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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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The MaxxiGold Electrode is intended for non-invasive use as recording electrodes in studies of physiological signals. These devices are indicated for use with adult or pediatric patients during electroencephalography, including biofeedback, sleep studies and evoked potential recordings.

MAXXI GOLD Electrode is intended for non-invasive use as recording electrodes in studies of physiological signals. These devices are indicated for use with adult or pediatric patients during electroencephalography, including biofeedback, sleep studies and evoked potential recordings.

The product is offered in 3 different lenath sizes, 48", 60" or 96". The product is offered in package of 5 or 10 units, with multicolor wires.

The electrode is compatible with any recording device with the DIN 42-802 receptacle which is the gold standard for EEG and EMG recording machines.

This document is a 510(k) submission for a medical device called the "MaxxiGold Electrode," a type of cutaneous electrode used for recording physiological signals (e.g., EEG, biofeedback, sleep studies, evoked potentials).

A 510(k) submission aims to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving safety and efficacy from scratch through clinical trials. Therefore, the information provided focuses on the comparison to predicate devices and adherence to manufacturing standards, rather than extensive clinical study data as might be found for novel devices or PMAs.

Based on the provided text, here's an analysis of the requested information:

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

The document does not explicitly state "acceptance criteria" in the traditional sense of performance metrics with specific thresholds that the device must meet, followed by a report of the device's performance against those thresholds. Instead, the submission relies on demonstrating substantial equivalence to existing predicate devices based on Intended Use and Technological Characteristics.

The implicit "acceptance criteria" for a 510(k) in this context are:

• Identical or highly similar Indications for Use.
• Highly similar technological characteristics, or if different, that the differences do not raise new questions of safety and effectiveness.

Implicit Acceptance Criteria (for 510(k) Substantial Equivalence)

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

The document explicitly states: "No clinical trial was performed." This indicates that there was no "test set" in the sense of patient data collected for a performance study. The data provenance is therefore not applicable as no clinical data was generated for this 510(k) submission. The basis for the submission is a comparison of design and specifications to predicate devices.

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, as no clinical trial or test set requiring expert ground truthing was performed. The "ground truth" for a 510(k) is typically the established safety and effectiveness of the identified predicate device(s).

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

Not applicable, as no clinical trial or test set requiring adjudication was performed.

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. This device is a passive electrode, not an AI-assisted diagnostic tool.

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

Not applicable. This device is a passive electrode, not an algorithm.

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

For this 510(k) submission, the "ground truth" is the legally marketed status and established safety and effectiveness of the predicate devices. The submission argues that because the new device is fundamentally the same as these predicates in terms of intended use and technological characteristics, it can also be considered safe and effective without requiring new clinical data.

8. The sample size for the training set

Not applicable, as no algorithm or machine learning model was developed or trained. This is a hardware electrode device.

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

Not applicable, as no algorithm or machine learning model was developed or trained.

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BWMini is an electroencephalograph, which is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.

BWMini is multi-channel (up to 32 channels) system designed for Electroencephalograph (EEG), Polysomnography (PSG) and Home Sleep Testing (HST) recording application, in research, home sleep studies, ambulatory and clinical environments.

The BWMini does not make any judgment of normality of the displayed signals or the results of an analysis. In no way are any of the functions represented as being in and of themselves diagnostic.

BWMini is an electroencephalograph, which is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.

BWMini is multi-channel (up to 32 channels) system designed for Electroencephalograph (EEG), Polysomnography (PSG) and Home Sleep Testing (HST) recording application, in research, home sleep studies, ambulatory and clinical environments.

The BWMini does not make any judgment of normality of the displayed signals or the results of an analysis. In no way are any of the functions represented as being in and of themselves diagnostic.

The device is software based (Windows), uses a memory card for data storage, has a hard plastic external material, and is powered by batteries. It has up to 32 channels, 16 Bits AD Resolution, 50-60Hz Notch Filter, 1-500uv/mm Configurable Sensitivity Selection, 0.16 - 15Hz Configurable Low Frequency filters, 15 - 100 Hz Configurable High Frequency filters, and 2 DC Inputs. The user interface is IBM PC.

The Neurovirtual BWMini EEG, HST, and PSG device is not an AI/ML powered device, therefore the typical acceptance criteria and study designs for such devices do not apply here. This document, K131335, is a 510(k) premarket notification for an electroencephalograph (EEG) device, which is a traditional medical device for measuring and recording electrical activity of the brain. The "acceptance criteria" and "study" described herein relate to the device's technical specifications and compliance with established medical device standards and performance testing, rather than AI model performance metrics like sensitivity, specificity, or AUC.

Here's a breakdown of the information provided, framed within the context of a traditional medical device submission:

1. Table of Acceptance Criteria and Reported Device Performance

For traditional medical devices like the BWMini, "acceptance criteria" refer to the specified technical performance parameters the device must meet, often derived from industry standards or clinical guidelines. "Reported device performance" then details how the device performed against these criteria in non-clinical testing.

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

This is a non-clinical device performance test, not a clinical trial with patient data.

• Sample Size for Test Set: Not applicable in the traditional sense of patient data. The "test set" here refers to the device units themselves that underwent performance testing. The document states "We test the produced units for performance..." implying testing was conducted on one or more manufactured units of the BWMini. The exact number of units or test runs is not specified but is typically part of a detailed test report (which is referenced as "ATTACHMENT 7").
• Data Provenance (e.g., country of origin of the data, retrospective or prospective): Not applicable as this is hardware/software performance testing, not data collection from patients. The testing was conducted by Neurovirtual USA, Inc.

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

Not applicable for this type of device performance test. The "ground truth" for these tests comes from known, calibrated external signal sources as described in Section J: Non-clinical Testing. The acceptance criteria themselves are based on established guidelines (e.g., ACNS Guideline) and widely accepted principles from "principal EEG authors" rather than expert consensus on specific cases.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

Not applicable. Device performance testing against calibrated sources does not involve adjudication by multiple experts. The comparison is objective: device output vs. known input from a calibrated source, with a defined acceptable deviation.

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. This device is an acquisition system, not an AI-powered diagnostic tool that assists human readers. It records physiological signals. There is no AI component mentioned or implied in this 510(k) submission. Therefore, no MRMC study or effect size related to AI assistance would be conducted.

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

Not applicable. The BWMini is an electroencephalograph (EEG), polysomnograph (PSG), and home sleep testing (HST) recording application. It is a hardware and software system for data acquisition. The submission explicitly states: "The BWMini does not make any judgment of normality of the displayed signals or the results of an analysis. In no way are any of the functions represented as being in and of themselves diagnostic." This confirms it is not an algorithm that performs standalone diagnoses or interpretations.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

For the non-clinical testing of the frequency response, the ground truth was data from a known and calibrated external source. This means a signal generator or similar equipment was used to inject precise, known frequencies and amplitudes into the device, and the device's recorded output was compared against these known inputs.

8. The Sample Size for the Training Set

Not applicable. This device is not an AI/ML device and therefore does not have a "training set" in the context of machine learning.

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

Not applicable, as there is no training set for an AI/ML algorithm.

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The BWIII system may be used for electroencephalography (EEG) and sleep recordings (Polysomnography) in research and clinical environments. It acquires displays and archives EEG and PSG data for on-screen review, annotation, and event-marking by the user.

The BWIII requires competent user input, and its output must be reviewed and interpreted by a trained physician who will exercise professional judgment in using this information.

The BWIII does not make any judgment of normality or abnormality of the displayed signals or the results of an analysis. In no way are any of the functions represented as being in and of themselves diagnostic.

BWIII is multi-channel (up to 50 channels) system designed for polysomnography (PSG) and electroencephalograph (EEG) recording application, in sleep lab, hospital or clinical environment under the supervision of a physician, using a laptop or a desktop computer.

The BWIII system consists of four major components: the amplifier unit, head box unit, flash stimulator unit and the power module (all plastic made). The system provides connections for electrodes and sensors, and connects to the computer using an ethernet cable.

The BWIII works with any good quality patient leads / electrodes and sensors snore, flow, effort belts and position) that have the safety touch connectors and are legally marketed in accordance with FDA requirements. As these accessories are already legally in the market from different manufactures, they are not part of this submission.

The BWIII does not make any judgment of normality of the displayed signals or the results of an analysis. In no way are any of the functions represented as being in and of themselves diagnostic.

The provided text is a 510(k) summary for the Neurovirtual BWIII device, an electroencephalograph (EEG) and polysomnography (PSG) system. It focuses on establishing substantial equivalence to previously cleared devices rather than providing detailed acceptance criteria or a comprehensive study demonstrating device performance against specific metrics.

Therefore, many of the requested elements are not present in the provided document.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

This information is not provided in the document. The 510(k) summary establishes substantial equivalence by comparing the technological characteristics of the BWIII to predicate devices (Neurovirtual BWII, Cadwell Easy Write, Excel Tech PSG-40). It does not specify quantitative acceptance criteria for performance metrics (e.g., accuracy, sensitivity, specificity) and then report how the BWIII met those criteria. The comparison tables provided here focus on features like number of channels, filter ranges, etc., demonstrating similar specifications, not performance against pre-defined clinical acceptance criteria.

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

This information is not provided. The document does not describe a clinical test set with specific data or provenance.

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

This information is not provided. As there's no described clinical test set, there's no mention of experts establishing ground truth for it.

4. Adjudication Method for the Test Set:

This information is not provided.

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

An MRMC study is not mentioned or described. The document does not discuss human reader performance, with or without AI assistance. The BWIII is a data acquisition and display system; it does not inherently involve AI for interpretation or assistance for human readers in the way an AI diagnostic algorithm would.

6. Standalone (Algorithm Only) Performance:

This is not applicable in the context of this device. The BWIII is a hardware/software system for acquiring, displaying, and archiving EEG/PSG data. It explicitly states: "The BWIII does not make any judgment of normality of the displayed signals or the results of an analysis. In no way are any of the functions represented as being in and of themselves diagnostic." Therefore, there is no "algorithm only" performance to evaluate in a standalone manner for diagnostic purposes.

7. Type of Ground Truth Used:

This information is not provided.

8. Sample Size for the Training Set:

This information is not provided. The device does not appear to involve machine learning models that require training sets in the typical sense of AI-driven diagnostic tools.

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

This information is not provided.

Summary of the Study and Evidence Presented in the Document:

Instead of a study demonstrating performance against acceptance criteria, the document indicates that safety and effectiveness are established through:

• Substantial Equivalence: The primary method for clearance is by demonstrating the BWIII's substantial equivalence to predicate devices (K932407, K991900, K062533) in terms of its technological characteristics, intended use, and performance specifications (as detailed in the comparison tables). This means showing that the device is as safe and effective as existing legally marketed devices.
• Compliance with Standards: The BWIII is stated to be in compliance with a list of applicable international and national standards for medical device equipment, safety, electromagnetic compatibility, software validation, and quality management systems (e.g., IEC 60601 series, EN ISO 14971, EN ISO 13485, FDA Guidance on Software Validation).
• Non-clinical Testing (Referenced Article): The document states: "In order to demonstrate that the use of EEG / PSG equipment is safe and effective, we have compiled one article from the Journal of Epilepsy and Clinical Neurophysiology (J Epilepsy Clin Neurophysiol 2004; 10(4):191-200) that demonstrates the safety and effectiveness regarding the equipment." The article, "Nonconvulsive Status Epilepticus: Clinical and Electrographic Aspects," is attached as "ATTACHMENT 05" (though the content of the article itself is not provided in the prompt). This suggests the article serves as general scientific evidence supporting the utility and safety of EEG/PSG equipment in clinical practice, rather than a specific study of the BWIII device's performance against defined acceptance criteria.

In conclusion, for the Neurovirtual BWIII, the provided 510(k) summary primarily relies on:

• Comparison to Predicate Devices: Demonstrating that its technical specifications and intended use are similar to already cleared devices.
• Adherence to Recognized Standards: Ensuring the device meets fundamental safety and operational requirements.
• General Scientific Literature: Referencing an external publication to broadly support the safety and effectiveness of the type of equipment.

It does not present a controlled study with specific acceptance criteria, a defined test set of data, expert ground truth establishment, or performance metrics typically associated with evaluating AI/diagnostic devices.

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The BWII system may be used for electroencephalography (EEG) and sleep recordings (polysomnography) in research and dinical environments. It acquires, displays, and archives EEG and PSG data for on screen review, annotation and event marking by the user. The BWII system also allows for computer-assisted, usercontrolled quantitative analysis of EEG.

The BWII system is not intended to replace conventional devices or methods used for EEG and PSG monitoring in critical care or intraoperative settings.

The BWII system requires competent user input, and its output must be reviewed and interpreted by a trained technicians or neurologists who will exercise professional judgment in using this information.

The BWII system does not make any judgment of normality or abnormality of the displayed signals or the results of an analysis. In no way are any of the functions represented as being in and of themselves diagnostic.

BWII is multi-channel (up to 25 channels) system designed for electroencephalograph (EEG) and polysomnography (PSG) recording application, in sleep lab, hospital or dinical environment under the supervision of a physician, using a lap top or a desk top computer.

The BWII system consists of three maior components: the Amplifier Unit, the Power Module (Both plastic made) and the BWAnalysis Software (a CD to be installed on a regular PC). The system provides connections for electrodes and sensors, and connects to the computer using an Ethernet cable.

The BWII works with any good quality patient leads / electrodes and sensors (snore, flow, effort belts and position) that have the safety touch connectors and are legally marketed in accordance with FDA requirements. As these accessories are already legally on the market from different manufactures, they are not part of this submission.

The BWII does not make any judgment of normality of the displayed signals or the results of an analysis. In no way are any of the functions represented as being in and of themselves diagnostic.

This document is a 510(k) premarket notification for the Neurovirtual BWII Digital (Amplifier and Power Module) and BWAnalysis (Software), an electroencephalograph (EEG) and polysomnography (PSG) recording system. The submission aims to demonstrate substantial equivalence to previously cleared devices.

Based on the provided text, a formal study demonstrating the device meets specific acceptance criteria is not present. Instead, the document focuses on establishing substantial equivalence to predicate devices and adherence to relevant safety and quality standards.

Here's an analysis of the requested information based on the provided text:

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

No explicit table of acceptance criteria and reported device performance is included in this 510(k) summary. The submission primarily uses a comparison chart (Section J) to demonstrate technological characteristics are similar to predicate devices, rather than outlining specific performance metrics and acceptance thresholds.

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. The document does not describe a clinical study with a test set of data for performance evaluation. The comparison is based on technological characteristics of the device itself and predicate devices.

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. No ground truth establishment by experts for a test set is described.

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

Not applicable. No test set adjudication method is described.

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. This device is an EEG/PSG recording and analysis system, not an AI-assisted diagnostic tool that would typically involve a multi-reader multi-case comparative effectiveness study with human readers. The document explicitly states: "The BWII does not make any judgment of normality of the displayed signals or the results of an analysis. In no way are any of the functions represented as being in and of themselves diagnostic."

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

Not applicable. As noted above, the device does not make diagnostic judgments on its own. It's a data acquisition and display system with user-controlled analysis.

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

Not applicable. There is no mention of a ground truth used for performance validation in the context of a clinical study. The device's safety and effectiveness are established through compliance with standards and substantial equivalence to predicate devices, not by direct comparison to a clinical ground truth.

8. The sample size for the training set

Not applicable. This document does not describe a machine learning algorithm that would require a training set.

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

Not applicable. No training set is mentioned in the context of this device.

Summary of the Study and Acceptance Criteria from the Text:

The "study" presented in this 510(k) summary is primarily a technological characteristics comparison study and a standards compliance declaration.

• Acceptance Criteria (Implied):

  • Substantial Equivalence: The primary acceptance criterion for this 510(k) is demonstrating that the BWII Digital and BWAnalysis system is substantially equivalent to legally marketed predicate devices (Cadwell Easy Write and Easy Reader, Excel Tech PSG-40, Excel Tech Neuro Works, Persyst Prism). This implied acceptance is based on similar intended use, technological characteristics, and safety profiles.
  • Compliance with Standards: The device must comply with a list of recognized national and international safety and quality standards (e.g., EN 60601-1, EN 60601-1-1, EN 60601-2, EN 60601-2-26, EN 60601-1-4, EN ISO 14971, EN 13485, FDA Guidance on Software Validation).

• Reported Device Performance:

  • The document provides a comparative table (Section J) outlining the technological characteristics of the BWII system against its predicate devices. This includes hardware specifications (application, number of channels, data communication, material, power source) and software features (platform, impedance, calibration, spectrum analyzer, montage editor). The "performance" is implicitly demonstrated by showing that these characteristics are comparable or equivalent to those of the predicate devices.
  • The manufacturer declares compliance with the listed safety and quality standards, implying that the device meets the performance and safety requirements outlined in those standards.

In essence, the submission does not detail experimental performance data against predefined statistical acceptance criteria typical of some AI/diagnostic device clearances. Instead, it relies on demonstrating that the device is functionally similar, equally safe, and meets the same fundamental technical requirements as other legally marketed devices, as assessed against established medical device standards.

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