PreOp

K172858 · Epilog · OLX · Jan 8, 2018 · Neurology

Device Facts

• Record ID: K172858
• Device Name: PreOp
• Applicant: Epilog
• Product Code: OLX · Neurology
• Decision Date: Jan 8, 2018
• Decision: SESE
• Submission Type: Traditional
• Regulation: 21 CFR 882.1400
• Device Class: Class 2
• Attributes: Software as a Medical Device, Pediatric

Intended Use

PreOp is intended for use by a trained/qualified EEG technologist or physician on both adult and pediativ subjects at least 3 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an individualized head model and an individualized MRI image.

Device Story

PreOp is cloud-based medical software for 3D visualization of EEG activity. Inputs: user-uploaded EEG data and MRI scans. Processing: 3D Electrical Source Imaging (ESI) module uses sLORETA (standardized Low Resolution Brain Electromagnetic Tomography) and Finite Difference Method (FDM) for forward head modeling, incorporating individualized anatomical MRI and tissue segmentation (CSF, white matter, air). Outputs: 3D visualization report and interactive 3D viewer accessed via cloud. Used by trained EEG technologists or physicians in clinical settings to assist in localizing brain activity. Output aids clinicians in evaluating brain function; does not provide treatment recommendations or decisive diagnostic information.

Clinical Evidence

Retrospective analysis of 18 epilepsy patients (ages 3-55) who underwent resection surgery (Engel I outcome). Compared source localization accuracy of PreOp (sLORETA/FDM with individualized MRI) vs. predicate (sLORETA/FDM with idealized MRI). Three epileptologists rated concordance of source localization results on a sublobular level; results demonstrated substantial equivalence. A second study compared HD-EEG (128/256 electrodes) vs. LD-EEG (25 electrodes) in 8 patients, finding identical or near-identical source localization in 13/16 spikes.

Technological Characteristics

Software-only device; operates on MS-Windows 7. Uses sLORETA for linear inverse source estimation and Finite Difference Method (FDM) for forward head modeling. Incorporates individualized MRI and segmentation of CSF, white matter, and air. Complies with AAMI/ANSI/IEC 62304 (Software Life Cycle) and AAMI/ANSI/IEC 62366 (Usability).

Indications for Use

Indicated for adult and pediatric patients (≥3 years) requiring visualization of human brain function via fusion of EEG data with individualized head models and MRI images.

Regulatory Classification

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.

Predicate Devices

• GeoSource (K092844)
• Curry Multimodal Imaging Software (K001781)

Submission Summary (Full Text)

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Image /page/0/Picture/0 description: The image shows the logo of the U.S. Food and Drug Administration (FDA). On the left is the Department of Health & Human Services logo. To the right of that is the FDA logo, which consists of the letters "FDA" in a blue square, followed by the words "U.S. FOOD & DRUG" in blue, and then the word "ADMINISTRATION" in a smaller font size below that.

January 8, 2018

Epilog % Patsy Trisler Consultant Qserve Group US Inc. 5600 Wisconsin Avenue Chevy Chase, Maryland 20815

#### Re: K172858

Trade/Device Name: PreOp Regulation Number: 21 CFR 882.1400 Regulation Name: Electroencephalograph Regulatory Class: Class II Product Code: OLX Dated: October 4, 2017 Received: October 10, 2017

#### Dear Ms. Trisler:

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 (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. 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.

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 803); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820);

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K172858

and if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to http://www.fda.gov/MedicalDevices/Safety/ReportaProblem/default.htm for the CDRH's Office of Surveillance and Biometrics/Division of Postmarket Surveillance.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/) and CDRH Learn (http://www.fda.gov/Training/CDRHLearn). 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 (http://www.fda.gov/DICE) for more information or contact DICE by email ([e-mail]) or phone [phone] or 301-796-7100).

Sincerely,

# Michael J. Hoffmann -S

Carlos L. Peña. PhD. MS for Director Division of Neurological and Physical Medicine Devices Office of Device Evaluation Center for Devices and Radiological Health

Enclosure

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# Indications for Use

510(k) Number (if known) K172858

Device Name PreOp

Indications for Use (Describe)

PreOp is intended for use by a trained/qualified EEG technologist or physician on both adult and pediativ subjects at least 3 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an individualized head model and an individualized MRI image.

Type of Use (Select one or both, as applicable)

| <span style="font-size:12px">☑ Prescription Use (Part 21 CFR 801 Subpart D)</span> |
|------------------------------------------------------------------------------------|
| <span style="font-size:12px">☐ Over-The-Counter Use (21 CFR 801 Subpart C)</span> |

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# 510(k) Summary

## 1. SUBMITTER

| Submitter Name: | Epilog |
|---------------------------------------------------|-------------------------------------------------|
| Submitter Address: | Vlasgaardstraat 52<br>9000 Gent, Belgium |
| Phone Number: | [phone] |
| Contact Person: | Gregor Strobbe |
| Date Prepared: | 18 September 2017; updated 18 December 2017 |
| 2. DEVICE | |
| Device Trade Name: | PreOp |
| Common Name: | Electroencephalograph Software |
| Classification Name,<br>Number &<br>Product Code: | Electroencephalograph<br>21 CFR 882.1400<br>OLX |
| Class: | II |
| Classification Panel: | Neurology |

#### 3. PREDICATE DEVICES

| Primary Predicate Device: | K092844, GeoSource |
|---------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Intended use: | GeoSource is intended for use by a trained/qualified<br>EEG technologist or physician on both adult and<br>pediatric subjects at least 3 years of age for the<br>visualization of human brain function by fusing a variety<br>of EEG information with rendered images of an<br>idealized head model and an idealized MRI image. |

The primary predicate device has not been subject to a design-related recall.

| Secondary Predicate Device: | K001781, Curry Multimodal Imaging Software |
|-----------------------------|--------------------------------------------------|
| Intended use: | No 510(k) Summary Posted; not publicly available |

The secondary predicate device has not been subject to a design-related recall.

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#### 4. DEVICE DESCRIPTION

PreOp is medical device software that combines EEG data and MR images to visualize recorded EEG activity in 3D in the brain. In figure 5.1 we present a general overview of the PreOp device. PreOp can be subdivided in 3 main modules: 3D Electrical Source Imaging (i.e. 3D ESI), Report generation and Viewer generation. The device's input is the MRI and EEG data that are uploaded by the user to the PreOp cloud environment. The output of the device is a report containing the results of the visualization and the ability to evaluate the results in 3D using the 3D viewer. The user can access the output through the PreOp cloud environment.

Image /page/4/Figure/4 description: The image shows a diagram of a process with inputs and outputs. The inputs are MRI and EEG, which are uploaded to a cloud. The data is then processed by PreOp 3D ESI, which generates a report and a 3D viewer. The report and 3D viewer are then downloaded from the cloud as outputs.

Figure 5.1: General overview of PreOp

#### 5. INDICATIONS FOR USE

PreOp is intended for use by a trained/qualified EEG technologist or physician on both adult and pediatric subjects at least 3 years of age for the visualization of human brain function by fusing a variety of EEG information with rendered images of an individualized head model and an individualized MRI image.

#### 6. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH PREDICATE DEVICE

| | New Device | Primary Predicate<br>Device | Secondary<br>Predicate Device |
|----------------------------------|--------------|-----------------------------|----------------------------------------------|
| Device name | PreOp | GeoSource | Curry multimodal<br>neuroimaging<br>software |
| 510(k) number | | K092844 | K001781 |
| Manufacturer | Epilog | EGI | Neurosoft Inc. |
| Regulation | 882.1400 | 882.140 | 882.1400 |
| Device<br>Classification<br>Name | Class II | Class II | Class II |
| Software only<br>product | Yes | Yes | Yes |
| Computer OS | MS-windows 7 | Mac OS | MS-windows 7 |

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| MRI visualization | Individualized MRI | Idealized MRI<br>(average) | Idealized MRI<br>(average) and<br>individualized MRI |
|-----------------------------------------------------------------------|----------------------------------|---------------------------------------------|------------------------------------------------------------------------------|
| Source<br>estimation<br>methods:<br>Dipole fit Linear inverse methods | No<br>sLORETA | Yes<br>sLORETA,<br>LORETA, LAURA | Yes<br>LORETA |
| Forward head<br>modeling | Finite Difference<br>Model (FDM) | Sphere, Finite<br>Difference Model<br>(FDM) | Sphere, Boundary<br>Element Model<br>(BEM), Finite<br>Element Model<br>(FEM) |

Table 5.1: Comparison of new device to predicate device

The intended use are the same, and the technological characteristics Summary of Technological are essentially the same, as those of the predicate, K092844, Characteristics GeoSource.

> The PreOp device has two different modeling features that the predicate does not have.

> First, the predicate device only works with idealized anatomical MR images. The usage of idealized anatomical MR images is however a special case of the more generic modeling framework of PreOp. If an idealized MRI is given to PreOp, an equivalent forward model will be constructed as in GeoSource (see section 20.1 for a comparison). The use of individualized anatomical MR images is implemented as described in the Secondary Predicate Device, K001781. There is no essential difference in technological characteristics between PreOp and the K001781 device since the Finite Element Model is equivalent to the Finite Difference Model from an individualized forward modeling point of view.

> Second, a segmentation of CSF, white matter tissue and air has been added. The use of additional tissues does not raise different questions of safety and effectiveness. See Neuroimage: Clinical 5(2014) 77-83, which concludes that the PreOp modeling of CSF, white matter and air in addition to 3-compartment, scalp, skull and gray matter of the predicate device is substantially equivalent for clinically accurate ESI.

Both the The PreOp and the predicate have the same intended use. Substantial Equivalence Both devices enable visualization of human brain function by fusing a Comparison variety of EEG information with MRI image.

> From the standpoint of both functionality and workflow The PreOp device is substantially equivalent to the identified predicate as follows:

- Within The PreOp and its predicate, the user can provide EEG ● input data.
- . The PreOp and its predicate are designed to segment EEG

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activity and visualize EEG activity in 3D in the brain using an MR image.

- The PreOp and its predicate use externally acquired medical data and user input to achieve the result.
- The intended patient population is > 3 years in both devices.
- Both devices use the standard 10/20 positioning system of the . electrodes and work with a distributed dipole source space.
- . Both devices use the finite difference method for forward modeling and sLORETA as linear inverse source estimation method.

Verification tests were written and executed to ensure that the system is working as designed. The PreOp passed testing and was determined safe and effective for its intended use.

Performance testing data for PreOp is available in the relevant sections of the 510(k) document to support the Substantial Equivalence determination.

## 7. PERFORMANCE DATA

Non-Clinical testing Validation and Verification Testing carried out on the PreOp indicates that it meets its predefined product's requirements and requirements from the following product standard: AAMI/ANSI/IEC 62304:2006. Medical Device . Software - Software Life Cycle Processes Clinical Performance In order to compare the source localization performance of Testing - study 1 PreOp as compared to that of the predicate device, a retrospective data analysis of 18 epilepsy subjects aged 3 to 55 who had previously undergone resection surgery was provided. The analysis compared the source localization accuracy of the PreOp software algorithms (i.e., sLORETA with the finite difference model [FDM] using an individualized anatomical MRI) to that of the predicate algorithm (i.e., sLORETA with the finite difference model [FDM] using a idealized anatomical MRI). All subjects had previously undergone a long-term EEG registration prior to resection surgery, had operative data available that described the resected zone, and were determined to be Engel I postoperatively. The study included subjects with temporal and extratemporal resected zones. Each subject's EEG was automatically processed using the FDA approved spike detection algorithm of Persyst and spikes were then grouped according to topographic distribution and then averaged relative to the peak of the spike to increase the signal-tonoise ratio. The average of the most dominant group was used in the source estimate. The time point used in the source estimate was the peak of the spike. The data were

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| | then run through the PreOp software algorithms and the<br>predicate algorithm. Three experienced epileptologists were<br>provided the source localization results along with summaries<br>of the postoperative reports and asked to rate whether each<br>of the algorithm solutions (sLORETA with the finite difference<br>model [FDM] using an idealized or individualized anatomical<br>MRI) were concordant on a sublobular level. The results<br>demonstrated that the proposed PreOp algorithms were<br>substantially equivalent to the predicate device algorithm. |
|-------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Clinical Performance<br>Testing - study 2 | In this study the performance was compared of spike source<br>localization using HD-EEG recordings (128 or 256<br>electrodes) and Low Density LD-EEG recordings (25<br>electrodes). By evaluating the source localization results, we<br>found that both algorithms provided identical source locations<br>in 13 epileptic spikes using LD-EEG and HD-EEG recordings<br>in 8 patients. Only in 3 spikes the spike localization was not<br>100% equivalent but very close to each other. |
| Software Verification<br>and Validation Testing | Validation testing involved algorithm testing which validated<br>the accuracy of PreOp. The product was deemed fit for<br>clinical use. Usability validation is part of the Clinical<br>Performance data and PreOp was tested and meets the<br>requirements of following standard:<br>AAMI/ANSI/IEC 62366:2007, Medical devices -<br>●<br>Application of usability engineering to medical<br>devices. |
| | PreOp was designed and developed as recommended by<br>FDA's Guidance, "Guidance for the Content of Premarket<br>Submissions for Software Contained in Medical Device".<br>PreOp was considered to represent "moderate" level of<br>concern as it is not intended to provide recommendations for<br>treatment nor to provide decisive information. According to<br>AAMI/ANSI/IEC 62304 Standard, PreOp safety classification<br>has been set to Class B. |

## 8. CONCLUSION

The information discussed above and provided in the 510(k) submission demonstrate that the PreOp device is substantially equivalent to the predicate.