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The Maxxi Flow sensor is a respiratory sensor. It is placed under the patient's and produce a signal that is directly proportional to the temperature changes of air inhaled and exhaled during respiration. Maxxi Flow sensors generate a small analog electrical signal that provides a clear, reliable indication of respiration airflow. This sensor is intended to be used with polysomnography devices and adult patients.

Intended to be use in a sleep laboratory, clinics or hospitals.

MAXXI FLOW SENSOR Thermocouple based sensors detect change of breath temperature between ambient temperature (inhalation) and lung temperature (exhalation). A thermocouple placed in front of a nostril detects breathing as a temperature change.

The product is offered in 2 different length sizes, 3ft and 7ft.

Maxxi Flow sensor is compatible with Neurovirtual PSG devices.

Here's an analysis of the acceptance criteria and study information for the Maxxi Flow Sensor, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance for Maxxi Flow Sensor (K201495)

The Neurovirtual Maxxi Flow Sensor is a respiratory thermocouple flow sensor intended for use with polysomnography devices in adult patients within sleep laboratories, clinics, or hospitals. It is designed to detect temperature changes of inhaled and exhaled air to provide a signal indicating respiration airflow.

Here's the detailed breakdown of its acceptance criteria and the study that proves its compliance:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document does not explicitly state the specific sample size of Maxxi Flow Sensors used for the non-clinical testing. However, the tests are described as comparing the Maxxi Flow sensor directly against a single predicate device (Easy Flow manufactured by S.L.P Ltd.). This suggests a comparative test design.

The provenance of this test data is non-clinical testing, performed by the manufacturer, Neurovirtual USA, Inc., implying internal validation. There is no information regarding the country of origin of the data beyond the manufacturer's location in Fort Lauderdale, Florida, USA. The testing appears to be prospective in nature, as it involves newly manufactured devices undergoing specific tests.

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

This information is not provided in the document. For this type of non-clinical, performance-based testing of a sensor, "expert ground truth" as typically understood in AI/image analysis studies (e.g., radiologist consensus) is not directly applicable. The "ground truth" for these tests is based on objective measurements against engineering specifications and comparison to a legally marketed predicate device.

4. Adjudication Method for the Test Set:

An adjudication method is not applicable for these non-clinical, objective performance tests. The criteria are quantitative (e.g., +/- 10% allowance, 0-10 ohms) or qualitative but directly observable (e.g., visual equivalency). Discrepancies would be identified through measurement, not through expert opinion requiring adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted. This type of study is relevant for evaluating the impact of AI algorithms on human readers' performance, typically in diagnostic imaging. The Maxxi Flow Sensor is a physical medical device (a respiratory sensor), not an AI-powered diagnostic tool, so such a study would not be applicable.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

A standalone performance study, as typically understood for AI algorithms, was not conducted because the Maxxi Flow Sensor is a physical device, not an AI algorithm. Its performance is inherent to its physical and electronic design and is measured directly through non-clinical testing. The performance evaluated here is the device's ability to generate signals based on temperature changes.

7. Type of Ground Truth Used:

The "ground truth" for the non-clinical testing was based on:

• Engineering Specifications: Pre-defined acceptable ranges for physical dimensions, cable length, and sensor resistance.
• Predicate Device Performance: Direct comparison of output signals (frequency and amplitude) against a legally marketed predicate device (S.L.P. Ltd.'s Thermocouple Flow Sensor (Easy Flow)) under controlled conditions.
• Visual Inspection Criteria: Equivalence to the predicate device in visual aspects.

8. Sample Size for the Training Set:

This information is not applicable as the Maxxi Flow Sensor is a physical medical device, not an AI/machine learning model that requires a training set.

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

This information is not applicable as there is no training set for a physical medical device like the Maxxi Flow Sensor.

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The Maxxi Position Sensor is intended for use with the BWMini polysomnograph system, to acquire the body position of adult patients during sleep studies. The Sensor produces signals for five positions: supine, prone, left and right. It is intended for use in research, home sleep studies, ambulatory, and clinical environments.

The Maxxi Position Sensor is a sensor that attaches to either around chest or abdominal belts using velcro tapes- so no additional belts or attachment systems are needed. The sensor features a fully encapsulated active element for troublefree cleaning. The Maxxi Position Sensor produces a clear and reliable signal for five positions: Upright, supine, prone, left and right. It comes with 7ft long cable for a convenient connection with the PSG device recorder.

The provided text describes the Neurovirtual Maxxi Position Sensor, a device intended to acquire body position data during sleep studies. Here's a breakdown of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance for Test Set

• Sample Size: N=20 volunteers were used in the simulated use study.
• Data Provenance: Not explicitly stated, but the company is Neurovirtual USA, Inc., located in Fort Lauderdale, Florida, implying the study was likely conducted in the USA. The study design is prospective as it involved volunteers in a "simulated use" study.

3. Number of Experts and Qualifications for Ground Truth

The document does not mention the use of experts to establish ground truth for the simulated use study. The ground truth appears to be based on direct observation of the actual body position of the volunteers.

4. Adjudication Method

Not applicable, as ground truth was established by direct observation of body position, not by expert review requiring adjudication.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC study was performed or mentioned. The primary comparison in the simulated use study was between the Maxxi Position Sensor and predicate devices, focusing on accuracy and latency of position detection.

6. Standalone Performance Study

Yes, a standalone performance study was done for the algorithm/device. The "Performance Testing" section details tests where the Maxxi Position Sensor's characteristics (dimensions, cable integrity, visual aspects, signal aspects, functional aspects) were directly verified against acceptance criteria derived from predicate device specifications. The "Performance Testing in Simulated Use" also assessed the device's functional performance in detecting body position.

7. Type of Ground Truth Used

For the "Performance Testing in Simulated Use," the ground truth was direct observation of patient body position (as the sensor's output was compared to the actual position of volunteers). For the other "Performance Tests" (Sensor Dimensions, Cable Dimensions, Cable Connectivity, Sensor Visual Aspects, Sensor Signal Aspect, Sensor Functional Aspect), the ground truth was based on physical measurements, qualitative assessment against predicate devices, and internal functional verification according to predefined specifications.

8. Sample Size for Training Set

The document is for a 510(k) premarket notification for a hardware device (sensor) and does not describe an AI/algorithm that requires a "training set" in the typical sense for machine learning. The device uses "a combination of electronic tilt switches [that] generates different types of signals depending on the sensor position" for its core function. Therefore, there is no mention of a training set as would be relevant for a machine learning model.

9. How Ground Truth for Training Set was Established

Not applicable, as there is no mention of a training set for an AI/machine learning model. The device operates based on physical principles of tilt switches.

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The Maxxi Snore Sensor is a device intended to acquire snore bursts. It responds to snoring and other sounds in the audio range picked up through the skin and convert them to a small analog voltage that provides an indication of the presence of these sound/vibration bursts. This sensor is intended to be used with polysomnography devices and adult patients. Intended to be use in a sleep laboratory, clinics or hospitals.

MAXXI SNORE SENSOR piezo electric based sensors detect sound and vibration of the snore through skin contact. The sensor is placed on the patient neck surface where it can easily detect the snoring bursts during the sleep study. The product is offered in 2 different length sizes, 3ft and 7ft. The sensor is compatible with any recording device with the DIN 42-802 receptacle which is the gold standard for PSG recording machines.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Maxxi Snore Sensor:

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided text does not explicitly state the sample size used for the performance testing.
The data provenance is also not explicitly stated beyond comparing the Maxxi Snore Sensor to a predicate device. It is indicated as performance testing rather than a study on 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. The performance testing appears to be a direct comparison of physical properties and signal output against a predicate device, rather than a clinical study requiring expert ground truth for interpretation.

4. Adjudication Method for the Test Set

This information is not provided. Given the nature of the tests (dimensions, cable length, visual inspection, signal comparison against a predicate), a formal adjudication method by experts is not described or implied.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

A MRMC comparative effectiveness study was not done. The device is described as a sensor that outputs analog voltage for polysomnography devices, not an AI-assisted diagnostic tool for interpretation by human readers.

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

A standalone performance study of an algorithm was not done. The device is hardware. The performance described is related to its physical characteristics and direct signal output, not algorithmic interpretation.

7. The Type of Ground Truth Used

The "ground truth" used for performance testing was the specifications and output of a legally marketed predicate device (S.L.P. Ltd.'s Snoring Sensor K941759). The Maxxi Snore Sensor's performance was evaluated against the predicate device to demonstrate substantial equivalence.

8. The Sample Size for the Training Set

This information is not applicable as the Maxxi Snore Sensor is a hardware device (piezoelectric sensor) that detects sounds/vibrations and converts them to an analog voltage. It does not involve machine learning or AI that requires a "training set."

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

This information is not applicable for the same reason as above; there is no training set for this type of device.

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Maxxi RIP Sensor is intended for measuring of respiratory effort signals. They function as accessories for sleep/ polysomnography (PSG) systems.

The device is offered in different sizes to be used on adult patients.

The intended environments are hospitals, institutions, sleep centers or sleep clinics.

Maxxi Rip Sensor is a device intended to capture respiratory effort from a patient and output the signal to a PSG device for sleep studies. This signal is captured using an elastic belt fastened around the thorax or abdomen that will exhibit a change in tension as the thorax or abdomen expands or contracts. This change in tension is measured and converted to a signal output by the interface and processed by a PSG device.

The product is composed of 3 major parts, the interface box, the cable and the belt.

Cables are used to connect between the respiratory effort sensor (RIP belts) and the applicable sleep recorder/polysomnography (PSG) system.

The device is offered in different sizes to be used on adult patients.

The Maxxi Rip was validated with Neurovirtual BWMini PSG device recorder only, therefore we don't quarantee compatibility with other devices.

The provided document describes the Neurovirtual Maxxi RIP Sensor, an accessory for sleep/polysomnography (PSG) systems intended for measuring respiratory effort signals. The document outlines performance testing conducted to demonstrate its safety and effectiveness.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

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

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

The document states: "The performance test is executed for certain % amount of the products manufactured." It does not specify the exact sample size for the individual performance tests (Belt Signal Verification, Belt Dimensions, etc.). No information is provided regarding the country of origin of the data, or whether the tests were retrospective or prospective.

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)

This section is not applicable as the performance tests described are for a physical device (Maxxi RIP Sensor) and its components, and do not involve human interpretation or a "ground truth" derived from expert consensus in the clinical sense. The "ground truth" here is the expected physical or electrical characteristic according to specifications.

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

Not applicable, as this is a physical device testing, not a clinical study involving human assessment of data. The pass/fail criteria are objective measurements.

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 Maxxi RIP Sensor is a hardware device for measuring respiratory effort. It is not an AI algorithm or a diagnostic tool that involves human readers or assessment of AI assistance.

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

Not applicable. This is a hardware device, not an algorithm.

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

The "ground truth" for the performance tests (e.g., Belt Dimensions, Cable Connectivity) is based on the pre-defined product specifications and engineering standards (e.g., 900mm-1200mm for belt length, continuity for cables, clean sine wave form for signal). For safety and EMC testing, it is compliance with international standards (IEC 60601-1, IEC 60601-1-2) and risk analysis (ISO14971). For signal quality, it is a comparison to the predicate device, although no specific metrics were reported.

8. The sample size for the training set

Not applicable. This is a hardware device and does not involve machine learning or training sets.

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

Not applicable. As there is no training set for this device.

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