Search Results

The Natus Quantum Amplifier is intended to be used as an electroencephalograph: to acquire, display, store and archive electrophysiological signals. The amplifier should be used in conjunction with Natus NeuroWorks™/SleepWorks™ software to acquire scalp and intracranial electroencephalographic (EEG) signals as well as polysomnographic (PSG) signals. The amplifier is designed to facilitate functional mapping using a Digital Switch Matrix. The Digital Switch Matrix portion of the headbox is a combination of hardware relays and software controls allowing the user (physician or technologist) to switch electrode pairs between the EEG recording amplifier and the external cortical stimulator for stimulus delivery.

The Natus Quantum Amplifier is intended to be used by trained medical professionals, and is designed for use in clinical environments such as hospital rooms, epilepsy monitoring units, intensive care units, and operating rooms. It can be used with patients of all ages, but is not designed for fetal use.

The Natus Quantum amplifier is comprised of a base unit and several breakout boxes. It is part of a system that is made up of a personal computer, a photic stimulator, an isolation transformer, video and audio equipment, networking equipment, and mechanical supports. The amplifier also contains an internal switch matrix to allow for a connection to an external cortical stimulator.

EEG and other physiological signals, from scalp electrodes, grid or needle electrodes, and other accessories such as pulse oximeters can be acquired by the Natus Quantum amplifier. These signals are digitized and transmitted to the personal computer running the Natus NeuroWorks software. The signals are displayed on the personal computer and can be recorded to the computer's local storage or to remote networked storage for later review.

The provided text describes the Natus Quantum Amplifier, an electroencephalograph, and its regulatory submission (K143440). However, the document does not contain a study that directly proves the device meets specific acceptance criteria in terms of clinical performance metrics like sensitivity, specificity, or accuracy.

The document focuses on demonstrating substantial equivalence to predicate devices (EMU128S and NeuroLink IP 256) primarily through technical specifications and compliance with various safety, EMC, and quality standards. The "Performance Tests" section is very brief and refers to non-clinical verification testing rather than clinical efficacy studies.

Therefore, the following information is based on what is available or can be inferred from the provided text. Many requested fields will be marked as "Not Applicable" or "Not Provided" because the document does not describe the kind of clinical study you're asking about.

1. Table of Acceptance Criteria and Reported Device Performance

Note on Acceptance Criteria: The document implies that meeting the specified technical characteristics that are substantially equivalent or superior to the predicate devices, and passing internal design verification tests, are the "acceptance criteria" for regulatory clearance based on substantial equivalence. It does not provide clinical acceptance criteria.

2. Sample size used for the test set and the data provenance

• Sample Size: Not Applicable. The document describes non-clinical verification testing of the device hardware/software, not a clinical study on patient data.
• Data Provenance: Not Applicable. No patient data was used for the described performance tests.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: Not Applicable. Ground truth for clinical data is not relevant to the described non-clinical verification tests.
• Qualifications of Experts: Not Applicable.

4. Adjudication method for the test set

• Adjudication Method: Not Applicable. No clinical test set requiring adjudication was 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

• MRMC Study: No. This document describes an EEG amplifier, not an AI-assisted diagnostic tool.

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

• Standalone Performance: Not Applicable. This is a hardware device (EEG amplifier) with associated software for data acquisition, display, storage, and archiving. It is not an algorithm for standalone diagnostic performance.

7. The type of ground truth used

• Type of Ground Truth: For the "Performance Tests" (Signal Quality Verification Test, Functionality Verification Test), the ground truth would be the design specifications and expected operational parameters of the device. These tests verify if the actual output matches the designed output. No clinical "ground truth" (e.g., pathology, outcomes data) for diagnosis is mentioned for these tests.

8. The sample size for the training set

• Sample Size: Not Applicable. This is not an AI/machine learning device that requires a training set.

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

• Ground Truth Establishment: Not Applicable. (See point 8)

Ask a specific question about this device

SleepSense sensors provide a qualitative measure of a patient's physiological parameters for recording onto an FDA-cleared data acquisition system. Their target population: Children and adult patients who are screened during sleep disorder studies. Their environment of use is usually at a sleep laboratory or sometimes at the patient's home.

Monitoring various physiological parameters is standard practice in sleep disorder testing. Standard overnight recordings show, among others, traces of parameters like respiration movement, leg and arm movement, snoring sounds, respiration airflow and body position during sleep.

In order to record tracings showing these parameters, sensors are needed to convert the physiological parameter into an electrical signal. These sensors are very simple sensing elements like piezo-crystals that convert mechanical force or vibrations to an electrical signal. Other sensing elements may be thermocouples which generate a signal proportional to temperature, or gravity switches, that switch and electrical circuit on and off depending on their position.

In practice, these sensing elements are packaged in small, patient-friendly enclosures which are applied to the patient, and connected to the recording system via a long and flexible cable. There is no electrical contact of any kind between the sensors and the patient.

All signals received from the sensors are qualitative, and are only used to record the dynamic nature or existence of the physiological parameter recorded. A specially trained sleep technician called "scorer" reviews the overall recording in the morning following the study. The signals recorded, together with additional channels like EKG or EEG, are analyzed to arrive at a diagnosis of a sleep disorder like sleep apnea or insomnia.

This 510(k) summary for the SleepSense Sleep Sensors outlines the device's classification, intended use, and substantial equivalence to predicate devices. However, it does not include any specific acceptance criteria or details of a study demonstrating the device meets such criteria.

The document states:

• "No performance standards are specified for physiological sensors for sleep disorder testing."
• The manufacturer claims substantial equivalence because they are the OEM manufacturer for the predicate devices.

Therefore, I cannot provide the requested information regarding acceptance criteria and the study that proves the device meets them because this information is not present in the provided text.

Based on the provided text, I can only confirm the following:

• 1. A table of acceptance criteria and the reported device performance: This information is not provided.
• 2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not provided.
• 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 provided.
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not provided.
• 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 provided. The device is a sensor, not an AI system for clinicians.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable, as it's a sensor providing qualitative signals for human interpretation.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not provided.
• 8. The sample size for the training set: Not applicable, as it's a sensor without a "training set" in the context of an algorithm.
• 9. How the ground truth for the training set was established: Not applicable.

Ask a specific question about this device