicate devices. The new device's performance characteristics (e.g., CMRR, noise, sampling rate) are measured and then compared to those of the predicates to show it is as safe and effective. It does not involve clinical "ground truth" from patient data.
8. The sample size for the training set
This information is not provided and is not applicable. This device is not an AI/ML model that requires a training set of data.
9. How the ground truth for the training set was established
This information is not provided and is not applicable.
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