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The NeuroSENSE Monitoring System, Model NS-901, is intended for monitoring the brain state of adult and pediatric patients (18 years of age and older) in the operating room and other clinical settings by acquiring electroencephalographic (EEG) signals.

The WAVCNs index, a quantifier of EEG activity calculated and displayed by the NeuroSENSE NS-901 Monitor, may be used as an aid in monitoring the hypnotic effect of anesthetics. The anesthetics include inhaled anesthetics and propofol in combination with opioids. The NeuroSENSE Monitor is intended to be used under the direction and interpretation of a qualified medical professional.

The NeuroSENSE Monitoring System, Model NS-901, is a 2-channel bilateral processed Electroencephalograph (EEG) monitor for brain function monitoring in the operating room and other clinical settings. The acquired EEG waveforms and processed EEG variables are continuously displayed by the system for interpretation by a qualified medical professional and for use as a supplement to the anesthesia standard of care. The user interacts with the system via a touch screen interface.

The NS-901 System consists of the following main components:

• Display Module (DM-901) processes acquired EEG signals, displays EEG waveforms . and processed EEG variables, and archives them for later review
• EEG Module (EM-901)
• 1. acquires analog EEG signals through the integrated Patient Cable connected to electrodes on a patient's forehead,
• 2. converts acquired analog EEG signals into digital EEG signals, and
• 3. sends the digital EEG signals to the Display Module through the integrated Data Cable
• EasyPrep Sensor Kit (EK-901) Noninvasive, disposable, single-use patient electrodes ● for acquiring the EEG signal

The NeuroSENSE Monitoring System displays EEG waveforms and the following EEG processed variables and plots for each EEG channel:

• Wavelet-based Anesthetic Value for Central Nervous System (WAVCNS) ●
• . Electromyogram (EMG)
• Suppression Ratio (SR) ●
• Spectral parameters: Density Spectral Array (DSA), Median Edge Frequency (MEF), ● Spectral Edge Frequency (SEF), and spectral powers in different EEG frequency bands

For improved reliability, the NeuroSENSE employs circuitry and algorithms for automatic detection, removal and/or filtering of physiological and environmental artifacts that commonly contaminate EEG signals. The NS-901 System also performs self-tests, automatic calibration of the amplifiers and continuous check of the electrode-skin contacts to ensure proper operation and optimal signal quality. Signal quality indicators (electrode status, 50/60 Hz noise level, artifact status) as well as system alarms, notifications and other related messages are displayed by the system. The system also provides protection for the operator and patient during cardiac defibrillation.

The provided text describes the NeuroSENSE Monitoring System, Model NS-901, and its substantial equivalence to a predicate device. While it details several performance characteristics and the clinical study conducted, it does not explicitly state acceptance criteria in a quantitative, pass/fail manner. Instead, the performance is described in terms of "correlation," "discrimination," and "agreement" with clinical observations and the predicate device.

Given this, I will infer relevant performance measures from the text and present them as "reported device performance." I will also explicitly state when information requested is not present in the provided document.

Acceptance Criteria and Device Performance

The NeuroSENSE Monitoring System, Model NS-901, was evaluated to demonstrate its substantial equivalence to the predicate device in monitoring brain function during anesthesia. While explicit, numeric acceptance criteria are not presented in a traditional table format in the provided text, the document describes the purpose of the validation and the outcomes that supported the substantial equivalence claim.

Based on the provided information, the implicit acceptance criteria are related to the device's ability to:

• Discriminate clinical endpoints related to consciousness.
• Correlate with changes in anesthetic dosing.
• Demonstrate an appropriate range for general anesthesia for its proprietary index (WAVCNS).
• Show excellent agreement with the predicate device for a specific parameter (SR).

Study Details:

1. Sample Size used for the Test Set and Data Provenance:

   • Sample Size: 75 adult surgical patients.
   • Patient Age: 18-71 years.
   • Gender: 18 male / 57 female.
   • ASA Classification: I-III.
   • Data Provenance: The study was a "prospective clinical study." While the country of origin is not explicitly stated, the context of an FDA 510(k) submission generally implies the study adheres to U.S. regulatory standards, often conducted in the U.S. or under international standards acceptable to the FDA. The data was collected "in the operating room."

2. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications:

   • This information is not provided in the text. The ground truth ("clinical observations" and "drug information") was seemingly captured during standard clinical practice by the operating medical professional(s), but the number and specific qualifications of the adjudicating or ground truth-establishing experts are not specified.

3. Adjudication Method for the Test Set:

   • This information is not provided in the text. The "clinical observations" and "drug information" that served as ground truth appear to be direct clinical records, but details on how these observations were adjudicated (e.g., by multiple experts, consensus, etc.) are absent.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No, an MRMC comparative effectiveness study was not explicitly mentioned or described. The study focused on the device's performance in relation to clinical observations and comparison to a predicate device's calculated parameter, rather than evaluating human reader improvement with or without AI assistance.
   • Effect Size of Human Readers Improvement: Not applicable, as no MRMC study was described.

5. Standalone Performance (Algorithm-only without human-in-the-loop performance):

   • Yes, in essence. The study evaluated the device's calculated indices (WAVCNS, SR) against clinical observations and anesthetic dosing, as well as the agreement of the device's SR calculation with that of the predicate. The device generates these values automatically. While the interpretation of the NeuroSENSE Monitor's output and its use as an aid is "under the direction and interpretation of a qualified medical professional," the performance evaluation described (e.g., discrimination of endpoints, correlation with dosing, agreement of SR) refers to the algorithmic output's accuracy and utility.

6. Type of Ground Truth Used:

   • The ground truth relied on clinical observations (e.g., loss of consciousness (LOC) and return of consciousness (ROC)) and drug information (anesthetic dosing). Comparison for SR was against the SR calculated by a predicate device. This implies a form of clinical outcome/physiological measurement-based ground truth, observed by medical professionals during actual procedures.

7. Sample Size for the Training Set:

   • This information is not provided in the text. The document describes the "validation" study as a "prospective clinical study in 75 adult surgical patients." It does not specify whether this dataset was used for training, testing, or exclusively for validation, nor does it mention the size of a separate training set if applicable. The focus of this 510(k) is on the validation data that supports substantial equivalence.

8. How Ground Truth for the Training Set Was Established:

   • This information is not provided in the text, as details on a distinct training set are absent. If part of the 75-patient dataset was used for training (which is unlikely for a validation study unless it was a hold-out set for final performance evaluation), the ground truth would have been established via clinical observations and drug information as described for the evaluation.

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The DiscoverEEG System, Model DE-401 is intended to be used for measuring and recording the electrical activity of a subject's brain, obtained by placing non-invasive electrodes on the head. The DiscoverEEG, DE-401 is indicated for use in acquiring electroencephalographic (EEG) signals in the OR, ICU, ER, clinical settings and at home and for clinical research. The medical use of data acquired by the DiscoverEEG is to be performed under the direction and interpretation of a licensed medical professional. The DiscoverEEG, Model DE-401 does not provide any diagnostic conclusion about the subject's condition.

The DiscoverEEG System, Model DE-401, is a wearable, medical-grade EEG device that acquires and stores up to four electroencephalograms (EEGs) obtained from noninvasive electrodes placed on a subject's head. The acquired EEG waveforms, as well as, processed EEG spectral variables are continuously stored by the system for later retrieval. The data can be transferred from the DiscoverEEG hardware to a computer for review. The DiscoverEEG System, Model DE-401 has four main components: Acquisition Module, Memory Module, Disposable Electrode Array, and Data Viewer Software.

The provided text does not contain specific acceptance criteria for performance metrics (like sensitivity, specificity, accuracy) related to the interpretation of EEG signals by the DiscoverEEG System, Model DE-401 for diagnostic purposes. This is explicitly stated in the Indications for Use: "The DiscoverEEG, Model DE-401 does not provide any diagnostic conclusion about the subject's condition."

Instead, the non-clinical testing sections focus on the device meeting its design and functional requirements, safety standards (UL, IEC60601-1, IEC60601-1-11, IEC60601-2-26), and electromagnetic compatibility (IEC60601-1-2). The substantial equivalence claim is based on similar intended use, technological characteristics, and principles of operation to predicate devices, with bench testing demonstrating functional performance without providing specific quantitative metrics for diagnostic accuracy.

Therefore, for the aspects requested, here's what can be extracted from the document:

1. A table of acceptance criteria and the reported device performance

The document does not provide a table of performance-based acceptance criteria (e.g., sensitivity, specificity) for diagnostic accuracy. The "reported device performance" is primarily qualitative, stating that the device "meets its design and functional requirements" and "performed as expected."

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not applicable. The document states "Laboratory testing, performed on identical hardware to the DiscoverEEG subject of this submission, demonstrated that the DiscoverEEG, Model DE-401 meets its design and functional requirements." This indicates bench testing rather than a clinical human subject test set. There's no mention of sample size or data provenance in this context.

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)

Not applicable. The testing described is non-clinical bench testing, not involving human interpretation for ground truth.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not applicable. There was no test set requiring expert adjudication for ground truth.

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

Not applicable. The device "does not provide any diagnostic conclusion about the subject's condition" and the submission states "further clinical data is not required to demonstrate performance for the DiscoverEEG, Model DE-401 for the indication for use subject to this submission." Therefore, no MRMC study was performed to assess human reader improvement with or without AI assistance.

6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

Not applicable in the context of diagnostic performance. The device's function is to "measure and record the electrical activity of a subject's brain" and provide "processed EEG spectral variables." The medical use and interpretation are explicitly "to be performed under the direction and interpretation of a licensed medical professional." While the device itself processes signals, the "standalone" performance for diagnostic purposes is not claimed or evaluated. The non-clinical testing focused on hardware functionality and adherence to standards.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not applicable. The testing described is non-clinical/bench testing against design specifications and functional requirements, not against clinical ground truth like expert consensus, pathology, or outcomes data.

8. The sample size for the training set

Not applicable. The document does not describe an AI/ML component that requires a training set for diagnostic or interpretative tasks. The device acquires and processes EEG signals, but its output is for interpretation by a medical professional, not a standalone diagnostic conclusion.

9. How the ground truth for the training set was established

Not applicable, as there is no described training set.

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