(30 days)
The EEG-1100A is intended to record, measure and display cerebral and extracerebral activity for EEG and Sleep Studies. These data may be used by the clinician in Sleep Disorder, Epilepsies and other related disorders as an aid in diagnosis.
The EEG-1100A Switch Boxes product is connected between the multi-channel electrode junction boxes. JE-207A/209A/212A and a mini electrode junction box and switches the signal line of electrodes to EEG or stimulation unit. It corresponds to 65 to 128 channels and 129 to 192 channels. The new EEG-1100A Switch Boxes device has two switch boxes, the AAA-15919 switch box which is designed for the JE-207A, JE-209A, JE-212A electrode Junction Boxes and JE-208A, JE-210A, JE-214A, JE-214A, JE-2156A, JE-216A, JE-216A, JE-217A Mini Junction Boxes and the AAA-16060 192 channel Switch Box which is designed for the JE-212A Electrode Junction Box and JE-213A, JE-214A, JE-217A Mini Junction Boxes. The electrical stimulation signals from an electrical stimulation unit can be applied to each electrode through the switch box. The AAA-6060 192 channel switching is available by connecting to the AAA-15919 128 channel Switch Box.
Here's an analysis of the provided text regarding the acceptance criteria and study proving the device meets them, presented in the requested format:
The provided text focuses on a Special 510(k) Notification for EEG-1100A Switch Boxes by Nihon Kohden America, Inc. It details the device's classification, predicate device, intended use, and technological characteristics. However, the document primarily addresses regulatory aspects, substantial equivalence, and general testing categories. It does not contain detailed information about specific acceptance criteria or a dedicated study design with quantitative results in the way typically expected for a performance study of a device with diagnostic or analytical claims.
The “performance testing” mentioned is very general and focuses on verifying operation rather than demonstrating diagnostic accuracy against a ground truth.
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria Category | Specific Acceptance Criteria | Reported Device Performance |
|---|---|---|
| Safety | Device does not directly contact patients. Accessories that contact patients are the same as predicate or comprise same material/design/manufacturing. | Verified. (Implicitly, as no safety issues are reported, and accessories are deemed equivalent or commercially available.) |
| Environmental | Meets relevant environmental standards. | Verified. (Tests "verified the operation of the device.") |
| EMC (Electromagnetic Compatibility) | Meets relevant electromagnetic standards. | Verified. (Tests "verified the operation of the device.") |
| Performance (General Operation) | Device performs within specifications for acquiring, processing, displaying, and recording EEG/stimulation signals. | "The results confirmed that the device performed within specifications." |
| Software Validation | Software functions correctly for acquiring, processing, displaying, and recording all device functions. | "Software validation tested the operation of the software functions or acquiring, processing, displaying and recording of all functions of the device. The results confirmed that the device performed within specifications." |
| Sterility | Not applicable. Device is not sterile. | Device is not sterile. |
Note: The document only provides high-level statements about testing being conducted and successfully meeting specifications. It does not provide specific numerical performance metrics, thresholds, or detailed pass/fail criteria for functionalities like signal integrity, accuracy, or specific clinical outcomes. This is typical for a 510(k) for an accessory device (switch box) that primarily enables functionality of a larger system (EEG) rather than performing a diagnostic function itself.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document does not describe a clinical "test set" in the context of patient data or samples. The testing described (electromagnetic, environmental, safety, performance, and software validation) would have been conducted on the device itself (prototypes or production units) rather than on patient data. Therefore, questions of sample size and data provenance for a test set of patient data are not applicable to the information provided.
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)
Not applicable. As no clinical "test set" requiring ground truth establishment is described, neither is the involvement of experts for this purpose.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. No clinical "test set" and thus no adjudication method are described.
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. The device is a switch box for an EEG system, not an AI-powered diagnostic or assistive tool. No MRMC study or AI assistance is mentioned.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. The device is hardware (switch boxes), not an algorithm or AI.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The concept of "ground truth" as typically applied to diagnostic or prognostic device performance studies is not directly applicable here. The "ground truth" for the device's performance would be engineering specifications and functional requirements against which the device's operational behavior (e.g., signal switching, signal integrity, electrical safety) was verified through technical testing.
8. The sample size for the training set
Not applicable. The device is a hardware accessory and does not involve machine learning algorithms that require training data.
9. How the ground truth for the training set was established
Not applicable. The device is a hardware accessory and does not involve machine learning algorithms that require training data or a ground truth for training.
§ 882.1400 Electroencephalograph.
(a)
Identification. An electroencephalograph is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.(b)
Classification. Class II (performance standards).