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RespiEvents™ is a software package running on a personal computer that is intended to provide analysis of breathing patterns from RespiTrace technology, aid in identifying and classifying apneas, displaying heart rate changes from electrocardiographic waveforms, logging values from pulse oximetry, and displaying signals from physiologic recording devices such as a body position sensor and impedance pneumograph, in the wake and sleeping states as well as activities of daily living.

RespiEvents™ is a software analysis program running on a personal computer. RespiEvents™ presents waveforms collected from RespiTrace™ technology-based RespiEvelts "prosents wavelorm" and RespiTrace PT™ and is intended to provide analysis of:

• Breathing patterns from Respitrace™ technology, ● ·
• Aid in identifying and classifying apneas, .
• Displaying heart rate changes from electrocardiographic waveforms, .
• Logging values from pulse oximetry, and .
• Displaying signals from physiologic recording devices .
  The purpose of RespiEvents™ software is to capture full fidelity Respitrace™ data on a continuous basis, and to provide playback and analysis of the captured data.
  The system presents the information from physiologic recording devices such as a body position sensor and impedance pneumograph, in the wake and sleeping states. The system does not present alarms, alarm functions, or alarm management.

The provided document (K012020) describes a Special 510(k) Submission for the RespiEvents™ software, Version 5.2. This submission focuses on modifications to a previously cleared device (RespiEvents™ software, Version 4.2, K001369). The document does not overtly present specific acceptance criteria or a detailed study proving the device meets them in the format usually expected for rigorous performance evaluation.

Instead, the submission emphasizes that the modifications did not change the intended use or the fundamental scientific technology of the device. It relies on nonclinical tests to demonstrate that the new software performs equivalently to the predicate device and that all functions comply with user requirements, safety standards, and performance specifications.

Therefore, many of the requested details about acceptance criteria, clinical studies, sample sizes, ground truth establishment, and expert involvement are not explicitly available in this summary document. The document primarily focuses on verifying the functionality and safety of the software modifications.

Here's an attempt to answer based on the provided text, with significant caveats for missing information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a formal table of acceptance criteria with specific quantitative performance metrics. Instead, the "acceptance criteria" are implied to be that the updated software functions as intended, is in compliance with user requirements, safety standards, and performance specifications, and performs substantially equivalently to the predicate device.

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

• Sample Size: Not explicitly stated. The document mentions "simulated input signals, manual operations and recordings of actual physiological waveforms" but does not give a number of cases or data points used in the nonclinical tests.
• Data Provenance: The document does not specify the country of origin for any "actual physiological waveforms" used, nor does it explicitly state if the data was retrospective or prospective. Given the nature of a software update for an existing device, it's likely they used existing or simulated data relevant to the predicate device's use.

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

• This information is not provided in the document. The testing described is "verification and validation scenarios." For software that processes physiological signals, "ground truth" might refer to known characteristics of simulated signals or expert interpretations of physiological waveforms. However, the document does not elaborate on how ground truth was established for these nonclinical tests or if any external experts were involved.

4. Adjudication Method for the Test Set

• This information is not provided in the document. Given the description of "verification and validation scenarios" for a software update, it's more likely that the testing involved comparing software output against expected results or direct observation of functionality, rather than an expert adjudication process typically seen in clinical studies validating diagnostic accuracy.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not done (or at least, not reported in this summary). The document explicitly discusses "nonclinical tests" and focuses on substantial equivalence to the predicate device. It does not describe any studies involving human readers or evaluating the improvement with AI assistance (as this is a software update for signal processing, not an AI diagnostic tool in the modern sense).

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

• Yes, implicitly. The "nonclinical tests" described, including the use of "simulated input signals" and analysis of "recordings of actual physiological waveforms," represent a form of standalone testing of the software's performance. The software's function is to analyze and present information; its performance in these tasks would be evaluated directly without human interpretation as part of a human-in-the-loop workflow being measured. The focus is on the software's ability to correctly process and display data.

7. The Type of Ground Truth Used

• Implicitly, known values for simulated signals and/or established interpretations for actual physiological waveforms. The document mentions "simulated input signals" and "recordings of actual physiological waveforms." For simulated signals, the ground truth would be the known parameters of the simulated data. For actual physiological waveforms, the ground truth would likely be established through established medical guidelines, prior expert interpretation, or consensus derived from the predicate device's accepted performance. No pathology or outcomes data is mentioned.

8. The Sample Size for the Training Set

• Not applicable / Not provided. This is a software update for an existing analytical tool, not a new machine learning algorithm that requires a "training set" in the conventional sense. The "core software, algorithms, or fundamental scientific technology" were explicitly not modified. Therefore, there isn't a "training set" for this specific submission. The original predicate device would have been developed based on scientific principles and potentially empirical data, but that's not described here.

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

• Not applicable / Not provided. As there is no "training set" for this specific submission due to it being a software update without core algorithm changes, this question is not relevant to the information provided.

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