(306 days)
The Wavestate Neuromonitor System is intended to collect, record, and store up to 24 channels of adult EEG data for up to 24 hours. The System also can perform a post review of adult EEG data and identify burst suppression pattern in the stored EEG. The device displays the mean interburst interval reviewed up to that time point and the probability that the displayed value is within +/- 2 seconds of the mean of the interburst intervals for the entire dataset for that patient. The Wavestate Neuromonitor System does not provide any diagnostic conclusion about the patient's condition to the user. The Wavestate Neuromonitor is to be used under the guidance and interpretation of a licensed medical practitioner.
Wavestate, Inc. has created a new application for the TrackIt-2, an FDA-approved ambulatory EEG hardware unit manufactured by Lifelines, Ltd (UK). Our proprietary software analyzes EEG data files recorded with the TrackIt-2. Data are displayed on an Xplore touch-screen tablet computer using Microsoft Windows XP.
Our application is used to quantify the inter-burst interval with 95% statistical confidence the duration of the interval within +/- 2 seconds.
The Trackit-2 system is FDA approved.
FDA-approved EEG electrodes will be bought separately by the end user.
The Wavestate Neuromonitor is an application for an FDA-approved ambulatory EEG hardware unit. Its proprietary software analyzes EEG data, primarily identifying burst suppression patterns and quantifying the inter-burst interval with 95% statistical confidence.
Here's an analysis of the acceptance criteria and the study that proves the device meets them:
1. Acceptance Criteria and Reported Device Performance
The acceptance criteria are implicitly defined by the software verification and validation summary, where the accuracy of specific features is tested against defined thresholds.
| Feature Tested | Acceptance Criterion | Reported Device Performance |
|---|---|---|
| Burst Detection (Single EEG Channel) | Detect spikes of 10 microvolts or higher within 40-ms duration. | Achieved: "demonstrate the accuracy of detection as only spikes of 10 microvolts or higher are identified." |
| Burst Detection (Multiple Channels) | Accurately detect 10.5 microvolt spikes independently and simultaneously in each of 19 channels. | Achieved: "demonstrate accurate detection of 10.5 microvolt spikes identified independently and simultaneously in each EEG of the 19 channels tested." |
| EEG Suppression Detection | Detect suppression only when spikes are separated by 500 ms or longer (defined as 500 ms of activity below 10 microvolts). | Achieved: "demonstrate detection of suppression only when spikes are separated by 500 ms or longer." |
| Calculation and Display of Mean Interburst Interval | Accurately calculate and display the mean interburst interval as an integer, based on inserted spikes at increasing intervals. | Achieved: "demonstrate accurate calculation and display of the mean interburst interval as an integer." |
| Statistical Confidence Computation | Display the mean interburst interval once statistical confidence attains 95% within +/- 2 seconds, and not display it when confidence is below 95%. | Achieved: "demonstrate that the mean interburst interval is displayed once statistical confidence attains 95% and is not displayed when confidence is below 95%." |
2. Sample Size for the Test Set and Data Provenance
The provided document does not specify a sample size for the test set in terms of actual patient data or real EEG recordings. Instead, the testing appears to be based on simulated or synthesized data.
- For burst detection, "40-ms-duration spikes of varying amplitude are inserted into digitized EEG files consisting of background activity."
- For multiple channel detection, "10.5 microvolt spikes identified independently and simultaneously in each EEG of the 19 channels tested."
- For suppression detection, "Spikes of 10.5 microvolt amplitude are inserted, at increasing interval length, into an EEG file consisting of baseline background activity."
- For mean interburst interval calculation, "Spikes of 10.5 microvolt amplitude are inserted at increasing intervals into an EEG file."
- For statistical confidence, "A series of interburst intervals are constructed with 10.5 microvolt spikes."
This suggests the data provenance is synthetic/simulated, not derived from a specific country or retrospective/prospective patient studies.
3. Number of Experts and their Qualifications for Ground Truth
The document does not mention the use of human experts to establish ground truth for the test set. The ground truth for the verification and validation appears to be based on the known parameters of the artificially inserted spikes and constructed intervals.
4. Adjudication Method for the Test Set
Since human experts were not used to establish ground truth, there was no adjudication method described. The validation relied on the algorithm's ability to accurately detect or calculate pre-defined synthetic events.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC comparative effectiveness study was mentioned in the provided summary. The device's validation focuses on its standalone algorithmic performance rather than its impact on human reader performance.
6. Standalone (Algorithm Only) Performance Study
Yes, a standalone study was performed. The entire "Software Verification and Validation Summary" describes the testing of the algorithm itself, without human-in-the-loop. The tests focused on the accuracy of burst detection, channel logic, suppression duration, interburst interval calculation, and statistical confidence, all as performed by the algorithm with synthetic data.
7. Type of Ground Truth Used
The ground truth used was synthetic/known parameters based on artificially injected spikes and constructed EEG patterns. For example, spikes of a known amplitude (e.g., 10 microvolts) were inserted, and the algorithm's ability to detect these known events was evaluated. Similarly, when testing the interburst interval and statistical confidence, known sequences of intervals were constructed.
8. Sample Size for the Training Set
The document does not provide information on the sample size used for a training set. Given the nature of the validation (inserting spikes into EEG files), it's possible the algorithm was developed based on theoretical EEG signal characteristics or a separate, unmentioned dataset. However, no specific training set size or methodology is presented in this 510(k) summary.
9. How the Ground Truth for the Training Set Was Established
The document does not provide information on how the ground truth for a training set was established, as it doesn't mention a distinct training set. If such a set was used, its ground truth establishment method is not described here.
§ 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).