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The Hewlett-Packard Viridia CMS Patient Monitoring System, Rel.K, with M1027A EEG Measurement Module is intended for measurement and display of the electroencephalogram of adults, pediatrics, and neonates in the Operating Room and intermediate/critical care environments.

The modification is the addition of new applications software and firmware that involves the addition of the M1027A EEG Module to the HP M1175A/76A/77A Component Monitoring System to allow the measurement of electroencephalographic signals.

The provided text describes a 510(k) summary for the Hewlett-Packard Viridia M1175A/76A/77A Component Monitoring System with M1027A EEG module. However, it does not contain detailed acceptance criteria or a study design structured in the way requested by the prompt for a device performance evaluation. The document primarily focuses on the device's substantial equivalence to predicate devices and describes general validation and testing activities.

Here's an attempt to extract and infer information based on the provided text, highlighting the missing details:

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: Specific quantitative thresholds for hardware, software, and clinical performance are not detailed. For example, what constitutes "acceptable" usability or specific signal-to-noise ratios for EEG acquisition are not provided.

2. Sample size used for the test set and the data provenance

The text mentions "Clinical performance evaluations were conducted with the EEG module to validate two channel functionality under conditions existing in the indicated hospital environments."

• Sample Size: Not specified. It only refers to "users."
• Data Provenance: The studies were conducted "under conditions existing in the indicated hospital environments" for the specified patient populations (adult, pediatric, and neonatal). No country of origin is explicitly mentioned, but the submitter is from Germany and the notification is to the US FDA. It's implied to be prospective clinical observations, but not explicitly stated.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This information is not provided in the document. The clinical evaluation focused on "applicability, usability, and efficiency" as perceived by "users," not on diagnostic accuracy requiring expert ground truth establishment.

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

This information is not provided as the study did not focus on diagnostic accuracy requiring adjudication.

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 was done. This device is a measurement module for EEG signals, not an AI-assisted diagnostic tool for human readers. The clinical evaluation focused on system usability and functionality.

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

• The document implies that the device's core functionality (measuring EEG signals) was evaluated as a standalone component within the larger monitoring system during "system level tests, integration tests, environmental tests, safety testing from hazard analysis, interference testing, and hardware testing." These tests would assess the algorithm's performance in signal acquisition and processing. However, a separate "algorithm only" performance study in the context of diagnostic accuracy (e.g., classifying EEG patterns) is not described.

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

For the clinical performance evaluation mentioned, the "ground truth" was user perception of "applicability, usability, and efficiency," rather than a clinical ground truth for diagnostic accuracy (like expert consensus on EEG abnormalities or pathology). For the technical tests, the "ground truth" would be established by engineering specifications, relevant standards, and the performance of predicate devices.

8. The sample size for the training set

This information is not provided. The document describes validation and testing activities, but not the development or training of any machine learning algorithms. The device's functionality seems to be based on established signal processing and measurement principles rather than a learning-based approach requiring a "training set."

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

This information is not applicable/not provided as there is no mention of a training set or machine learning components requiring a "ground truth" for training.

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