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1. LVIS NeuroMatch Software is intended for the review, monitoring and analysis of electroencephalogram (EEG) recordings made by EEG devices using scalp electrodes and to aid neurologists in the assessment of EEG. The device is intended to be used by qualified medical practitioners who will exercise professional judgement in using the information.

2. The Seizure Detection component of LVIS NeuroMatch is intended to mark previously acquired sections of adult EEG recordings from patients greater than or equal to 18 years old that may correspond to electrographic seizures, in order to assist qualified medical practitioners in the assessment of EEG traces. EEG recordings should be obtained with a full scalp montage according to the electrodes from the International Standard 10-20 placement.

3. The Spike Detection component of LVIS NeuroMatch is intended to mark previously acquired sections of adult EEG recordings from patients >18 years old that may correspond to spikes, in order to assist qualified medical practitioners in the assessment of EEG traces. LVIS NeuroMatch Spike Detection performance has not been assessed for intracranial recordings.

4. LVIS NeuroMatch includes the calculation and display of a set of quantitative measures intended to monitor and analyze EEG waveforms. These include Artifact Strength, Asymmetry Spectrogram, and Fast Fourier Transform (FFT) Spectrogram. These quantitative EEG measures should always be interpreted in conjunction with review of the original EEG waveforms.

5. LVIS NeuroMatch displays physiological signals such as electrocardiogram (ECG/EKG) if it is provided in the EEG recording.

6. The aEEG functionality included in LVIS NeuroMatch is intended to monitor the state of the brain.

7. LVIS NeuroMatch Artifact Reduction (AR) is intended to reduce muscle and eye movements, in EEG signals from the International Standard 10-20 placement. AR does not remove the entire artifact signal and is not effective for other types of artifacts. AR may modify portions of waveforms representing cerebral activity. Waveforms must still be read by a qualified medical practitioner trained in recognizing artifacts, and any interpretation or diagnosis must be made with reference to the original waveforms.

8. This device does not provide any diagnostic conclusion about the patient's condition to the user.

NeuroMatch is a cloud-based software as a medical device (SaMD) intended to review, monitor, display, and analyze previously acquired and/or near real-time electroencephalogram (EEG) data from patients 18 years old or older. The device is not intended to substitute for real-time monitoring of EEG. The software includes advanced algorithms that perform artifact reduction, seizure detection, and spike detection.

Here's a breakdown of the acceptance criteria and the study details for the NeuroMatch device, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

For Seizure Detection:

For Spike Detection:

2. Sample Sizes and Data Provenance

For Seizure Detection Validation:

• Test Set Sample Size:
  • Patients: 181 patients with at least 1 verified seizure event, and 10 control patients with 0 verified seizure events.
  • EEG Hours: 979.40 hours from seizure patients, 80 hours from control patients.
  • Events: 504 events used for sensitivity calculation (capped at 6 events per patient).
• Data Provenance: Data collected from three independent and geographically diverse medical institutions. The document indicates this data was "completely separate and independent from the data used to design and train the algorithm," implying it is retrospective data. Country of origin is not explicitly stated but implied to be the US based on the FDA submission.

For Spike Detection Validation:

• Test Set Sample Size:
  • Patients: 149 patients with at least 1 verified spike event.
  • Total EEG minutes (hours): 2752.72 minutes (~45.9 hours).
• Data Provenance: Data collected from three independent and geographically diverse medical institutions. This dataset was also "completely separate and independent from the data used to design and train the algorithm," indicating retrospective data. Country of origin is not explicitly stated.

3. Number and Qualifications of Experts for Ground Truth

• Number of Experts: Three independent EEG-trained neurologists.
• Qualifications of Experts: EEG trained neurologists. More specific details like years of experience are not provided.

4. Adjudication Method for the Test Set

• Seizure Detection: A reference standard was established by a panel of three independent EEG trained neurologists. Seizures were identified based on a 2 out of 3 majority rule.
• Spike Detection: The reference standard was established by a panel of three independent EEG trained neurologists. Spikes were identified with majority consensus among the annotating physicians (i.e., consensus of at least 2 out of the 3 physicians).

5. MRMC Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study evaluating human reader improvement with AI assistance versus without AI assistance was not explicitly described in the provided text. The study compares the algorithm's performance (NeuroMatch) to a predicate algorithm's performance (Persyst 14), not human performance.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done for both seizure and spike detection. The results presented in the tables are direct comparisons between the NeuroMatch algorithm and the Persyst 14 predicate algorithm, both run on the validation dataset against a ground truth established by experts.

7. Type of Ground Truth Used

The ground truth used for both seizure and spike detection was expert consensus, specifically "consensus of at least 2 out of the 3 physicians" (majority rule) from independent EEG-trained neurologists.

8. Sample Size for the Training Set

The sample size for the training set is not explicitly provided in the given document. The document states that the validation dataset "was completely separate and independent from the data used to design and train the algorithm," but it does not specify the details of the training data.

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

The method for establishing ground truth for the training set is not explicitly provided in the document. It only states that the validation set was independent of the data used for training and design.

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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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