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The EP-WorkMate™ System is indicated for use during clinical electrophysiology procedures.

The EP-WorkMate® system with an EP-4 stimulator is intended to be used for diagnostic electrical stimulation of the heart for the purpose of refractory measurements, initiation and termination of tachy-arrhythmias, measurements of electrical conduction, and arrhythmia mapping.

The EP-WorkMate system is a computer-based electrophysiological recording and monitoring system that is used to capture, display, store, and retrieve surface and intracardiac electrical signals during electrophysiology studies. It consists of a computer. two 21" high-resolution monitors, a multi-channel signal amplifier and filtering system (signal conditioning unit), a catheter junction box, and a laser printer. The system may also be configured with an integrated EP-4 clinical stimulator and touch-screen computer monitor (cleared in K040207).

The EP-WorkMate is connected to electrophysiology catheters that are guided into various locations within the heart, and to surface electrocardiogram (ECG) cables. Intracardiac and ECG signals are then acquired from electrodes on the indwelling catheters and ECG leads, and transmitted to the amplifier, which amplifies and conditions the signals before they are received by the EP-WorkMate computer for measurement and display.

During the procedure, cardiac signals are acquired and an automated software waveform detector (trigger) performs online recognition of cardiac activation on preselected leads. Temporal interval measurements are computed on multiple channels on a beat-by-beat basis and dynamically displayed on the real-time display. Menu-driven software is utilized for data acquisition and analysis, interval posting, and instant data retrieval with waveform markers and intervals displayed.

Signals are also presented on a review monitor for measurement and analysis. Continuous capture of the digitized signals can be invoked, and the user can also retrieve and display earlier passages of the current study without interruption of the realtime display. The system can also acquire, display and record data from other interfaced devices in use during the procedure, such as imaging devices and ablation generators,

This 510(k) summary does not contain information typically found in a study proving a device meets acceptance criteria, especially for AI/ML-driven devices. This is a submission for an electrophysiology recording and monitoring system, which as described, appears to be a hardware and software system for acquiring, displaying, and storing physiological signals, rather than an AI/ML diagnostic tool.

The document states that "Bench testing was performed to confirm that the changes met design requirements and did not affect the safety or effectiveness of the product." This indicates an engineering validation approach rather than a clinical study with acceptance criteria for diagnostic performance.

Given the provided text, I cannot extract answers for many of your questions, as they pertain to clinical studies, AI performance metrics, and expert adjudication, which are not detailed in this submission.

Here's what I can infer from the provided text, addressing your questions where possible:

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

The document does not explicitly state acceptance criteria in a quantitative, performance-based manner typical for diagnostic device efficacy (e.g., sensitivity, specificity, accuracy). Instead, it states that "Bench testing was performed to confirm that the changes met design requirements and did not affect the safety or effectiveness of the product." This implies acceptance related to meeting engineering specifications and maintaining safety and effectiveness attributes similar to the predicate device.

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 or not provided. The bench testing performed would likely involve hardware and software validation testing, not a clinical test set with patient data provenance.

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 or not provided. Ground truth establishment by experts is typically relevant for diagnostic AI/ML devices or clinical studies with human interpretation, which is not described here.

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

Not applicable or not provided.

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 is not described as an AI assistance tool for human readers in a diagnostic context.

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

Not applicable. The description is of a system that acquires and displays signals for human interpretation, not an algorithm providing a standalone diagnostic output.

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

Not applicable or not provided. For "bench testing," ground truth would likely be based on known signal inputs or predefined engineering specifications rather than clinical outcomes or expert consensus.

8. The sample size for the training set

Not applicable. The device is not described as an AI/ML model requiring a training set in the conventional sense.

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

Not applicable.

Summary of what is known about the "study":

• Type of Study: Bench testing.
• Purpose: To confirm that changes in the device met design requirements and did not affect the safety or effectiveness of the product.
• Methodology: The development was performed in accordance with St. Jude Medical's Quality System requirements and in compliance with Quality System Regulation design control requirements (21 CFR 820.30). A Declaration of Conformity with Design Controls was provided.
• Conclusion: Performance testing demonstrated that any operational and performance differences from the predicate device do not adversely affect the device's safety and effectiveness, leading to a substantial equivalence determination.

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