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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 System 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.

REMI System uses the REMI Mobile software application that runs on qualified commercial off-the-shelf mobile 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 System does not make any diagnostic conclusion about the subject's condition and is intended as a physiological signal monitor. REMI System is indicated for use with adult and pediatric patients (1+ years).

The REMI System has three major components:

• 1. REMI Sensor A disposable EEG sensor which is placed on the patient's scalp using a conductive REMI Sticker
• 2. 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 device (phone or 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.
• 3. 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.

The provided document is a 510(k) Pre-market Notification Summary for the REMI Remote EEG Monitoring System (K243185). This document details the device's characteristics, indications for use, and the studies conducted to demonstrate its substantial equivalence to a predicate device (REMI Remote EEG Monitoring System, K230933).

Based on the provided information, here's a description of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily relies on comparisons to its own predicate device (K230933) and general performance testing against recognized standards. Specific quantitative acceptance criteria are not explicitly detailed in a table format within this summary, but the general assertion is that the device met all predetermined acceptance criteria derived from the listed tests.

2. Sample Size Used for the Test Set and Data Provenance

• Clinical Performance Test Set (Pediatric Extension):
  • Sample Size: 13 younger pediatric patients (ages 1 to <6 years)
  • Data Provenance: Retrospective review of existing REMI EEG records gathered from NIH-funded studies conducted by Epitel in support of REMI development efforts. Data collected under IRB oversight and registered under NCT03583957. The document states a single pediatric-focused site was involved.
  • Geographic Origin: Not explicitly stated, but NIH funding implies a U.S. origin.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: One
• Qualifications: An "experienced pediatric epileptologist independent of wired EEG and generally separated in time from the actual time of collection by over a year - to ensure appropriate experience, consistency, and minimization of bias."

4. Adjudication Method for the Test Set

• Adjudication Method: None explicitly described beyond a single expert's retrospective review. The expert's review was the ground truth.

5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study

• MRMC Study Done? No. The document describes a retrospective review by a single expert to affirm the clinical value of the EEG records, not a comparative effectiveness study pitting human readers with vs. without AI assistance. The device is a physiological signal monitor, not an AI diagnostic tool in this context.

6. Standalone (Algorithm Only) Performance

• Standalone Performance Done? The "Software Verification Testing" section describes end-to-end testing of the system (acquiring, transmitting, and making data viewable), which is essentially testing the algorithm's ability to process and present the data. It verifies the functionality of the system components and data integrity. However, it's not a standalone diagnostic performance study (e.g., sensitivity/specificity for a given condition), as the device "does not make any diagnostic conclusion about the subject's condition." Its essential performance is to record and transfer EEG data.

7. Type of Ground Truth Used

• For Clinical Performance (Pediatric Extension): Expert consensus/review. The "experienced pediatric epileptologist" reviewed the REMI EEG data to assess its quality and presence of EEG features.
• For Technical Performance (Software, Electrical, etc.): Predetermined engineering specifications, compliance with recognized standards, and functional verification.

8. Sample Size for the Training Set

• The document does not mention a training set or machine learning model being the primary focus of this submission. The device is described as a "physiological signal monitor" that "does not make any diagnostic conclusion." The clinical experience discussed references "318 pediatric patients with a mean REMI Sensor wear of 1.7 days" from NIH-funded studies initially used for "REMI development efforts," which could have implicitly involved some level of data-driven development or refinement, but it's not explicitly framed as a "training set" for a distinct AI algorithm evaluated in this 510(k). The focus of this 510(k) is the extension of the device's indications to a younger pediatric age group and the new hydrocolloid sticker.

9. How the Ground Truth for the Training Set Was Established

• As a training set is not explicitly referred to as part of a machine learning model specific to this 510(k)'s purpose, the method for establishing its ground truth is not detailed. The "REMI development efforts" data (n=318) was collected "under Institutional Review Board oversight." However, for the specific aspect of this 510(k) (pediatric extension), the ground truth for the test set (n=13) was established via retrospective review by an experienced pediatric epileptologist.

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The REMI-AI Rapid Detection Module (REMI-AI RDM) is a seizure detection module which is integrated into the REMI Remote EEG Monitoring System and is only indicated for use within non-ICU (Intensive Care Unit) healthcare settings. REMI-AI RDM has not been validated for and is not indicated for detection of electrographic status epilepticus.

REMI-AI RDM conducts automated analysis of REMI EEG data in near real-time and provides notifications of potential electrographic seizures (events) through the REMI System when seizure prevalence of 10% or greater (indicating seizure activity of at least 30 seconds within a 5-minute rolling window) is detected. When seizure prevalence is displayed, the notification also displays the corresponding event detection confidence. Notifications are intended to be used by qualified clinicians who will exercise professional judgment in their application. Detected events are also annotated in the associated REMI EEG record as an aide to the qualified physician's REMI EEG review.

Delays of up to several minutes may occur between the detection of an event and the generation of an event notification, and are thus not a substitute for real-time monitoring. REMI-AI RDM does not make any diagnostic conclusion about the subject's condition and is intended as a physiological signal monitor. REMI-AI RDM is indicated for use with adult and pediatric patients (6+ years).

REMI-AI RDM conducts automated analysis of EEG data collected by the REMI System in near real-time. REMI-AI RDM provides notifications of the prevalence and confidence of potential electrographic seizures, having a combined prevalence of 10% or greater, which correlates with a duration of at least 30 seconds of activity within a rolling 5 minute window of EEG.

REMI-AI RDM notifications are presented through the REMI Mobile software application, and are intended to be used by qualified clinicians who will exercise professional judgment in their interpretation. Notifications include the prevalence and confidence value for the event and are marked in the associated EEG record in order to assist qualified clinicians in their assessment.

REMI-AI RDM notifications identify when a section of EEG is consistent with seizure characteristics it has been trained to recognize. When a notification is presented, clinical context and facility procedures should inform next steps in patient evaluation and management. REMI-AI RDM does not make any treatment or management recommendations.

Here's a summary of the acceptance criteria and study details for the REMI-AI Rapid Detection Module (REMI-AI RDM), based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size:
  • 22 patient records with 54 consensus-determined electrographic seizures (lasting at least 30 seconds).
  • 22 patient records with no consensus-determined electrographic seizures.
  • Total Validation Sample Size: 44 patient records.
• Data Provenance: The text does not explicitly state the country of origin. It indicates that the data was collected concurrently with standard-of-care 19-channel, full-montage, video-EEG in "Epilepsy Monitoring Units (EMUs) or for up to 3 continuous days during at-home ambulatory EEG monitoring." This suggests the data is retrospective as it was "previously acquired" for validation, but the initial data collection method (prospective/retrospective) for the source may vary.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications

• Number of Experts: 3 independent expert epileptologists for panel review, selected from a panel of 6.
• Qualifications of Experts: Certified by the American Board of Psychiatry and Neurology or certified by the American Board of Clinical Neurophysiology with Special Competency in Epilepsy Monitoring.

4. Adjudication Method for the Test Set

• Adjudication Method: Consensus ground truth was established when at least 2 of the 3 expert epileptologists agreed on the presence or absence of an electrographic seizure event (2+1).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of Human Reader Improvement

• No, an MRMC comparative effectiveness study involving human readers with and without AI assistance was not reported in this document. The study focuses on the standalone performance of the AI algorithm.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, a standalone performance study was done. The "Clinical Validation" section explicitly details the algorithm's performance (Sensitivity and False Alarm Rate) against a clinical reference standard, with no mention of human interaction with the AI output during this performance assessment. Notifications are "intended to be used by qualified clinicians," but the validation itself is on the algorithm's detection capabilities.

7. The Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. The ground truth was established by a panel of 3 independent expert epileptologists reviewing "standard 19+channel wired 10-20 montage EEG records."

8. The Sample Size for the Training Set

• Training Set Sample Size: 117 patient records.
  • 82 patient records with seizures ("Train Sz").
  • 35 patient records without seizures ("Train No-Sz").

9. How the Ground Truth for the Training Set Was Established

• The document states that EEG data was used to "train the REMI-AI RDM algorithm to identify potential electrographic seizure events." While it explicitly describes how the ground truth for the validation data set was established (panel review by experts), it does not explicitly detail the exact method for establishing ground truth for the entire training set. However, given the context of the validation process, it is highly probable that a similar expert review and consensus process would have been used to annotate the training data as well.

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The REMI-AI Discrete Detection Module (REMI-AI DDM) is indicated for the analysis of REMI Remote EEG Monitoring System electroencephalogram (EEG) recordings. REMI-AI DDM is intended to be used by physicians qualified to analyze and interpret EEG who will exercise professional judgment in using the information.

As an aide to the qualified physician's REMI EEG review, REMI-AI DDM marks previously acquired sections of REMI EEG that may correspond to neurological events of interest indicative of potential electrographic seizures lasting at least 10 seconds in duration. REMI-AI DDM is indicated for use with adult and pediatric patients (6+ years).

REMI-AI DDM does not mark REMI EEG records in real time and does not provide any diagnostic conclusion about the patient's condition to the user.

REMI-Al Discrete Detection Module (REMI-AI DDM) is a software as a medical device (SaMD) that automatically identifies and annotates discrete seizure-like events in previously acquired electroencephalography (EEG) traces to aid a qualified physician in their review of REMI EEG records. REMI-AI DDM analyzes previously acquired EEG data from 4-channel recordings obtained from bilateral, bipolar scalp EEG recordings at both the frontal and temporoparietal regions, collected and stored by the REMI Remote EEG Monitoring System. REMI-AI DDM analyzes EEG recordings and detects regions of the data that may correspond to electrographic seizures lasting at least 10 seconds in duration. These regions are annotated in the REMI EEG file as discrete events and are provided to assist in REMI EEG review.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

1. Test Set Sample Size and Data Provenance:

   • Sample Size: 50 patient records (31 with consensus-determined electrographic seizures, 19 with no consensus-determined electrographic seizures). This included 87 consensus-determined electrographic seizures lasting at least 10 seconds.
   • Data Provenance: Not explicitly stated (e.g., country of origin). However, the data was collected from patients wearing REMI wireless EEG sensors alongside standard-of-care 19-channel, full-montage, video-EEG in Epilepsy Monitoring Units (EMUs) or during at-home ambulatory EEG monitoring. The study design suggests retrospective use of this collected data for validation.

2. Number of Experts and Qualifications for Ground Truth (Test Set):

   • Number of Experts: 3 independent expert epileptologists for panel review, selected from a panel of 6.
   • Qualifications: Holding certification by the American Board of Psychiatry and Neurology or certification by the American Board of Clinical Neurophysiology with Special Competency in Epilepsy Monitoring.

3. Adjudication Method for the Test Set:

   • Method: Consensus ground truth was established using 2 out of 3 (2+1, if referring to a single round of review among three) members identifying the presence or absence of an electrographic seizure event from the wired EEG records.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No, a MRMC comparative effectiveness study was not explicitly done or reported in the provided text to show the effect size of human readers improving with AI vs. without AI assistance. The clinical validation focuses on the standalone performance of the AI algorithm.

5. Standalone Performance (Algorithm Only):

   • Yes, a standalone (algorithm only without human-in-the-loop) performance study was done. The reported Event-Level Sensitivity and False Alarm Rate (FAR) are direct measures of the REMI-AI DDM algorithm's performance in identifying electrographic seizures.

6. Type of Ground Truth Used:

   • Expert Consensus: The ground truth for both training and validation was established through panel review by independent expert epileptologists based on standard 19+channel wired 10-20 montage EEG records acquired concurrently with the REMI 4-channel EEG.

7. Training Set Sample Size:

   • 108 patient records (73 seizure patients, 35 non-seizure patients).
   • This included a total of 562 electrographic seizures used for training.

8. How Ground Truth for Training Set Was Established:

   • Similar to the validation set, the ground truth for the training set was established by panel review of standard 19+channel wired 10-20 montage EEG records concurrently acquired with REMI 4-channel EEG. The same criteria of consensus (at least 2 of 3 experts) identifying the presence or absence of an electrographic seizure event would apply, as described in the "Clinical Reference" section for generating a reference standard for REMI-AI DDM.

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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:

1. REMI Sensor A disposable EEG sensor which is placed on the patient's scalp using a conductive REMI Sticker
2. 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,
3. 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.

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.

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The REMI Platform is intended to be used in healthcare settings where near real-time and/or remote EEG is warranted. REMI consists of Epilog disposable Sensors - a single patient, disposable, wearable sensor intended to amplify, capture, and wirelessly transmit a single channel of electrical activity of the brain for up to 48 hours. The REM-Mobile software and REMI-Tablet are intended to receive and transmit data from four Epilog Sensors to secure cloud storage for subsequent viewing and reviewing of EEG on third-party software.

REMI does not make any diagnosis or recommendations and is intended only as a physiological signal monitor. Epilog Sensors are intended for use by trained medical professional healthcare facility environment.

Epilog Sensors are intended for use with adult and pediatric patients (6+). (Rx only).

REMI amplifies the electroencephalogram (EEG) from a patient's scalp. After amplification, the EEG are sent to the REMI-Tablet running the REMI-Mobile Application. REMI-Mobile combines the EEG from four Epilog Sensors and patient information and relays the data to a cloud server running Persyst software. The EEG data is accessible through the Persyst Mobile interface. REMI is designed for use with adult and pediatric patients (6+). The user interface for the REMI-Tablet is an 10" LCD touchscreen display.

The user interface for Epilog Sensors is a single button kevpad overmolded in each Sensor, REMI-Tablet power is through A/C adapter as well as limited onboard rechargeable battery. Epilog Sensor power is through a single-use primary coin cell. Using its wireless link, the Epilog Sensors can exchange EEG data and commands with the REMI-Mobile application running on the REMI-Tablet.

REMI has three major components:

1. Epilog-D disposable EEG sensors,

2. REMI-Mobile - mobile OS application designed to run on a medical-grade tablet, acquire EEG data transmitted from Epilog devices along with user-entered patient and device information, and then transmit the EEG data and patient/device information via wireless encrypted WiFi to.

3. REMI-Cloud – A HIPAA-compliant cloud storage and data processing platform where data is processed into a format that a FDA-cleared (K171184) EEG reviewing software called Persyst can use, which will allow remote neurological review.

The firm Epitel, Inc. did not conduct a clinical study to prove the device meets acceptance criteria. Instead, they performed non-clinical performance testing and biocompatibility testing. The device is a physiological signal monitor and does not make diagnoses or recommendations. Therefore, the information provided below is a summary of the non-clinical and biocompatibility tests performed.

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This section is not applicable as no clinical study was conducted. The performance data is based on non-clinical and biocompatibility testing. The document does not specify general sample sizes (e.g., number of devices tested) for the non-clinical or biocompatibility tests, nor does it specify data provenance in terms applicable to clinical studies.

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 section is not applicable as no clinical study was conducted that required ground truth established by experts.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This section is not applicable as no clinical study was conducted.

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

This section is not applicable as no clinical study, particularly an MRMC study, was conducted. The device is a physiological signal monitor and does not include AI for interpretation or diagnosis.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

This section is not applicable as the device is a physiological signal monitor and does not involve a standalone algorithm for diagnostic performance. The submission explicitly states, "REMI does not make any diagnosis or recommendations and is intended only as a physiological signal monitor."

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

This section is not applicable as no clinical study requiring a ground truth was performed. The non-clinical and biocompatibility testing rely on established standards and laboratory results.

8. The sample size for the training set

This section is not applicable, as no machine learning algorithm requiring a training set for diagnostic or interpretative purposes was mentioned or evaluated in the context of device performance.

9. How the ground truth for the training set was established

This section is not applicable, as no machine learning algorithm requiring a training set was mentioned or evaluated.

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