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The disposable AlphaMed Inc. Breathing/Snoring Sensors and Snoring Sensor are used with existing recording devices in support of diagnostic recording of nasal and oral airflow and recording of breath sounds. The sensors are used with patients who require a sleep study recording. The sensors are contraindicated for support of apnea monitoring.

The Breathing/Snoring Sensors and Snoring Sensors are disposable, single patient use devices. The Breathing/Snoring Sensors combine breathing and snoring sound sensing capabilities in one sensor. The sensors use a self-generating signal technology. The sensors contain no electronics or software and require no special interface electronics. The sensors attach to the patient with medical grade tape. The Breathing/Snoring Sensors are applied under the nostrils and above the lip. The Breath Sounds sensors are applied to the throat of the patient. The sensors connect to existing recording equipment by means of an AlphaMed Adapter Cable. The existing recording equipment amplifies the signal the sensors generate in response to a temperature change and/or a vibration due to breath sounds. The existing recording device provides patient electrical isolation for the applied sensors. The sensors consist of a temperature and vibration sensitive material covered with conductive ink to collect the electrical charge generated by the temperature and vibration sensitive material. The conductive ink surface has electrodes lead wires attached. The lead wires comply with 21 CFR Part 898 Performance Standards for Electrode Lead Wires and Patient Cables. The lead wires interface to existing sleep recording devices by a supplied Adapter Cable. Different Adapter Cables are available to support interface to various sleep recording instruments.

1. Table of Acceptance Criteria and Reported Device Performance:

Note: The provided document does not specify quantitative acceptance criteria or detailed performance metrics. It indicates that "Laboratory data are presented to establish the performance."

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

The document mentions "Laboratory data are presented to establish the performance." However, it does not provide details on the specific sample size used for the test set or the provenance of this data (e.g., country of origin, retrospective or prospective).

3. Number of Experts and Qualifications for Ground Truth:

The document does not specify the number of experts used to establish ground truth for a test set or their qualifications. It states that "Trained professionals (physicians, clinicians) interpret the recorded signals in support of sleep study diagnosis" when discussing the intended use of the device, but this refers to the interpretation of signals in clinical practice rather than the establishment of ground truth for a device study.

4. Adjudication Method for the Test Set:

The document does not mention an adjudication method for a test set.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it discuss the effect size of human readers improving with or without AI assistance. The device itself is a sensor, not an AI software.

6. Standalone (Algorithm Only) Performance Study:

The document does not describe a standalone (algorithm only) performance study. The device is a physical sensor (breathing/snoring sensor) and contains "no electronics or software." Therefore, a standalone algorithm performance study would not be applicable.

7. Type of Ground Truth Used:

The document refers to "Laboratory data" to establish performance. It also mentions that "Trained professionals (physicians, clinicians) interpret the recorded signals in support of sleep study diagnosis." However, it does not explicitly state the type of ground truth used for the device's validation (e.g., expert consensus, pathology, outcomes data).

8. Sample Size for the Training Set:

The document does not provide information regarding a training set sample size, as the device is a physical sensor and not a software algorithm that would typically require a training set.

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

This question is not applicable as the device is a physical sensor and not an AI/algorithm-based system requiring a training set with established ground truth.

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