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510(k) Data Aggregation
(324 days)
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
| Performance Test | Acceptance Criteria (Predicate Specs) | Maxxi Position Results (Reported Performance) |
|---|---|---|
| Sensor Dimensions | 40x29x14 mm Allowance: +-10% | Measured Value: 39x31x15 mm Pass (x) Fail ( ) |
| Cable Dimensions | Cable length: 7ft Allowance: +-5% | Measured Value: 7ft Pass (x) Fail ( ) |
| Cable Connectivity | Not allowed false contact, or no connectivity. | Pass (x) Fail ( ) |
| Sensor Visual Aspects | The visual aspects of the Maxxi Position must be similar/equivalent to the predicate device Ultima Body Position Sensor. | Pass (x) Fail ( ) |
| Sensor Signal Aspect | Qualitative signal analysis must be similar in signal type, amplitude and linearity when compared with the predicate device Ultima Body Position Sensor. | Pass (x) Fail ( ) |
| Sensor Functional Aspect | All positions signal shown in the user manual must follow the patient body position based on the sensor axis position | Pass (x) Fail ( ) |
| Accuracy in Simulated Use | 100% accuracy (implied by "100% of accuracy between them" when compared to predicates) | 100% of accuracy between devices |
| Latency in Simulated Use | Latency differences should be negligible/acceptable (implied by 1 second difference) | Average time to detect patient body position was 1 second of latency difference |
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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