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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

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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

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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.

• 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.

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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

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