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The intended use of the HHSsystems QANS (Quantitative Autonomic Nervous System) Monitoring System:

OANS (Quantitative Autonomic Nervous System) Monitoring System is a physiological monitoring device that collects and transmits measurements (heart rate, respiration rate, and muscle activity) using sensors. The QANS monitoring system monitors the sympathetic and parasympathetic nervous system using accurate recordings of heart rate (HR) from EKG and provides statistical data as well as heart rate variability (HRV) data which the clinician can use it make clinical assessments of medical, dental or peak performance interventions.

The physiological information can be used as a general patient monitoring system as well as for research. QANS is not intended to produce any interpretation of the measurements obtained and does not provide any type of diagnosis.

Patients should be 5 years old or older.

The HHSsystems QANS Monitoring System is the same as that of the Thought Technology devices as the HHSsystems QANS uses the same hardware as the previously cleared ProComp Infiniti, FlexComp Infiniti, ProComp5 and ProComp2 devices with the same BioGraph Infiniti and CardioPro software.

This information is contained in 510(k)'s K903497 for the ProComp Infiniti, FlexComp Infiniti, ProComp5 and ProComp2 and in K972723 for the BioGraph Infiniti and CardioPro software.

The technology and the method of monitoring heart rate, respiration rate, and muscle activity, are essentially the same as those of the other legally marketed predicate devices.

QANS (Quantitative Autonomic Nervous System) Monitoring System is a computer-based system for measurement of Heart Rate Variability (HRV) in response to paced respiration. It is comprised of off-the-shelf Personal Computers (PCs) and special purpose hardware capable of acquiring, storing, analyzing and reporting ECG data from cardiac monitoring devices, respiration rate and muscle activity, using sensors designed to monitor these functions. Data is acquired using the Thought ProComp Infiniti, FlexComp Infiniti, ProComp5 and ProComp2 and in K972723 for the BioGraph Infiniti and CardioPro software.

The analysis algorithms identify heart rate variability (HRV) patterns that reflect transient decelerations and/or reduced baseline variability. QANS evaluates the variation of the heart rate, both in the time domain (statistical methods) and in the frequency domain (spectral analysis). Each QRS complex (Q, R and S wave deflections on a ECG) detected and the so-called normal-to-normal (NN) or Rate-to-Rate (RR) intervals between adjacent QRS complexes are resulting from sinus node depolarization. The system performs a fully automated quantitative analysis of HRV based on data collected by an FDA-compliant R Wave Trigger device. The system presents the results to the healthcare provider through a computerbased user interface. The system has patient data management capability.

The provided document is a 510(k) summary for the QANS (Quantitative Autonomic Nervous System) Monitoring System. It mainly focuses on demonstrating substantial equivalence to predicate devices, rather than a detailed study proving the device meets specific acceptance criteria through a standalone performance study.

Based on the information provided, here's a breakdown of the requested data:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not present a formal table of acceptance criteria with corresponding performance metrics from a specific study of the QANS device itself. Instead, it relies on the safety and accuracy of its predicate devices and the fact that the QANS system uses the same hardware and software as those predicates.

1. Thought Technology ProComp Infiniti, FlexComp Infiniti, ProComp5 and ProComp 2 Encoder systems --K903497.
2. using BioGraph Infiniti and CardioPro software -- K972723."
   "The HHSsystems QANS uses the same hardware as the previously cleared ProComp Infiniti, FlexComp Infiniti, ProComp5 and ProComp2 devices with the same BioGraph Infiniti and CardioPro software."
   "The technology and the method of monitoring heart rate, respiration rate, and muscle activity, are essentially the same as those of the other legally marketed predicate devices." |
   | Functional Equivalence to Predicate Monitoring Functions | "For monitoring functions:
3. HeRO ™ HRV analysis system (510(k) K021230).
4. Intelwave Heart Rate Variability System (510(k) K062068)"
   "All of the parameters that are recorded by the QANS system are recorded by the Thought Technology devices." |
   | Compliance with Regulations | "This system was developed in accordance with 21CFR820 Quality System Regulation and has been tested in accordance with UL 60601-1 and IEC 60601-1-2." |
   | Functionality Verification and Validation | "All HHSsystems QANS functionality has been verified and validated both as part of the BioGraph and the QANS application suite." |

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

The document does not describe a separate "test set" for the QANS device's performance study. Instead, it relies on the testing and validation performed on the predicate devices (Thought Technology ProComp Infiniti, FlexComp Infiniti, ProComp5 and ProComp2, and the BioGraph Infiniti and CardioPro software). Therefore, details on the sample size and data provenance for a distinct QANS test set are not available in this document.

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

This information is not provided as the document defers to the existing validation of the predicate devices. There is no mention of a ground truth establishment process specifically for the QANS device's performance in this summary.

4. Adjudication Method for the Test Set:

This information is not provided as there is no specific test set or adjudication process described for the QANS device's performance in this summary.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size:

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study is not mentioned in the document. The filing focuses on substantial equivalence based on identical hardware and software with predicate devices, not on the impact of the QANS system on human reader performance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

While the document states that "All HHSsystems QANS functionality has been verified and validated," it does not explicitly detail a standalone algorithm-only performance study with quantitative metrics. The emphasis is on the QANS system using the same hardware and software as the already cleared predicate devices, implying that the standalone performance is inherited from these predicates. The device itself is described as a physiological monitoring device that provides data for clinicians to make assessments, rather than a device with an AI algorithm providing interpretations or diagnoses.

7. The Type of Ground Truth Used:

The document does not describe a specific ground truth used for a performance study of the QANS device. Its substantial equivalence argument relies on the inherent accuracy and safety of the predicate devices that the QANS system directly incorporates. The predicate devices, such as the Thought Technology systems, would have undergone their own validation against established physiological measurement standards (e.g., comparison to gold-standard ECGs for heart rate, respiratory monitors for respiration rate).

8. The Sample Size for the Training Set:

This information is not applicable/provided. The QANS system uses the same software as predicate devices (BioGraph Infiniti and CardioPro software). Therefore, if any "training" (in the context of machine learning) occurred, it would have been part of the development and validation of the predicate software, not separately for QANS. The document does not indicate that the QANS device itself utilizes a machine learning algorithm that requires a "training set." It processes physiological measurements and calculates statistical data and HRV.

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

This information is not applicable/provided for the same reasons as point 8. The device's functionality is based on established methods of physiological measurement and calculation, not on a machine learning model trained on a specific dataset with ground truth established for that training.

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