(88 days)
The REMI Remote EEG Monitoring System is indicated for use in healthcare settings where near real-time and/or remote EEG is warranted and in ambulatory settings where remote EEG is warranted. REMI uses single patient, disposable, wearable sensors intended to amplify, capture, and wirelessly transmit a single channel of electrical activity of the brain for a duration up to 30 days.
The REMI System uses the REMI-Mobile software application that runs on qualified portable general purpose computing platforms. REMI-Mobile displays user setup information to trained medical professionals and provides notifications to medical professionals and ambulatory users. REMI-Mobile receives and transmits data from connected REMI Sensors to the secure REMI-Cloud where it is stored and prepared for review on qualified EEG viewing software.
REMI does not make any diagnostic conclusion about the subject's condition and is intended as a physiological signal monitor. The REMI System is indicated for use with adult and pediatric patients (6+ years).
The REMI System has three major components:
- REMI Sensor A disposable EEG sensor which is placed on the patient's scalp using a conductive REMI Sticker
- REMI Mobile A mobile medical application that is designed to run on a qualified commercial-off-the-shelf mobile computing platform (an Android tablet for use in healthcare settings, and a portable/wearable Android smartwatch for use in ambulatory settings), acquire EEG data transmitted from REMI Sensors and then transmit the EEG data and associated patient information via wireless encrypted transmission to,
- REMI Cloud A HIPAA-compliant secure cloud storage and data processing platform where data is processed into a qualified EEG reviewing software format for neurological review.
This 510(k) submission includes the addition of the Android smartwatch for ambulatory use and increases the duration of monitoring to up to 30 days.
The provided text describes the REMI Remote EEG Monitoring System and its substantial equivalence to a predicate device. However, it does not include specific quantitative acceptance criteria or detailed study results that would typically be associated with performance metrics like sensitivity, specificity, accuracy, or effect sizes for AI assistance. The document focuses on demonstrating substantial equivalence through testing of electrical safety, wireless technology, software, and human factors.
Here's an attempt to answer your questions based on the available information, with acknowledgements where information is missing.
1. A table of acceptance criteria and the reported device performance
Based on the provided text, the acceptance criteria are generally framed around meeting regulatory standards and functional requirements rather than quantitative performance metrics for diagnostic accuracy.
| Acceptance Criteria Category | Reported Device Performance |
|---|---|
| Electrical Safety / EMC / Ingress Protection | Met all relevant standards: IEC 60601-1, IEC 60601-1-2, IEC 60601-2-26, IEC 60601-1-11:2015 /A1:2021. |
| Wireless Technology Functionality | - Wireless connections can be initiated, are stable, and accurately transfer EEG signals. |
- Wireless connection maintained for a minimum of 48 continuous hours. |
| Environmental/Shelf life | Accelerated aging and subsequent functional verification testing conducted. (No specific performance metrics are given, but implies successful completion). |
| Packaging Performance | Ship testing and subsequent functional verification testing conducted. (No specific performance metrics are given, but implies successful completion). |
| Biocompatibility | Patient-contacting components verified with Irritation, Sensitization, and Cytotoxicity testing per ISO 10993-5:2009 and ISO 10993-10:2010 for a prolonged time period. (Identical to predicate device). |
| Usability/ Human Factors | Evaluated tasks associated with use of the device. (Implies successful evaluation, no specific outcomes provided). |
| Software Functionality | Updated REMI Mobile software successfully supports portable/wearable ambulatory use by initiating sessions from a primary computing platform (Android tablet) to a portable/wearable computing platform (Wear OS smartwatch). |
| Bench Testing (End-to-End System Performance) | - Able to acquire EEG signals using REMI Sensors and transmit to REMI Mobile software. - REMI Mobile able to transfer EEG data to REMI Cloud.
- Final EEG file format within REMI Cloud is viewable in qualified EEG viewing software.
- System meets its Essential Performance (record digitized EEG data with patient-applied sensors, transfer wirelessly to cloud-based archive) and fulfills system requirements. |
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 specify sample sizes for any of the described tests. It mentions "testing conducted," "accelerated aging," "ship testing," and "human factors/usability testing," but provides no details on the number of units, subjects, or data points involved. Similarly, data provenance (country of origin, retrospective/prospective) is not mentioned.
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)
The document does not describe any study establishing ground truth with expert review for a diagnostic purpose. The device is explicitly stated to "not make any diagnostic conclusion" and is "intended as a physiological signal monitor." Therefore, this question is not applicable in the context of the provided information.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Since no expert-based ground truth establishment or diagnostic performance evaluation is detailed, there is no mention of an adjudication method.
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 MRMC study is mentioned. The device is a physiological signal monitor and does not involve AI assistance for human readers in a diagnostic context.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device itself is a system for acquiring and transmitting EEG data for review by medical professionals on qualified EEG viewing software. It does not perform standalone diagnostic algorithms. Its "Essential Performance" is to record digitized EEG data and transfer it.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Given that the device is a physiological signal monitor and "does not make any diagnostic conclusion," the concept of "ground truth" as typically used for diagnostic or screening devices (e.g., pathology, expert consensus on a disease state) is not applicable here. The ground truth for its performance would be the accuracy of EEG signal acquisition and transmission, which is assessed through bench testing and compliance with electrical standards.
8. The sample size for the training set
The document does not describe any machine learning or AI-based component that would require a "training set." The software updates mentioned are for supporting new hardware (smartwatch) and extending monitoring duration, not for developing new diagnostic algorithms.
9. How the ground truth for the training set was established
Not applicable, as no training set for an AI/ML algorithm is described.
§ 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).