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The Heart Rate Variability (HRV) Acquire device is intended to record and indicate the following parameters during autonomic testing maneuvers:

• Expiratory pressure recording and display
• Respiratory effort
• Breathing cue metronome
• Heart rate via electrocardiography (ECG)
• Non-invasive beat-to-beat blood pressure from optional external device

The HRV Acquire does not make a diagnosis or indicate by itself any disease state exists. The HRV Acquire is not designed for vital signs monitoring or self monitoring of patients.

The HRV Acquire (HRV) Device with TestWorks software can be used as a standalone cardiovagal device to support heart rate variability testing and expiratory pressure evaluation during valsalva breathing testing. The HRV device supports measurement of heart rate variability, respiratory effort from commercially available circumferential chest bellows, display of a valsalva breathing metronome breathing cue and display of non-invasive beat to beat blood pressure from an external device. An example of an external blood pressure device is the commercially available FMS Finometer, cleared to market by 510(k) K880572. Other commercially available, FDA cleared to market external beat to beat blood pressure devices with an analog blood pressure signal output may be applied.

The HRV device user interface is integrated into the existing WR Medical Electronics CO TestWorks software. The Test Works software was cleared to market with the Q-Sweat device, 510(K) K992874. The Test Works software is modified to support an interface to the HRV device.

The HRV device may be mounted to a standard IV Pole.

The HRV device has two modes of operation:

• 1. Valsalva Mode supporting valsalva breathing cue to the subject under test,
• 2. Heart Rate Deep Breathing (HRDB) Mode.

In both modes, the HRV device acquires and the TestWorks software displays heart rate and blood pressure. The HRV device eliminates the need for a separate ECG acquisition device when these types of studies are performed. The HRV device acquires a Lead II ECG signal.

The HRV device and TestWorks software do not supply any alarms. All interpretation of acquired and displayed data is the responsibility of the physician/clinician supervising the test.

Here's an analysis of the acceptance criteria and study information for the WR Medical Electronics CO. Heart Rate Variability Device (HRV Acquire), based on the provided 510(k) Summary K092809.

This document describes a medical device rather than an AI/ML device. Therefore, many of the typical AI/ML study components (like expert ground truth for a test set, MRMC studies, standalone algorithm performance, and training set details) are not applicable or detailed in this submission. The "study" described here focuses on demonstrating the device's functionality, safety, and equivalence to predicate devices, which is standard for traditional medical device clearances.

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device are largely based on the technical specifications and comparison to predicate devices, rather than clinical performance metrics typical of diagnostic algorithms. The "performance" is reported as compliance with these specifications and standards.

Study Details (Focusing on Device Functionality and Safety)

Since this is a traditional medical device (not AI/ML), the "study" is primarily a series of verification and validation tests to ensure the device meets its design specifications and complies with relevant standards.

1. Sample size used for the test set and the data provenance:

   • The document does not specify a "test set" in the context of patient data or clinical cases for performance evaluation in the way an AI/ML device would.
   • The testing described is system-level verification and validation. This typically involves using simulated signals, test equipment, and potentially a limited number of human subjects for usability and basic functionality checks, but the details (sample size, data provenance like country, retrospective/prospective) are not provided as they are not typically required for this type of submission.

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

   • Not applicable in the typical AI/ML sense. Ground truth for hardware/software verification typically involves comparing device outputs to known inputs from test equipment or established engineering standards.
   • Any human evaluation would be conducted by engineers, technicians, or clinicians for functional testing, but they wouldn't be "establishing ground truth" for diagnostic accuracy.

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

   • Not applicable. Adjudication methods are used for resolving disagreements in ground truth labeling for clinical data, which is not the primary focus of this device's validation.

4. 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. This device is a measurement tool, not an AI-assisted diagnostic. It provides raw data and measurements for a clinician to interpret. There is no "human reader" using "AI assistance" with this device.

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

   • The term "standalone" here would mean the device accurately acquires and displays the specified parameters. The 510(k) summary indicates that "testing of the Heart Rate Variability device was performed in compliance with the WR Medical Electronics CO. design control process," including "Software verification and validation" and "System verification." This implies its standalone functionality was tested against its specifications. The device does not make a diagnosis; "All interpretation of acquired and displayed data is the responsibility of the physician/clinician supervising the test." Thus, its "standalone performance" refers to its ability to accurately measure and display parameters.

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

   • For hardware and software verification, the "ground truth" is typically defined by:
     • Known input signals: e.g., electrical signals from a signal generator for ECG and R-wave detection accuracy.
     • Calibration standards: for pressure and timing measurements.
     • Engineering specifications: The device is tested to ensure its output matches expected values given specific inputs according to its design.
     • Compliance with recognized standards: Such as AAMI EC13 for cardiac monitors.

7. The sample size for the training set:

   • Not applicable. This is a traditional medical device, not an AI/ML system that requires a "training set" for model development.

8. How the ground truth for the training set was established:

   • Not applicable, as no training set exists for this type of device.

Summary of the Study:

The "study" described in the 510(k) summary for the HRV Acquire device is a design control verification and validation process. This process aimed to demonstrate that the device meets its predetermined technical specifications and complies with relevant safety and performance standards. Key aspects of this process included:

• Software verification and validation: Ensuring the software operates as intended and meets requirements.
• System verification: Confirming that the integrated hardware and software function correctly together.
• Compliance with AAMI EC13:2002/(R)2007: Adherence to this standard for cardiac monitors, heart rate meters, and alarms. This implicitly sets performance targets for ECG and R-wave detection accuracy.
• Safety standard compliance: Meeting requirements of EN/IEC 60601-1 and EN/IEC 60601-1-2 (electrical safety and electromagnetic compatibility).

The conclusion of the submission states, "The safety and effectiveness of the Heart Rate Variability device was demonstrated by testing in compliance with the Design Control process." This indicates that the device successfully passed these verification and validation activities, and by meeting its specifications and standards, it was deemed substantially equivalent to predicate devices.

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The Hilger Dual-Stim Nerve Stimulator provides electrical stimulation to nerves during diagnostic and surgical procedures.

The Hilger Dual-Stim Nerve Stimulator is a self contained, battery powered device. There is an option to provide a line powered option in the future. Two modes of nerve stimulation are provided, a Clinical Mode and a Surgical Mode.

The provided document describes the Hilger Dual-Stim Nerve Stimulator, a device used for electrical nerve stimulation during diagnostic and surgical procedures. The 510(k) summary focuses on demonstrating substantial equivalence to predicate devices rather than conducting a de novo clinical study with specific acceptance criteria based on performance metrics. Therefore, the requested information regarding acceptance criteria, study details, and expert involvement for ground truth cannot be fully provided in the format typically associated with AI/ML device evaluations.

However, based on the provided text, the device's "acceptance criteria" are implied through its compliance with safety standards and a design control process, and its equivalence to predicate devices in terms of intended use and technical characteristics.

Here’s a breakdown of the information that can be extracted, and where limitations exist:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not applicable. This document describes a traditional 510(k) submission for a non-AI/ML device where a "test set" in the context of data evaluation (like for AI performance) is not relevant. The device underwent engineering testing (software V&V, safety standard compliance) rather than performance evaluation against a labeled dataset.
• Data Provenance: Not applicable in the context of a dataset for AI evaluation. The "data" here refers to test results from design verification and validation activities.

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

• Number of Experts & Qualifications: Not applicable. Ground truth, in the context of expert consensus for labeling data, is not mentioned because this is not an AI/ML device submission. The "ground truth" for this device's performance relies on engineering specifications and compliance with safety standards as verified by qualified engineers and testers.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. There is no mention of an adjudication process for a "test set" as understood in AI/ML performance evaluation. The compliance was determined through internal design control processes and testing against engineering requirements and safety standards.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done. This type of study is relevant for evaluating human reader performance with and without AI assistance, which is not applicable to this nerve stimulator device.

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

• Standalone Performance: Not applicable. This device is an electrical nerve stimulator; it does not rely on an algorithm to interpret data or make diagnostic decisions in a standalone capacity. Its function is to provide electrical stimulation for human interpretation of nerve response.

7. The Type of Ground Truth Used

• Type of Ground Truth: The "ground truth" in this context is based on engineering specifications, international safety standards (e.g., EN/IEC 60601 series), and the established performance of predicate devices. The device's electrical output parameters (current, pulse width, frequency) are measured and compared against these defined specifications and those of the predicates.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This is not an AI/ML device that requires a training set.

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

• Ground Truth for Training Set: Not applicable. This is not an AI/ML device that requires a training set or ground truth for such a set.

Summary of the Study that Proves the Device Meets Acceptance Criteria:

The "study" referenced in the document is the internal design control process and associated testing, which included:

• Software verification and validation: Ensuring the device's software functions as intended and meets specifications.
• Declaration of safety standard compliance: Demonstrating that the device adheres to relevant international safety standards (EN/IEC 60601-1, EN/IEC 60601-1-2, EN/IEC 60601-2-10, EN/IEC 60601-2-40). This would involve electrical safety testing, EMC testing, and other relevant tests depending on the specific standard.

The conclusion states that these activities were sufficient to demonstrate the safety and effectiveness of the Hilger Dual-Stim Nerve Stimulator and to show its substantial equivalence to predicate devices, thus raising "No new questions of safety or effectiveness."

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The Q-Sweat™ Quantitative Sweat Measurement System is designed to measure the sweat output of the skin of humans. This device does not make a diagnosis or indicate by itself that any disease state exists; it simply documents sweat output. This device is to be used in scientific studies of the anatomy, physiology, and biochemistry of the skin & associated structures.

The O-Sweat device is measurement-only device which is designed to measure the rate & volume of sweating by capturing a sample of sweat (water) inside a small measuring chamber which is affixed to the skin. It does not measure any other parameters of the sweat sample. The measurement made is simply a calculation of moisture given off by the skin.

Here's an analysis of the provided text regarding the Q-Sweat Quantitative Sweat Measurement System, focusing on acceptance criteria and study details.

Based on the provided text, there is no specific study described that proves the device meets explicitly stated acceptance criteria. The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance study results against predefined metrics.

The document primarily states that the device is substantially equivalent to other sweat measuring devices. This is a claim made by the applicant, and the FDA's letter indicates their agreement with this claim for market clearance, not necessarily a detailed performance validation against specific criteria.

Therefore, many of the requested sections (1, 2, 3, 4, 5, 6, 7, 8, 9) cannot be fully populated as the information is not present in the provided text.

However, I can extract the relevant available information:

1. Table of acceptance criteria and the reported device performance

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

• Sample Size (Test Set): Not specified in the provided text.
• Data Provenance: Not specified in the provided text.

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 text as no specific performance study is detailed with an independent ground truth establishment.

4. Adjudication method for the test set

• This information is not provided in the text as no specific performance study is detailed.

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 information about an MRMC study or AI assistance is present. This device is a measurement-only system and does not appear to involve AI or human interpretation in the context of diagnostic decision-making that would necessitate an MRMC study.

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

• The document implies standalone performance comparison to predicate devices, but no dedicated "standalone study" with detailed metrics is described. The device is a direct measurement tool, not an algorithm.

7. The type of ground truth used

• This information is not provided in the text as no specific performance study is detailed with an independent ground truth. The basis of equivalence is implied to be through comparison with the operational principles and intended use of existing predicate devices.

8. The sample size for the training set

• Not applicable/Not provided. The text does not describe a machine learning model or a training set.

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

• Not applicable/Not provided. The text does not describe a machine learning model or a training set.

Summary of what is provided:

The document is a 510(k) summary for the Q-Sweat Quantitative Sweat Measurement System. Its primary goal is to establish substantial equivalence to existing predicate devices.

• Intended Use: "To measure the sweat output of the skin of humans. This device does not make a diagnosis or indicate by itself that any disease state exists; it simply documents sweat output. This device is to be used in scientific studies of the anatomy, physiology, and biochemistry of the skin & associated structures."
• Comparison to Predicate Devices: The applicant claims the Q-Sweat operates in "nearly exact same fashion" as predicate devices and does not raise new questions of safety or effectiveness. The FDA's letter confirms substantial equivalence, allowing the device to be marketed.
• Device Type: A "measurement-only device" designed to measure the rate and volume of sweating. It captures a sample of sweat inside a small measuring chamber affixed to the skin and calculates moisture given off by the skin.

The absence of detailed performance study data, acceptance criteria, and ground truth establishment methods is typical for 510(k) applications that rely heavily on demonstrating substantial equivalence to already cleared devices, especially for devices where the technology and intended use are well-established. The equivalence claim focuses on the device's operational similarity and lack of new safety/effectiveness concerns, rather than a quantifiable performance comparison against specific metrics.

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