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The device is intended to monitor, display and record physiological data to provide cardiac and vital signs monitoring within a medical facility. The device is intended to produce a visual record of the electrical signals produced by the heart and monitor the electrocardiogram to generate audible and/or visual alarms when an arrhythmia exists. The device is also intended to monitor patients' physiological data including heart rate, pulse rate, blood oxygen saturation (SpO2) noninvasive blood pressure (NIBP), body temperature respiratory rate and apnea. The device may generate an audible and/or visual alarm when a measured rate falls outside preset limits. This device receives physiological signals via radio from the transmitters. The device has the capability of communicating with external devices, such as personal computers. Vital sign data can be sent to a central monitor through the network via network card.

The system provides for monitoring ECG, ST levels, VPC rate, arrhythmia events, SpO2, NIBP, temperature, respiration and apnea. The device will be offered with up to 5 multi-connectors and SpO2. The system is comprised of a receiver (WEP-4200A), and a transmitter (ZS-910P). WEP is a multi-parameter monitor consisting of a color LCD touch-screen to display waveforms and numerics of monitored parameters, multi-parameter receiver unit, visual alarm indicator, external communications port, and a removable battery pack for transmitter. Options include a thermal array recorder and a laser printer interface card.

The provided text is a 510(k) summary for the Nihon Kohden WEP-4200A series Central Telemetry System, seeking clearance as substantially equivalent to previously marketed predicate devices. It describes the device, its intended use, and a summary of non-clinical tests conducted. However, the document does not contain explicit acceptance criteria or detailed study results with specific performance metrics (like sensitivity, specificity, accuracy) to "prove the device meets acceptance criteria" in the way typically expected for an AI/ML medical device.

The document focuses on demonstrating substantial equivalence to existing devices, primarily by highlighting that the new device uses the same underlying technology, algorithms, and accessories for its physiological monitoring functions (ECG, arrhythmia detection, SpO2, NIBP, temperature, respiration) as the predicate devices. The main differences noted are an updated transmitting frequency band (WMTS) and increased storage memory.

Therefore, many of the requested items cannot be directly extracted from the provided text. I will address what can be inferred or explicitly stated.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a table of quantitative acceptance criteria (e.g., minimum sensitivity/specificity for arrhythmia detection) or corresponding reported device performance metrics for specific functions. Instead, it relies on demonstrating that the core monitoring functionalities are "the same" as or use "the same control software" or "same algorithm" as legally marketed predicate devices.

The closest to "acceptance criteria" are compliance with regulatory standards and verification of operation.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a sample size for a test set in the context of performance evaluation for detection algorithms. The "non-clinical tests" mentioned refer to compliance with standards and design validation, not a clinical performance study with patient data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided as the submission relies on equivalence to predicate devices and compliance with engineering/safety standards rather than a de novo clinical ground truth establishment for a novel algorithm.

4. Adjudication Method for the Test Set

This information is not provided as there is no mention of a clinical test set requiring adjudication in the context of algorithm performance.

5. If a Multi-reader Multi-case (MRMC) Comparative Effectiveness Study was done, and its effect size.

No, an MRMC comparative effectiveness study was not done or mentioned in this document. The device is a "Central Telemetry System" for physiological monitoring and arrhythmia detection, which typically involves automated algorithms rather than human "readers" interpreting cases in the same way a diagnostic imaging AI might.

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

The document implies standalone algorithm performance was established by demonstrating that the arrhythmia detection and other physiological monitoring algorithms are the same as those in legally marketed predicate devices. It states: "The device incorporates the same multi-template arrhythmia detection and analysis of the predicate device..." and "The software algorithm that determines the respiration rate count is the same in both the new device and the predicate." However, specific standalone performance metrics (e.g., sensitivity, specificity) for these algorithms on a defined dataset are not provided.

7. The Type of Ground Truth Used

For the specific algorithms, the ground truth establishment is implicitly linked to the predicate devices. Since the algorithms are stated to be "the same," their original validation (which would have involved expert consensus or known physiological states for ground truth) for the predicate devices serves as the basis. The current document does not detail this original ground truth establishment.

8. The Sample Size for the Training Set

The document does not specify a sample size for a training set. This is likely because the device is not introducing a new, independently trained AI/ML algorithm but re-using or updating established (presumably rule-based or historically trained) algorithms from predicate devices.

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

This information is not provided, as the document focuses on substantial equivalence based on existing, proven technologies from predicate devices rather than the development and training of a new algorithm. The ground truth for the predicate devices' algorithms would have been established historically, but those details are not in this submission.

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