Reviewer Manual

Reviewer Manual for REMI Vigilenz AI for Event Detection - Rev 5
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Table of Contents
Safety Information
Indications for Use

3
3

Contraindications

3

Operator Profile
Explanation of Signal Word Consequences
Warnings333Precautions

4

Section 1- REMI Vigilenz AI for Event Detection Overview

5

Clinical Study

5

Clinical Reference

5

Training and Clinical Reference Data Overview

6

Product Performance

7

Section 2 - Authorized Predetermined Change Control Plan (Authorized PCCP)

9

Section 3 - Operating Instructions

10

Viewing REMI Vigilenz AI for Event Detection Outputs

10

Event Confidence Labels

13High Sensitivity Detection

13

Event Confidence Assessment
Event Confidence Level Annotations
Troubleshooting
Frequently Asked Questions
System Maintenance and Care
Server Maintenance

13
13
15
15
15
15

Software Updates

15

Software Bill of Materials (SBOM)

15Legal and Regulatory Information

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Safety Information
Please read, understand, and follow all safety information contained in these instructions prior
to using REMI Vigilenz™ AI for Event Detection. Retain these instructions for future reference.

Indications for Use
REMI Vigilenz AI for Event Detection is indicated for the analysis of REMI Remote EEG
Monitoring System electroencephalogram (EEG) recordings. REMI Vigilenz AI for Event
Detection 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 Vigilenz AI for Event Detection
marks previously acquired sections of REMI EEG that may correspond to neurological eventsof interest indicative of potential electrographic seizures lasting at least 10 seconds in duration.
REMI Vigilenz AI for Event Detection is indicated for use with adult and pediatric patients (6+years).
REMI Vigilenz AI for Event Detection does not mark REMI EEG records in real time and does
not provide any diagnostic conclusion about the patient's condition to the user.

Contraindications●

REMI Vigilenz AI for Event Detection is not for use with EEG records gathered by standard
wired montages and should only be used with EEG data gathered by the REMI Remote
EEG Monitoring System.

Operator Profile
REMI Vigilenz AI for Event Detection operates using previously acquired EEG traces collectedby the REMI™ Remote EEG Monitoring System, and can assist a qualified physician (i.e. aphysician qualified to analyze and interpret EEG who will exercise professional judgment inusing the information) in the review of REMI EEG records.

Explanation of Signal Word Consequences
Signal WordWARNING
PRECAUTION /CAUTION
IMPORTANT

Consequence
Indicates a hazardous situation, which, if not avoided, could result in major
injury and/or death.
Indicates a hazardous situation, which, if not avoided, could result in minor
injury and/or property damage.
Indicates a special item of note that the user must be aware of for the system
to work properly.

Warnings●●

REMI Vigilenz AI for Event Detection is not meant to be used as a standalone means ofidentifying seizure-like events in a REMI EEG record or of diagnosing a detected potential
event as a true seizure. Qualified physician oversight is required.
REMI Vigilenz AI for Event Detection has not been trained or validated on the records ofany children under 6 years of age.
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Precautions●●●

Caution: Federal (USA) law restricts this device to sale by or on the order of a physician.
If REMI EEG traces are incompatible or incomplete, REMI Vigilenz AI for Event Detection
may not provide any output.
REMI Vigilenz AI for Event Detection is trained to detect only discrete electrographic
seizures lasting a minimum of ten seconds. The following seizure modalities are notincluded in REMI Vigilenz AI for Event Detection training: continuous seizures (e.g., statusepilepticus), seizure clusters, very short duration seizures (e.g., single myoclonic seizures).
While patients experiencing these seizure types may still be appropriate candidates foruse of the REMI Remote EEG Monitoring System and REMI Vigilenz AI for Event
Detection, these seizure types need to be confirmed independently.

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Section 1- REMI Vigilenz AI for Event Detection Overview
REMI Vigilenz AI for Event Detection will analyze previously collected 4-channel data frompatients equipped with the REMI Remote EEG Monitoring System for identification of discrete
electrographic seizure events lasting ≥10 seconds. REMI Vigilenz AI for Event Detection will
annotate these neurological events of interest within a compatible REMI EEG file andelectronically annotate the start/end points of that event in such a way that it is visible to thephysicians qualified to analyze and interpret EEG when viewed using a qualified EEG viewing
software.
For additional information on operating REMI Remote EEG Monitoring System, please refer tothe Clinician Manual for Ambulatory Use.

Clinical Study
EEG data from adult and pediatric patients was used to 1) train the REMI Vigilenz AI for Event
Detection to identify potential electrographic seizure events in a broad patient population, and2) validate the REMI Vigilenz AI for Event Detection’s 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-montage, 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 Vigilenz AI for Event Detection validation data set consisted of 31 patient records
with 87 consensus-determined electrographic seizures lasting at least 10 seconds in duration,and 19 patient records with no consensus-determined electrographic seizures, for a total
validation sample size of 50. 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

Clinical Reference
EEG data used to generate a reference standard for REMI Vigilenz AI for Event Detection wascollected 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 panelof 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 seizures and 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.
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Training and Clinical Reference Data Overview
Demographics by age are presented in Table 1 below.
Table 1. Demographics By Age. “Train” is the set of patient records used to train the algorithm,“Test” is the set of patient records used in this validation analysis. (Sz: Seizure patients; No-Sz:
Non-seizure patients)
Age

Train

Pediatric (≤21) 43 (40%)

Train Sz

Train No-Sz

Test

Test Sz

Test No-Sz

31 (42%)

12 (34%)24 (48%)13 (42%)11 (58%)Adult (22+)

65 (60%)42 (58%)23 (66%)26 (52%)18 (58%)8 (42%)

Total

108

73

35

50

31

19

Demographics by gender are presented in Table 2 below.
Table 2. Demographics By Sex. “Train” is the set of patient records used to train the algorithm,“Test” is the set of patient records used in this validation analysis. (Sz: Seizure patients; No-Sz:
Non-seizure patients)
Sex

Train

Train Sz

Train No-Sz

Test

Test Sz

Test No-Sz

Male

46 (43%)32 (44%)14 (40%)26 (52%)17 (55%)9 (47%)

Female62 (57%)41 (56%)21 (60%)24 (48%)14 (45%)10 (53%)

Total

108

73

35

50

31

19

Demographics by EEG monitoring environment are presented in Table 3 below.
Table 3. Demographics By Monitoring Environment. “Train” is the set of patient records used totrain the algorithm, “Test” is the set of patient records used in this validation analysis. (EMU:
Epilepsy Monitoring Unit environment; Ambulatory: Ambulatory environment; Sz: Seizure patients;
No-Sz: Non-seizure patients)
Environment

Train

Train Sz

Train No-Sz

Test

Test Sz

Test No-Sz

EMU

85 (79%)59 (81%)26 (74%)38 (76%)27 (87%)

11 (58%)Ambulatory

23 (21%)14 (19%)9 (26%)

12 (24%)4 (13%)

8 (42%)

Total

108

73

35

50

31

19

A summary of electrographic seizure types included in REMI Vigilenz AI for Event Detection
training and validation is presented in Table 4 below.
Table 4. Electrographic Seizure Type Count. “Train” is the set of patient records used to train thealgorithm, “Test” is the set of patient records used in this validation analysis.
Seizure Type

Train

Test

Focal

254 (45%)

22 (25%)

Focal Evolving To Generalized

39 (7%)

35 (40%)Generalized

269 (48%)

30 (34%)

Total

562

87

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A summary of the duration of seizure record data, broken down by electrographic seizure type,is presented in Table 5 below.
Table 5. Duration of Seizures by Electrographic Seizure Type. “Train” is the set of patient records
used to train the algorithm, “Test” is the set of patient records used in this validation analysis.
Focal

Focal Evolving To Generalized

Generalized

Duration (s)

Train

Test

Train

Test

Train

Test

<10

9 (4%)

0 (0%)

0 (0%)

0 (0%)

76 (28%)0 (0%)

10-20

51 (20%)4 (18%)

0 (0%)

0 (0%)

100 (37%)

14 (47%)21-40

80 (31%)

1 (5%)

0 (0%)

0 (0%)

40 (15%)7 (23%)

41-60

45 (18%)7 (32%)

5 (13%)

2 (6%)

43 (16%)2 (7%)

61-80

25 (10%)7 (32%)

6 (15%)

6 (17%)

6 (2%)

3 (10%)

81-10015 (6%)

1 (5%)

10 (26%)5 (14%)

3 (1%)

2 (7%)

101-120

9 (4%)

1 (5%)

3 (8%)

10 (29%)1 (0%)

1 (3%)

121+

20 (8%)

1 (5%)

15 (38%)12 (34%)0 (0%)

1 (3%)

Total

254

22

39

35

269

30

Product Performance
REMI Vigilenz AI for Event Detection was evaluated through validation against a combined
primary endpoint of Sensitivity > 70% and of a False Alarm Rate (FAR) < 0.35 False Positives(FP)/hr. REMI Vigilenz AI for Event Detection clinical validation testing demonstrated that REMI
Vigilenz AI for Event Detection achieved Event-Level Sensitivity > 70% (with a calculated 95%
Confidence Interval (CI) lower bound of 79.5%) and FAR < 0.35 FP/hr (with a calculated CI
upper bound of 0.221 FP/hr).
Across all 31 patients with seizures, the event-level Sensitivity was 86.2%. The Mean
Per-Patient Sensitivity was determined to be 92.2%, with a 95% CI Lower Bound of 86.5%,
and ranged between 50% to 100%. Per-Patient Sensitivity was 100% for 23 of the 31 patients.
At least one known event was detected for all 31 patients with seizures.
Across all 50 patients, the event-level FAR was 0.162 FP/hr (with 415 FP for 2,562.5 hours ofdata). The Mean Per-Patient FAR was determined to be 0.176 FP/hr, with a 95% CI Upper
Bound of 0.230, and ranged between 0 to 0.929 FP/hr. There were 17 patients with FARs ≤0.08 FP/hr, 8 patients with no more than one FP (including 4 non-seizure patients), and 4
patients with no FP, including 2 non-seizure patients.

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Sensitivity by age group and FAR by age group are presented in Table 6 below.
Table 6. Sensitivity and False Alarm Rate by Age Group (pediatric vs. adult)
Parameter

Pediatric (6-21 years)

Adult (22+ years)

Sensitivity
Subjects with Seizuresn = 13n = 18

Event-level Sensitivity
95% Confidence Interval

83.0%
73.1, 93.3

90.0%
81.5, 100.0

Subject-level Sensitivity
95% Confidence Interval
False Alarm Rate (FAR)
Total Subjects

87.8%
77.0, 97.0

95.5%
90.0, 100.0

n = 24n = 26

Event-level FAR
95% Confidence Interval

0.227 FP/hr
0.131, 0.335

0.131 FP/hr
0.085, 0.197

Subject-level FAR
95% Confidence Interval

0.223 FP/hr
0.146, 0.316

0.132 FP/hr
0.088, 0.190

Sensitivity by monitoring environment and FAR by monitoring environment are presented inTable 7 below.
Table 7. Sensitivity and False Alarm Rate by Monitoring Environment (EMU vs. Ambulatoryenvironment). (EMU: Epilepsy Monitoring Unit environment; Ambulatory: Ambulatory environment)
Parameter

EMU

Ambulatory

Sensitivity
Subjects with Seizuresn = 27n=4

Event-level Sensitivity
95% Confidence Interval

87.5%
80.0, 94.4

80.0%
71.0, 100.0

Subject-level Sensitivity
95% Confidence Interval
False Alarm Rate (FAR)
Total Subjects

92.2%
85.9, 97.3

92.5%
77.5, 100.0

n = 38n = 12

Event-level FAR
95% Confidence Interval

0.136 FP/hr
0.089, 0.194

0.290 FP/hr
0.170, 0.434

Subject-level FAR
95% Confidence Interval

0.138 FP/hr
0.096, 0.187

0.294 FP/hr
0.184, 0.440

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Section 2 - Authorized Predetermined Change Control Plan
(Authorized PCCP)REMI Vigilenz AI for Event Detection has been cleared by the US FDA with an Authorized
PCCP. The REMI Vigilenz AI for Event Detection 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 Vigilenz AI for Event Detection’s data management practices (i.e., how data iscollected, annotated, curated, stored, retained, controlled, and used), re-training
practices, how and when its performance is evaluated.
The Authorized PCCP also defines validation requirements for all algorithm updates. Prior
to release, modifications are validated through testing against a previously establishedvalidation data set as well as an updated validation data set. Updates to REMI Vigilenz AI
for Event Detection will be implemented per the Authorized PCCP and through theSoftware Update process (described below), and will be communicated through updates
to this document.

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Section 3 - Operating Instructions
REMI Vigilenz AI for Event Detection, if enabled, will analyze previously acquired REMI EEG
traces. REMI Vigilenz AI for Event Detection can assist qualified physicians in their review of
REMI EEG records by highlighting sections of REMI EEG that may correspond to neurologicalevents of interest indicative of potential electrographic seizures lasting at least 10 seconds induration, which qualified physicians can then review for relevance.

Viewing REMI Vigilenz AI for Event Detection Outputs
Once analysis is complete, REMI Vigilenz AI for Event Detection annotations are appended to
the REMI EEG record file. The full REMI EEG record remains available to give context to thesections marked by REMI Vigilenz AI for Event Detection and to permit identification of otherpotential electrographic seizure events within the broader record. Qualified physicians using
REMI Vigilenz AI for Event Detection may determine whether detected potential events
constitute electrographic seizures and may assess how this information impacts patient
diagnosis and care plans.
Annotations are accessible on both the Persyst Mobile Patient Views screen and via theComments button within a Persyst Mobile EEG review screen. See Figures 1 and 2 to view how
to access REMI Vigilenz AI for Event Detection outputs within Persyst Mobile.
Note that annotation duration is limited to the initial 5 minutes of any detected event, so any
detected electrographic event lasting longer than 5 minutes will only be annotated across thefirst 5 minutes of detection.
Note that the first annotation of every REMI EEG trace that has been analyzed by REMI Vigilenz
AI for Event Detection identifies that the EEG record was analyzed by REMI Vigilenz AI for
Event Detection and provides its Unique Device Identifier (UDI) along with the duration of data
analyzed.

Figure 1: REMI Vigilenz AI for Event Detection Outputs as displayed in Persyst Mobile (Patient View)
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Figure 2: REMI Vigilenz AI for Event Detection Outputs as displayed in Persyst Mobile (within record
comments)

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Annotations of discrete electrographic seizure events detected by REMI Vigilenz AI for Event
Detection are also visible at the base of the Persyst Mobile EEG review screen in the“Comment” row. See Figure 3 to view how REMI Vigilenz AI for Event Detection outputs are
displayed in Persyst Mobile.

Figure 3: REMI Vigilenz AI for Event Detection Outputs as displayed in Persyst Mobile (within record
annotations)

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Event Confidence Labels
REMI Vigilenz AI for Event Detection has been designed to process REMI EEG records toidentify potential discrete electrographic seizure events within the recording. REMI Vigilenz AI
for Event Detection is designed to maximize the number of potential events that may
correspond to true seizure events, but also to report its confidence that a detected potential
event represents a true positive electrographic seizure event.
Review of the REMI EEG record by a qualified physician should still be conducted even after
REMI Vigilenz AI for Event Detection processing.
WARNING: REMI Vigilenz AI for Event Detection is not meant to be used as a standalone
means of identifying seizure-like events in a REMI EEG record or of diagnosing a detected
potential event as a true seizure. Qualified physician oversight is required.
High Sensitivity Detection
Event detection occurs based on the Event Detection Model (which is represented by the gold
circle in Figure 4). The Event Detection Model optimizes high Sensitivity1 at an acceptable
FAR2. All events identified by the Event Detection Model are annotated to the REMI EEG
record.
Event Confidence Assessment
Event Confidence is determined for each event detected by the Event Detection Model throughthe use of additional Confidence Models that were created and evaluated during algorithm
training (all gray to black markers in Figure 4). The Confidence of these models are determinedby their performance on training data3. The increase in Confidence occurs because althoughfewer total events are found (and thus lower Sensitivity), the ratio of true positive to false
positive events is higher.

Figure 4: Event and Confidence Models of REMI Vigilenz AI for Event Detection.
1

Sensitivity = True Positives / Total Ground Truth Events
False Alarm Rate = False Positives / Duration of EEG data in hours
3
Confidence = True Positives / (True Positives + False Positives)
2

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Event Confidence Level Annotations
Upon processing through REMI Vigilenz AI for Event Detection, annotations are appended to
the REMI EEG record that include the detected potential discrete electrographic seizure events
along with a corresponding confidence level and range.
The ranges of each confidence level are described in Table 8 below:
Table 8. REMI Vigilenz AI for Event Detection Confidence Level Descriptions.

Confidence Level

Description

Very High (95-100%)

REMI Vigilenz AI for Event Detection is ≥95% confident that the identified
event is not a FP, based on training data.

High (80-94%)

REMI Vigilenz AI for Event Detection is 80-94% confident that the identified
event is not a FP based on training data.

Moderate (50-79%)

REMI Vigilenz AI for Event Detection is 50-79% confident that the identified
event is not a FP based on training data.

Low (0-49%)

REMI Vigilenz AI for Event Detection is <50% confident that the identified
event is not a FP based on training data.

Examples: 1) If a REMI Vigilenz AI for Event Detection detected event is found by a model with75% confidence but not by any model with greater than 80% confidence, theconfidence would be determined as “Moderate” and the text “Moderate Confidence(50-79%)” would be included in the annotation for that event.
2) If a REMI Vigilenz AI for Event Detection detected event is found by a model with
94% Confidence but not by any model with 95% or greater confidence, theConfidence would be determined as “High” and the text “High Confidence(80-94%)” would be included in the annotation for that event.

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Troubleshooting
Frequently Asked Questions
REMI Vigilenz AI for Event Detection annotations were expected but are not available –REMI Vigilenz AI for Event Detection analyzes previously collected REMI EEG traces. Additionaltime may be required before REMI Vigilenz AI for Event Detection analysis is complete. Ifannotations are still not available after one day, contact your Epitel representative to ensurethat REMI Vigilenz AI for Event Detection is enabled and functional.
I cannot find REMI Vigilenz AI for Event Detection annotations on Persyst Mobile –Annotations are accessible within Persyst Mobile on both the Patient Views screen and thecorresponding REMI EEG recording. If you cannot find these screens, consult the Persyst
Mobile Instructions for Use or contact your Epitel representative.

System Maintenance and Care
Server Maintenance
REMI Vigilenz AI for Event Detection operates on a virtual server within Epitel’s Cloud
Environment. Epitel’s Cloud Environment runs on the Amazon Web Services (AWS) cloud
platform and follows AWS best practices for HIPAA security and compliance, including
end-point protections and limited/secured user access.
In the event that server maintenance is required that would impact REMI Vigilenz AI for Event
Detection, Epitel will communicate with hospital or physician practice IT administrators to notify
them of the maintenance and of any expected impacts.
Software Updates
All software updates will be managed and deployed by Epitel within Epitel’s Cloud Environment
without the need for end user intervention. Epitel will notify customers of software updates andof any changes they may experience, and these changes will be described in release notes
viewable on the Epitel website.
Software Bill of Materials (SBOM)
Access to a current machine-readable SBOM is made available to customers upon request.

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Legal and Regulatory Information
This document was, as far as possible, accurate at the time of release, though subsequentchanges may have been made. Epitel reserves the right to alter specifications and details as
required. Late-breaking information may be supplied separately for completeness.
Product
REMI Vigilenz AI for Event Detection
Manufacturer:
Epitel, Inc.
465 S. 400 E., Suite 250
Salt Lake City, UT 84111
Users should contact Epitel for assistance with setting up or using REMI Vigilenz AI For Event
Detection, or to report unexpected operations or events. For support contact Epitel at any of
the following:
Phone: (801) 497-6297
Email: support@epitel.com
Website: www.epitel.com

For Patent information, visit www.epitel.com/patents.
Copyright © Epitel, Inc. All rights reserved. Epitel, REMI, and REMI Vigilenz are trademarks of
Epitel, Inc. All other trademarks are the property of their respective owners. No part of thisdocument may be reproduced by any means without the prior written permission of Epitel, Inc.
LB-0021 Rev 5 Rel: 08/2024

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