(251 days)
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
| Acceptance Criteria | Target | Reported Device Performance |
|---|---|---|
| Event-Level Sensitivity | > 70% | > 70% (95% Cl lower bound of 78.9%) |
| False Alarm Rate (FAR) | < 0.446 False Positives (FP)/hr | < 0.35 FP/hr (95% Cl upper bound of 0.164 FP/hr) |
| Patient-level Sensitivity | Not explicitly stated (implied high) | 92.5% (95% Cl Lower Bound of 84.8%) |
| Subject-level FAR | Not explicitly stated (implied low) | 0.117 FP/hr (95% Cl Upper Bound of 0.176) |
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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October 17, 2024
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Epitel, Inc Christopher Phillips VP. Regulatory Affairs and Ouality 465 S 400 E Suite 250 Salt Lake City, Utah 84111
Re: K240408
Trade/Device Name: REMI-AI Rapid Detection Module (REMI-AI RDM) Regulation Number: 21 CFR 882.1400 Regulation Name: Electroencephalograph Regulatory Class: Class II Product Code: OMB Dated: September 16, 2024 Received: September 17, 2024
Dear Christopher Phillips:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
FDA's substantial equivalence determination also included the review and clearance of your Predetermined Change Control Plan (PCCP). Under section 515C(b)(1) of the Act, a new premarket notification is not
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required for a change to a device cleared under section 510(k) of the Act, if such change is consistent with an established PCCP granted pursuant to section 515C(b)(2) of the Act. Under 21 CFR 807.81(a)(3), a new premarket notification is required if there is a major change or modification in the intended use of a device, or if there is a change or modification in a device that could significantly affect the safety or effectiveness of the device, e.g., a significant change or modification in design, material, chemical composition, energy source, or manufacturing process. Accordingly, if deviations from the established PCCP result in a major change or modification in the intended use of the device, or result in a change or modification in the device that could significantly affect the safety or effectiveness of the a new premarket notification would be required consistent with section 515C(b)(1) of the Act and 21 CFR 807.81(a)(3). Failure to submit such a premarket submission would constitute adulteration and misbranding under sections 501(f)(1)(B) and 502(o) of the Act, respectively.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device" (https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-device-advicecomprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
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Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,
Jay R. Gupta -S
Jay Gupta Assistant Director DHT5A: Division of Neurosurgical, Neurointerventional, and Neurodiagnostic Devices OHT5: Office of Neurological and Physical Medicine Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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Indications for Use
Submission Number (if known)
Device Name
REMI-AI Rapid Detection Module (REMI-AI RDM)
Indications for Use (Describe)
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).
Type of Use (select one or both as applicable)
| ☑ Prescription Use (21 CFR 801 Subpart D) | |
|---|---|
| -- | -------------------------------------------------------- |
ver-The-Counter Use (21 CFR 801 Subpart C)
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REMI-AI Rapid Detection Module (REMI-AI RDM) 510(k) SUMMARY
1. Applicant Information
Epitel, Inc. 465 S 400 E, Suite 250 Salt Lake City, UT 84111
Contact Information
Christopher M. Phillips VP, Regulatory Affairs and Quality
Date Prepared
October 9, 2024
2. Subject Device Information
Name of Device: REMI-AI Rapid Detection Module (REMI-AI RDM) Common or Usual Name: EEG System Classification Name: Automatic Event Detection Software For Full-Montage Electroencephalograph Regulatory Class: Class II Product Code and Regulation Number: OMB - Sec. 882.1400
3. Predicate Devices
| Predicate Type | 510(k) Number | Name of Device | Name of Manufacturer |
|---|---|---|---|
| Primary Predicate | K191301 | Ceribell Pocket EEGDevice(Seizure Detection Module) | Ceribell, Inc. |
| Secondary Predicate | K231779 | REMI-AI Discrete DetectionModule (RDM) | Epitel, Inc. |
4. Device Description
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.
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Delays of up to several minutes may occur between the start of an event, the detection of an event and the generation of an event notification, and are thus not a substitute for real-time monitoring.
5. Indications for Use
The REMI-AI Rapid Detection Module (REMI-AI RDM) is a seizure detection module which is intecrated 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-Al 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).
6. Predicate Selection
In alignment with FDA Draft Guidance Best Practices for Selecting a Predicate Device to Support a Premarket Notification [510(k)] Submission - Draft Guidance for Industry and Food and Drug Administration Staff, appropriate predicate devices have been selected. Potential predicates were reviewed and the Ceribell Pocket EEG Seizure Detection module primary predicate device was selected as it meets or exceeds the expected safety and performance. does not have unmitigated user-related or design related safety issues, and is not associated with any design-related recalls. A secondary predicate was selected in the REMI-Al Discrete Detection Module as a secondary predicate in order to support some technological characteristics. The secondary predicate also meets or exceeds the expected safety and performance, does not have unmitigated user-related or design related safety issues, and is not associated with any design-related recalls.
7. Substantial Equivalence
REMI-AI RDM has been developed in compliance with applicable FDA requirements and guidance as well as with recognized standards. This submission includes required documentation and testing data demonstrating substantial equivalence of REMI-AI RDM to its predicate device. REMI-AI RDM has undergone Software Testing, Human Factors/Usability testing, and Clinical Validation which demonstrate that it is safe and effective for its intended use. Assessment of the technological characteristics, intended use, and conclusions drawn from the verification tests, presented in their respective sections of this submission, demonstrate that the device is as safe and effective as the leqally marketed predicate devices.
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7.1 Summary of Technological Characteristics and Substantial Equivalence to Predicate Devices
| Attribute | Subject DeviceREMI-AI Rapid Detection Module | Primary Predicate DeviceCeribell Pocket EEG Device(K191301) | Secondary Predicate DeviceREMI-Al Discrete Detection Module(K231779) |
|---|---|---|---|
| Classification andRegulation | Class II per 21 CFR 882.1400Electroencephalograph | Class II per 21 CFR 882.1400Electroencephalograph | Class II per 21 CFR 882.1400Electroencephalograph |
| FDA ProductCode(s) | OMB - Automatic Event DetectionSoftware | OMB - Automatic Event DetectionSoftwareOMC - Reduced Montage SystemGWQ - Full Montage SystemGXY - Electrodes | OMB - Automatic Event DetectionSoftware |
| Intended Use | Analysis of EEG signal data fordetection of seizure events | Analysis of EEG signal data fordetection of seizure events | Analysis of EEG signal data fordetection of seizure events |
| Indications for Use | The REMI-AI Rapid DetectionModule (REMI-AI RDM) is a seizuredetection module which isintegrated into the REMI RemoteEEG Monitoring System and is onlyindicated for use within non-ICU(Intensive Care Unit) healthcaresettings. REMI-AI RDM has notbeen validated for and is notindicated for detection ofelectrographic status epilepticus.REMI-AI RDM conducts automatedanalysis of REMI EEG data in nearreal-time and provides notificationsof potential electrographic seizures(events) through the REMI Systemwhen seizure prevalence of 10% orgreater (indicating seizure activity ofat least 30 seconds within a 5-minute rolling window) is detected.When seizure prevalence isdisplayed, the notification alsodisplays the corresponding eventdetection confidence. Notificationsare intended to be used by qualifiedclinicians who will exerciseprofessional judgment in theirapplication. Detected events arealso annotated in the associatedREMI EEG record as an aide to thequalified physician's REMI EEGreview.Delays of up to severalminutes may occur between thedetection of an event and thegeneration of an event notification,and are thus not a substitute forreal-time monitoring. REMI-AI RDMdoes not make any | The Ceribell Pocket EEG Device isintended to record and store EEGsignals, and to present the EEGsignals in visual and audibleformats in real time. The visual andaudible signals assist trainedmedical staff to make neurologicaldiagnoses. The Pocket EEG Deviceis intended to be used in aprofessional healthcare facilityenvironment.Additionally, the EEG RecordingViewer Software component of thePocket EEG Device incorporates aSeizure Detection component thatis intended to mark previouslyacquired sections of EEGrecordings in patients greater thanor equal to 18 years of age thatmay correspond to electrographicseizures in order to assist qualifiedclinical practitioners in theassessment of EEG traces. TheSeizure Detection componentprovides notifications to the userwhen detected seizure prevalenceis "Frequent," "Abundant," or"Continuous," per the definitions ofthe American ClinicalNeurophysiology Society Guideline14. Notifications include anon-screen display on the PocketEEG Device and the optionalsending of an e-mail message to aclinician. Delays of up to severalminutes can occur between thebeginning of a seizure and whenthe Seizure Detection notifications | The REMI-AI Discrete DetectionModule (REMI-AI DDM) is indicatedfor the analysis of REMIRemote EEG Monitoring Systemelectroencephalogram (EEG)recordings.REMI-AI DDM isintended to be used by physiciansqualified to analyze and interpretEEG who will exercise professionaljudgment in using the information.As an aide to the qualifiedphysician's REMI EEG review,REMI-AI DDM marks previouslyacquired sections of REMI EEGthat may correspond to neurologicalevents of interest indicative ofpotential electrographic seizureslasting at least 10 seconds induration. REMI-AI DDM is indicatedfor use with adult and pediatricpatients (6+ years).REMI-AI DDM does not mark REMIEEG records in real time and doesnot provide any diagnosticconclusion about the patient'scondition to the user. |
| Attribute | Subject DeviceREMI-AI Rapid Detection Module | Primary Predicate DeviceCeribell Pocket EEG Device(K191301) | Secondary Predicate DeviceREMI-AI Discrete Detection Module(K231779) |
| diagnostic conclusion about thesubject's condition and is intendedas a physiological signal monitor.REMI-AI RDM is indicated for usewith adult and pediatric patients (6+years). | will be shown to a user.The Pocket EEG Device does notprovide any diagnostic conclusionabout the subject's condition andSeizure Detection notificationscannot be used as a substitute forreal time monitoring of theunderlying EEG by a trained expert. | ||
| Seizure eventdetection module | REMI-AI RDM is a seizuredetection module that analyzes thelast 5 minutes of EEG in a rollingwindow and identifies seizureevents. | The Ceribell Pocket EEG seizuredetection module analyzes the last5 minutes of EEG in a rollingwindow and identifies seizureevents. | REMI-AI DDM detectselectrographic events in previouslyacquired EEG data. |
| Seizure eventnotifications | When a seizure event is detected, anotification is generated. Thesenotifications are provided to theREMI System (K230933) forinteroperable display on the REMIMobile software.Notifications include identifying anevent that has been detected andprovides the prevalence values andalgorithm confidence.Event notifications are alsoannotated in the EEG record. | When a seizure event is detected, anotification is generated. TheSeizure Detection module alsoprovides notifications of detectedseizure by displaying an on-screenmessage on the Ceribell EEGRecorder and the optional sendingof an email message | No notifications are generated |
| Seizureprevalence | Seizure prevalence is calculated forthe 5-minute rolling window.Notifications and annotationsinclude of:• Prevalence values presented asa percentage which can rangefrom 10% to 100%. | Seizure prevalence (defined asseizure burden, or the percentageof time epochs classified asseizure) is calculated for the5-minute rolling window.Notifications and annotationsinclude of:• Frequent seizure detected ifseizure burden is 10% (30seconds or more)• Abundant seizure if greater thanor equal to 50%• Continuous if greater than orequal to 90% | Seizure prevalence is notcalculated.No seizure prevalence notificationsor annotations |
| Event confidence | Confidence is calculated fordetected events. Confidence is ameasure that the event is not afalse positive.Notifications and annotationsincludes of: | Event confidence is not calculated.No event confidence included innotifications or annotations | Confidence is calculated fordetected events. Confidence is ameasure that the event is not afalse positive.No notifications are presented. |
| • Event confidence presented asLow, Moderate, High, or Very | Annotations consist of:• Event confidence presented as | ||
| Attribute | Subject DeviceREMI-AI Rapid Detection Module | Primary Predicate DeviceCeribell Pocket EEG Device(K191301) | Secondary Predicate DeviceREMI-AI Discrete Detection Module(K231779) |
| High (along with correspondingpercentage ranges) | Low, Moderate, High, or VeryHigh (along with correspondingpercentage ranges) | ||
| Time beforenotifications maybe presented | Several minutes | Several minutes | Not applicable |
| Seizure eventreview | REMI-AI RDM will annotate EEGrecords to identify the detectedseizure event to aid in EEG review.These events are provided to theREMI System. | Ceribell EEG Portal includes aseizure detection module that willmark EEG records to identify thedetected seizure event to aid inEEG review. | REMI-AI DDM will annotate EEGrecords to identify the detectedseizure event to aid in EEG review.These events are provided to theREMI System. |
| The REMI System stores EEGrecords in a standard EEG formatfor viewing in a qualified EEGviewing software. | The REMI System stores EEGrecords in a standard EEG formatfor viewing in a qualified EEGviewing software. | ||
| Use environment | The REMI-AI RDM is indicated foruse in non-ICU (Intensive CareUnit) healthcare settings. | Ceribell Pocket EEG is used in aprofessional healthcare facilityenvironment. | The REMI-AI DDM is indicated foruse in healthcare or ambulatorysettings. |
| Software/ systemuser interface | The REMI-AI RDM producesnotifications and annotations thatare provided to the REMI System(K230933) in an interoperable way. | Pocket EEG Device EEGRecording Viewer software | The REMI-AI DDM producesannotations that are provided to theREMI System (K230933) in aninteroperable way. |
| The REMI System was cleared withthe ability to conduct interoperablecommunications for transfer of EEGdata for the purpose of analysismodules and receive the outputs ofany such analysis. | The REMI System was cleared withthe ability to conduct interoperablecommunications for transfer of EEGdata for the purpose of analysismodules and receive the outputs ofany such analysis. | ||
| The REMI System displays thisinformation through the REMIMobile medical application, andprovides the RDM outputs asannotations in the EEG record. | The REMI System provides theDDM outputs as annotations in theEEG record. | ||
| Algorithm inputs | EEG data acquired from REMISensors placed on a patient's scalp.REMI Sensors are a component ofthe REMI Remote EEG MonitoringSystem. | EEG acquired from a Ceribell EEGheadband placed on a patient'sscalp. The sensor band is anothercomponent of the Ceribell PocketEEG system | EEG data acquired from REMISensors placed on a patient's scalp.REMI Sensors are a component ofthe REMI Remote EEG MonitoringSystem. |
| Data format(viewer software) | Common EEG data formats(e.g.lay-dat) viewable in qualified EEGviewing software | Format is not publicly available.Data is viewable in the CeribellEEG Portal viewing software | Common EEG data formats(e.g.lay-dat) viewable in qualified EEGviewing software |
| Clinical validation | Demonstrated through statisticalanalysis of clinical data | Demonstrated through statisticalanalysis of clinical data | Demonstrated through statisticalanalysis of clinical data |
| Attribute | Subject DeviceREMI-AI Rapid Detection Module | Primary Predicate DeviceCeribell Pocket EEG Device(K191301) | Secondary Predicate DeviceREMI-Al Discrete Detection Module(K231779) |
| PredeterminedChange ControlPlan (PCCP) | PCCP included in submission | N/A | Authorized PCCP |
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8. Performance Data
REMI-AI RDM was tested to verify its design and to validate its safe and effective use for the intended population and use environments. Results of this testing, included in this premarket. notification, support a determination of substantial equivalence. Testing included the following:
| Test Type | Summary |
|---|---|
| Software Verification | Software verification testing was conducted to ensure software meetsspecified requirements. Interoperability verification testing was conducted toensure interoperability of REMI-AI RDM with the REMI Remote EEGMonitoring System and to ensure wireless quality of service. |
| Clinical Validation | The algorithm was tested against a clinical reference to ensure it meetsclinical performance requirements (as outlined in Section 11, ClinicalValidation) |
| Human Factors Validation forREMI-AI RDM outputs | REMI-AI RDM EEG notification outputs were evaluated by representativeclinicians reviewers to validate usability |
| REMI-AI RDM EEG annotation outputs were evaluated by representativeepileptologist reviewers to validate usability |
REMI-AI RDM met all predetermined acceptance criteria derived from the above listed tests and demonstrated substantially equivalent performance as compared with the predicate devices.
9. Clinical Study
EEG data from adult and pediatric patients was used to 1) train the REMI-AI RDM algorithm to identify potential electrographic seizure events in a broad patient population, and 2) validate the REMI-AI RDM algorithm ability to identify potential electrographic seizure events within an indicated patient population.
Patients at these sites wore REMI wireless EEG sensors at bilateral frontal and temporoparietal scalp sites alongside standard-of-care 19-channel, full-montaqe, video-EEG for up to 7 continuous days in Epilepsy Monitoring Units (EMUs) or for up to 3 continuous days during at-home ambulatory EEG monitoring.
The REMI-AI RDM validation data set consisted of 22 patient records with 54 consensus-determined electrographic seizures lasting at least 30 seconds in duration, and 22 patient records with no consensus-determined electrographic seizures, for a total validation sample size of 44. All attempts were made to ensure diverse patient demographics. The consensus-determined electrographic seizures represented in the validation data set include:
- Focal Seizures
- Focal Evolving To Generalized Seizures
- Generalized Seizures ●
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10. Clinical Reference
EEG data used to generate a reference standard for REMI-AI RDM was collected from standard 19+channel wired 10-20 montage EEG records acquired concurrently with REMI 4-channel EEG. Prior to inclusion in the validation data set, all patients' EEG records underwent panel review by 3 independent expert epileptologists. Experts consisted of a panel of 6 epileptologists, 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. Consensus ground truth electrographic seizure negative determinations were made using the wired EEG records when at least 2 of 3 members identified the presence or absence of an electrographic seizure event.
Training and Clinical Reference Data Overview
Demographics by age are presented in Table 10.1 below.
| Age | Train | Train Sz | Train No-Sz | Test | Test Sz | Test No-Sz |
|---|---|---|---|---|---|---|
| Child (≤21) | 47 (40%) | 32 (39%) | 15 (43%) | 21 (48%) | 11 (50%) | 10 (45%) |
| Adult (22+) | 70 (60%) | 50 (61%) | 20 (57%) | 23 (52%) | 11 (50%) | 12 (55%) |
| Total | 117 | 82 | 35 | 44 | 22 | 22 |
Table 10.1. Demographics By Age. Train is the set of patient records used to train the algorithm and Test is the set of patient records used in this validation analysis. (Sz: Seizure Patients, No-Sz: Non-Seizure Patients)
| Gender | Train | Train Sz | Train No-Sz | Test | Test Sz | Test No-Sz |
|---|---|---|---|---|---|---|
| Male | 53 (45%) | 40 (49%) | 13 (37%) | 21 (48%) | 11 (50%) | 10 (45%) |
| Female | 64 (55%) | 42 (51%) | 22 (63%) | 23 (52%) | 11 (50%) | 12 (55%) |
| Total | 117 | 82 | 35 | 44 | 22 | 22 |
Demographics by gender are presented in Table 10.2 below.
Table 10.2. Demographics by Gender. Train is the set of patient records used to train the algorithm and Test is the set of patient records used in this validation analysis. (Sz: Seizure Patients, No-Sz: Non-Seizure Patients)
A summary of electrographic seizure types included in REMI-AI RDM training and validation is presented in Table 10.3 below.
| Seizure Type | Train | Test |
|---|---|---|
| Focal | 257 (47%) | 17 (31%) |
| Focal Evolving To Generalized | 45 (8%) | 21 (39%) |
| Generalized | 243 (45%) | 16 (30%) |
| Total | 545 | 54 |
Table 10.3. Electrographic Seizure Type Count. Train is the set of patient records used to train the alqorithm and Test is the set of patient records used in this validation analysis.
A summary of the duration of seizure record data, broken down by electrographic seizure type, is presented in Table 10.4 below.
| Focal | Focal Evolving To Generalized | Generalized | ||||
|---|---|---|---|---|---|---|
| Duration (s) | Train | Test | Train | Test | Train | Test |
| <10 | 8 (3%) | 0 (0%) | 0 (0%) | 0 (0%) | 58 (24%) | 0 (0%) |
| 10-20 | 50 (19%) | 0 (0%) | 0 (0%) | 0 (0%) | 100 (41%) | 0 (0%) |
| 21-40 | 83 (32%) | 0 (0%) | 0 (0%) | 0 (0%) | 31 (13%) | 9 (56%) |
| 41-60 | 47 (18%) | 6 (35%) | 5 (11%) | 0 (0%) | 44 (18%) | 2 (13%) |
| 61-80 | 25 (10%) | 8 (47%) | 6 (13%) | 0 (0%) | 8 (3%) | 1 (6%) |
| 81-100 | 14 (5%) | 2 (12%) | 10 (22%) | 4 (19%) | 1 (0%) | 2 (13%) |
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| 101-120 | 11 (4%) | 1 (6%) | 5 (11%) | 10 (48%) | 1 (0%) | 2 (13%) |
|---|---|---|---|---|---|---|
| 121+ | 19 (7%) | 0 (0%) | 19 (42%) | 7 (33%) | 0 (0%) | 0 (0%) |
| Total | 257 | 17 | 45 | 21 | 243 | 16 |
Table 10.4. Duration of Seizures by Electrographic Seizure Type. Train is the set of patient records used to train the algorithm and Test is the set of patient records used in this validation analysis.
11. Clinical Validation
REMI-AI RDM validation was evaluated against a combined primary endpoint of Sensitivity > 70% and of a False Alarm Rate (FAR) < 0.446 False Positives (FP)/hr. REMI-AI RDM clinical validation testing demonstrated that REMI-AI RDM achieved Event-Level Sensitivity > 70% (with a calculated 95% Cl lower bound of 78.9%) and FAR < 0.35 FP/hr (with a calculated Cl upper bound of 0.164 FP/hr).
Across all 22 patients with seizures, patient-level Sensitivity was 92.5%, with a 95% Cl Lower Bound of 84.8%. Per-patient Sensitivity was 100% for 23 of the 31 patients. At least one known event was detected for all 22 patients with seizures, and every event was detected for 18 of the 22 patients.
Across all 44 patients, the subject-level FAR was 0.117 FP/hr, with a 95% Cl Upper Bound of 0.176, and ranged between 0 to 1.03 FP/hr. There were 22 patients that had no more than one FP (including 6 non-seizure patients), and 13 patients that had no FPs (including 6 non-seizure patients).
Clinical Reference Data Overview
Sensitivity by age group and FAR by age group are presented in Table 11.1 below.
| Parameter | Pediatric (6-21 years) | Adult (22+ years) |
|---|---|---|
| Sensitivity | ||
| Subjects with Seizures | n = 11 | n = 11 |
| Event-level Sensitivity | 91.2% | 85.0% |
| 95% Confidence Interval | 80.0, 100.0 | 68.0, 100.0 |
| Subject-level Sensitivity | 94.1% | 90.9% |
| 95% Confidence Interval | 85.5, 100.0 | 78.8, 100.0 |
| False Alarm Rate (FAR) | ||
| Total Subjects | n = 21 | n = 23 |
| Event-level FAR | 0.162 FP/hr | 0.070 FP/hr |
| 95% Confidence Interval | 0.070, 0.280 | 0.040, 0.099 |
| Subject-level FAR | 0.174 FP/hr | 0.064 FP/hr |
| 95% Confidence Interval | 0.083, 0.289 | 0.037, 0.091 |
Table 11.1. Sensitivity and False Alarm Rate by Age Group (pediatrics vs. adults)
12. Predetermined Change Control Plan (PCCP)
The REMI-AI RDM has been cleared by the US FDA with an Authorized PCCP. The REMI-AI RDM Authorized PCCP outlines authorized modifications intended to improve algorithm performance through expansion of the training data and/or through optimizations of the algorithm. The Authorized PCCP outlines REMI-Al RDM's data management practices (i.e., how data is collected, annotated, stored, retained, controlled, and used), re-training practices, how and when its performance is evaluated.
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The Authorized PCCP also defines validation requirements for all algorithm updates. Prior to release, modifications are validated through testing against a previously established validation data set as well as an updated validation data set. Updates to REMI-AI RDM will be implemented per the Authorized PCCP and through the Software Update process described in the user manual. Epitel will update the user manual following implemented changes and notify customers of software updates and of any changes they may experience, and these changes will be described in release notes viewable on the Epitel website.
13. Substantial Equivalence Conclusion
The REMI-AI Rapid Detection Module (RDM) subject device has the same intended use, similar indications for use and incorporates the same fundamental technology as the legally marketed predicate devices to which it was compared. Based on intended use, technological characteristics, and performance testing, it can be concluded that the subject device, REMI-AI RDM, is substantially equivalent to the identified predicate device.
§ 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).