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The Delphi Amplifier is intended to be used by or under the direction of a physician for acquisition of EEG signals and to transmit them digitally to a computer. The device is intended for use on humans. The device is intended for use in a clinical environment (e.g., hospital, physician's office, etc.). The device is not intended for use in life support systems.

The Delphi Amplifier has been designed as a mobile recording device for EEG (electroencephalography) signals. It provides access to recorded data over a USB connection to external software over its signal driver interface. The Delphi Amplifier is powered via a USB connection and does not require any additional power source. The Delphi Amplifier supports EEG acquisition and storage by Referential DC input channels, and Parallel trigger input channels. The Delphi Amplifier enables connection of up to 48 referential channels and 8bit trigger input channel. Impedance values can be measured for all referential electrodes as well as the reference. An SDK (Software Development Kit) is available for direct amplifier communication. The device is powered by the USB port connection to a computer. The USB port supports interface to the computer running the SDK.

This document is a 510(k) summary for the Delphi Amplifier, a physiological signal amplifier, specifically for EEG signals. It primarily focuses on demonstrating substantial equivalence to a predicate device (eego amplifiers). This document describes non-clinical bench testing for technical performance and safety, but it does not describe a clinical study involving human patients, human readers, or the establishment of ground truth for a diagnostic AI algorithm. Therefore, many of the requested elements for describing an AI/ML-based diagnostic device's acceptance criteria and study proving its performance cannot be answered from this document.

However, I can extract the information related to the device's technical performance testing:

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

The document does not explicitly present a table of acceptance criteria for each test with numerical performance targets vs. reported values. Instead, it lists the tests performed and states a "Pass" result, indicating that the device met the underlying requirements of these standards.

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

• Sample Size: The document mentions "Multiple units tested" for reliability. For other tests, specific unit numbers are not provided, but the language implies testing on representative devices. This is not a study on clinical data, but rather on the device hardware/software itself.
• Data Provenance: Not applicable. The testing is bench testing of a physical device, not analysis of patient data.

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. This device is an amplifier for EEG signals, not an AI diagnostic algorithm that requires expert-established ground truth from clinical images or data. The "ground truth" for these tests is defined by the technical specifications and standards (e.g., IEC 60601 series).

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

• Not applicable. This is not a human assessment study.

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

• No. This is not an AI/ML diagnostic device requiring an MRMC study. It is a physiological signal amplifier.

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

• "Standalone performance" in this context would refer to the device's technical specifications and performance in measuring/amplifying EEG signals, which is what the bench tests (e.g., Impedance test, Noise test, CMRR) address. The software and firmware testing also falls under this. The document states these tests were passed.

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

• The "ground truth" for the performance of this device is defined by the established technical standards (e.g., IEC 60601 series for medical electrical equipment) and the manufacturer's own design specifications (e.g., "no errors encountered" for data packet loss, "operates per specifications" for software/firmware). It's a hardware/software performance "ground truth" rather than a clinical diagnostic "ground truth."

8. The sample size for the training set

• Not applicable. This is a hardware device (amplifier) with associated software/firmware, not a machine learning model that requires a training set of data.

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

• Not applicable. As above, this is not an ML model.

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The NExT Station and NExT Station Advanced are physiological signal amplifiers intended to be used during Electromyography (EMG) and Evoked Potential (EP) exams to acquire bioelectric signals produced by the patient's central and peripheral nervous system and muscles. The devices include Galileo NT Line software to record and display acquired data to aid the diagnosis and monitoring of potential disorders of the central and peripheral nervous system and muscles.

The devices include an electrical stimulator, an auditory stimulator, and a photic stimulator as accessories that may optionally be used in evoked response analysis.

EMG Family devices (NEXT Station and NEXT Station Advanced) are capable of detecting electrical signals produced by the central and peripheral nervous systems and by striated muscles. In connection with a PC, where the Galileo NT Line software is installed, they allow to perform both spontaneous and evoked electromyographic activity easily and in all their phases. Their fields of application are Electromyography (EMG) and Evoked Potentials (EP).

EB Neuro EMG Family devices include all the elements necessary for the acquisition of neurographic signals, for their analog-digital conversion and pre-processing, for audio monitoring, for the delivery of electrical, acoustic and visual stimulation. The functions of user interface, presentation, post-processing, storage, signals archiving and printing and data extracted from them are managed by a PC installed with the Galileo NT Line software.

Two versions are available depending on the support where the Medical Device is placed:

• “Mobile” is a mobile device placed on a cart, powered at 115V by Isolation Transformer and connected with a PC with Galileo NT Line medical device software running on the PC.
• “Portable” is a portable device placed on a desk, powered at 115V by medical power supply and connected with a PC with Galileo NT Line medical device software running on a Laptop PC.

This FDA 510(k) summary (K242832) for the EB Neuro S.p.A. NExT Station and NExT Station Advanced devices focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a performance study against predefined acceptance criteria for a new clinical claim. The summary explicitly states that:

"The subject devices are essentially a bundled together package of our own previously cleared predicate devices. The first predicate device Nemus 2 is an EMG amplifier hardware and the second predicate device Galileo NT is an EMG analysis software. They were previously 510(k) cleared on their own and are now being bundled together as a full EMG system.

No new features are added to the subject devices compared to the predicate devices. The subject devices essentially have the same design as the predicate devices."

Therefore, much of the requested information regarding acceptance criteria, study design, sample sizes, expert ground truth, adjudication, and MRMC studies is not applicable in the context of this 510(k) submission. The submission's primary purpose is to show that the new combined device performs substantially similarly to its already-cleared components and does not introduce new safety or effectiveness concerns.

However, I can extract information related to the device's technical specifications and the testing performed to ensure it meets general safety and performance standards relevant to medical devices:

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

Since this is a 510(k) for a bundled device with "no new features" in terms of clinical performance, there isn't a table of clinical acceptance criteria vs. device performance in the traditional sense of a de novo device. Instead, the acceptance criteria are met by demonstrating compliance with recognized standards and equivalence in technical specifications to the predicate devices. The performance is assessed against these standards and predicate device specifications.

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

This submission does not discuss a clinical test set or data provenance in the context of diagnostic performance. The "testing" mentioned refers to engineering verification and validation (V&V) to ensure compliance with standards and the predicate device's technical specifications.

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 there was no clinical study comparing device findings to a ground truth established by experts.

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

Not applicable.

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 physiological signal amplifier and software system, 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 measurement and display system for physiological signals, intended for use by qualified medical professionals.

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

Not applicable. The performance is assessed against technical standards and the specifications of predicate devices, not against a clinical ground truth for diagnostic accuracy.

8. The sample size for the training set

Not applicable, as this is not an AI/machine learning device requiring a training set for algorithm development.

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

Not applicable.

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BE Plus PRO and Neurotravel LIGHT are intended to be used for Electroencephalography (EEG) and Polysomnography (PSG) exams, in combination with the Galileo NT Line software, for human beings to assist the user in diagnosis and monitoring of disorders of the central and peripheral nervous system and muscles.

EEG Family devices (BE plus PRO and Neurotravel LIGHT) are active medical devices composed of various parts necessary to allow the achievement of their intended use. All these parts are intended to be interconnected to achieve the specified medical purpose.

EEG Family devices can be used in patients, diagnostics centers, neurosurgical hospitals and experimental laboratories of research institutions.

EEG Family devices are capable of acquiring the bioelectric signal generated by the electrical potentials of the neurons of the cerebral cortex, by means of suitable signal, stimulus electrodes. This signal, of the order of a first and then, filtered to claim it of noise and offset. It is then digitized by a high-resolution analog-to-digital converter at a certain sampling rate.

The signals acquired and converted into digital format are transferred to a Host PC through a special serial communication with a dedicated protocol and then can be processed by the Galileo NT software to complete the intended use.

The software part allows the implementation of many specific functions of visualization, measurement, processing and storage of brain signals, acquired by the acquisition unit, based on the clinical area of interest.

EEG Family devices are offered in the following two configurations:

• Mobile: All components are mounted on a mobile trolley.
• Portable: All components are mounted on a stationary desktop.

The provided text describes specific details about device performance relative to established standards. However, it does not contain information about a study designed to evaluate acceptance criteria in the context of diagnostic accuracy, human reader performance, or clinical outcomes. The "acceptance criteria" presented are primarily related to safety and electromagnetic compatibility standards, rather than performance metrics like sensitivity, specificity, or reader agreement.

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

Acceptance Criteria and Device Performance (Safety and EMC)

Missing Information (Not Available in Provided Text):

The provided text focuses on demonstrating substantial equivalence based on safety and performance standards for an amplifier and software, not on evaluating a diagnostic algorithm's accuracy against a ground truth or its impact on human reader performance. Therefore, the following requested information is not present in the given document:

• Sample size used for the test set and the data provenance: Not applicable to the type of testing described (safety and EMC).
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
• Adjudication method for the test set: Not applicable.
• 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: Not applicable. The device is a physiological signal amplifier and analysis software, not an AI-based diagnostic aid that directly improves human reader performance in a comparative study.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. The "software" component seems to be for signal processing, visualization, and measurement, not a diagnostic algorithm that would have standalone performance metrics like sensitivity/specificity.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable to safety and EMC testing.
• The sample size for the training set: Not applicable. These are hardware (amplifier) and signal processing software, which typically don't have "training sets" in the AI/machine learning sense.
• How the ground truth for the training set was established: Not applicable.

Summary of the Study Performed:

The study performed was a non-clinical performance evaluation focusing on the physical and operational aspects of the BE Plus PRO and Neurotravel LIGHT devices (physiological signal amplifiers and associated software).

• Objective: To verify the electrical/mechanical/thermal safety and electromagnetic compatibility (EMC) of the subject devices, particularly due to enhancements made to electrical components compared to their predicate devices.
• Methodology: The evaluation was conducted using the exact same test methods as those used for the predicate devices, adhering to FDA-recognized external standards:
  • ANSI/AAMI ES60601-1 (Electrical, Mechanical, and Thermal Safety)
  • IEC 60601-2-26 (Specific to EEG equipment, for BE Plus PRO)
  • IEC 60601-1-2 (Electromagnetic Compatibility)
  • IEC 62304 (Medical Device Software)
  • ISO 14971 (Medical Device Risk Management)
• Results: The subject devices "passed all requirements of these standards. No deviations or exceptions were encountered."
• Conclusion: The manufacturer concluded that the subject devices "do not introduce any new safety considerations in comparison to the predicate devices. All identified differences between the two systems are minor and without any known impact on safety or efficacy."

This type of study is typical for demonstrating substantial equivalence for hardware devices and their accompanying software in terms of safety and basic functionality, rather than complex diagnostic AI performance.

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DreamClear is intended for use on both adults and children to collect, amplify, and transmit physiological signals acquired from a patient, including electroencephalogram (EEG), electrooculography (EOG), limb movement, respiration effort, air pressure, and peripheral oxygen saturation (SpO2). DreamClear is intended for use on the order of a physician and is non-sterile.

While primarily intended for use with dedicated polysomnography software to aid in the diagnosis of sleep disorders, DreamClear may be used to amplify physiological signals for any medical diagnostic procedure that does not involve:

• use alone as an apnea monitor or as a critical component in an apnea monitoring system; or
• use alone as a life support device or as a critical component of a life support system.

DreamClear is a physiological signal amplifier and medical device data system that captures and streams various physiological parameters via Bluetooth for remote data analysis and diagnosis of sleep disorders. DreamClear features analog sensor interface circuits for Electroencephalograph (EEG), Electrooculography (EOG) and Electromyography (EMG) signals. Additionally, the device includes digital sensor interface circuits for nasal and oral airflow (breathing pattern), microphone (snoring), heart rate and blood oxygen saturation with pulse oximeter (SpO2).
The DreamClear may be used in either a Basic Kit or Extended Kit configuration. The DreamClear device in its Basic Kit configuration includes the chest belt, pulse oximeter, and nasal cannula. The Basic Kit configuration has the following channels: thoracic effort, nasal pressure, pulse rate, SpO2, body position, and snore. The data recorded by the Basic Kit may be exported for optional display and analysis using third-party software products.

When the DreamClear device is used in the Extended Kit configuration, the additional channels of EEG and EOG are used, which gives the DreamClear a total of eight (8) channels.

The provided text is a 510(k) summary for the DreamClear device and focuses on establishing substantial equivalence to a predicate device (Nomad Sleep System Recorder) and a reference device (Alice PDx) for physiological signal amplification. It primarily discusses the device's intended use, functionality, and compliance with general medical device safety standards.

Crucially, this document does NOT contain information about a study that proves the device meets specific performance acceptance criteria related to its accuracy in aiding the diagnosis of sleep disorders (e.g., measuring sleep stages, apnea events, etc.). The text explicitly states: "Substantial equivalence was also established through a testing protocol that used bench data to evaluate the performance of DreamClear. Testing demonstrated that DreamClear amplified physiological signals sufficient for use with polysomnography software to aid in the diagnosis of sleep disorders, which is substantially equivalent to the intended use of the predicate Nomad device." This "bench data" refers to electrical and electromagnetic safety and performance testing, not clinical performance for diagnosis.

Therefore, I cannot fulfill most of your request based on the provided text, as the information regarding specific acceptance criteria for diagnostic performance, a test set, expert involvement, and MRMC studies is absent.

However, I can extract the following relevant information:

Device Name: DreamClear

Device Type: Physiological Signal Amplifier (specifically for sleep studies)

Intended Use: To collect, amplify, and transmit physiological signals (EEG, EOG, limb movement, respiration effort, air pressure, SpO2) for various medical diagnostic procedures, primarily for use with polysomnography software to aid in the diagnosis of sleep disorders.

Here's an attempt to answer your questions based on the limited information provided, and where the information is not present in the text, I will explicitly state that.

Acceptance Criteria and Device Performance (Based on Provided Text)

As the document focuses on substantial equivalence for a physiological signal amplifier rather than a diagnostic algorithm, the acceptance criteria mentioned are primarily related to its ability to collect, amplify, and transmit signals in a physiological range, and its compliance with safety and usability standards.

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

Since the document describes the type of testing performed (bench testing for signal amplification, and usability), but does not provide specific quantitative acceptance criteria or precise performance metrics in a table format, a direct table cannot be created.

However, the general performance claim is:

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

• Sample Size for Test Set: Not specified. The document mentions "bench data" and a "usability study." Bench testing typically involves laboratory setups, not patient data in the sense of a clinical test set. The usability study would have involved human participants, but the number is not provided.
• Data Provenance: Not specified. "Bench data" suggests laboratory testing rather than clinical patient data. For the usability study, the origin of participants (e.g., country) is not mentioned.
• Retrospective or Prospective: Not specified. "Bench data" implies a controlled, non-clinical setting.

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 for the described studies. The testing described (bench data for signal amplification sufficiency and a usability study) does not involve establishing ground truth for diagnostic purposes in the way you might for an AI algorithm interpreting medical images or signals. The device is an amplifier, not an interpreter.

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

• Not applicable. No ground truth establishment for diagnostic performance is mentioned.

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

• No. The document does not describe an MRMC study. The DreamClear device is a physiological signal amplifier, not an AI that aids human readers in interpretation. It outputs raw physiological signals for third-party polysomnography software and physician interpretation.

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

• Yes, implicitly in the context of signal amplification. The "bench data" testing of the DreamClear device's ability to amplify physiological signals is a standalone performance evaluation of the hardware's function. However, this is not a diagnostic standalone performance. The device itself does not interpret or diagnose; it provides the raw data for interpretation.

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

• Not applicable in the context of diagnostic ground truth. For the stated purpose of the device (signal amplification), the "ground truth" would be the known input signals used in bench testing to verify the amplified output, adhering to engineering and signal processing principles. For the usability study, the ground truth would be user feedback and observed user behavior against defined usability metrics.

8. The sample size for the training set

• Not applicable. This device is a hardware physiological signal amplifier, not an AI/ML diagnostic algorithm that requires a training set.

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

• Not applicable. This device is a hardware physiological signal amplifier, not an AI/ML diagnostic algorithm that requires a training set.

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The X-trodes System M is intended for prescription use only in the home or healthcare facility to acquire, record, transmit and display physiological signals from adult patients. The X-trodes System M acquires, records, transmits, and displays electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and/or electromyogram (EMG), and accelerometer and gyroscope signals. The X-trodes System M only acquires and displays physiological signals, no claims are being made for analysis of the acquired signals with respect to the accuracy, precision, and reliability.

The X-trodes System M combines hardware, firmware, and software to acquire the following physiological signals: physiologic signal amplifier (EEG), electrooculography (EOG), surface electromyography (sEMG), electrocardiography (ECG), and accelerometer and gyroscope signals. It acquires physiological data through a data acquisition unit connected to electrode arrays patches, applied by a technician or patient to the patient. The data is recorded and transmitted to a cloud where it is converted to an EDF (European Data Format) format, suitable for analysis by third party software.

Here's a detailed breakdown of the acceptance criteria and the study that proves the X-trodes System M meets them, based on the provided FDA 510(k) summary:

Acceptance Criteria and Device Performance

The provided document focuses on demonstrating substantial equivalence to predicate devices rather than establishing specific, quantified clinical acceptance criteria for standalone diagnostic accuracy or effectiveness against a clinical gold standard. The "acceptance criteria" can be inferred from the "comparison of technological features" table (Table 1) and the discussion items (Sections 9.1-9.7), where the X-trodes System M's performance characteristics are compared to those of the predicate devices and relevant IEC standards.

The closest thing to a directly stated performance acceptance criterion, beyond comparisons to predicates and standards, is for the clinical study's primary endpoint:

• Primary Study Endpoint (Infered Acceptance Criterion): A high proportion of interpretable readings of each ExG signal (ECG, EEG, EMG, EOG) by the X-trodes System M that are equivalent to those of the reference device, with lower 98.75% confidence intervals higher than 60%.

Study Details

The document describes a clinical study to demonstrate the X-trodes System M's performance compared to an FDA-cleared clinical electrophysiology device.

1. Sample Size used for the test set and the data provenance:

   • Sample Size: 55 subjects.
   • Data Provenance: The study was a "case-controlled study" conducted in a "clinic environment." The subjects were "adults who had been referred by either a neurologist for an electrophysiology test in the last 5 years." This suggests the data is prospective, collected specifically for this study, and likely from a single or limited number of clinical sites. The country of origin is not explicitly stated, but the applicant's address is Herzliya, Israel, which might imply the study was conducted there or in the US.

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

   • The study design describes a comparison against an "FDA-cleared clinical electrophysiology device" (referred to as X8). The "interpretability" and "equivalency" of XTR signals to the reference device were assessed.
   • The document does not explicitly state the number or qualifications of experts used to establish the ground truth or determine the "interpretable readings" and "equivalency" for the test set. It implies the reference device (X8) provided the comparison standard, and then the XTR signals were evaluated against that. It's unclear if independent human experts adjudicated the signals from both devices or if a software-based comparison was used for "equivalency."

3. Adjudication method for the test set:

   • The document states "The primary study endpoint was the proportion of interpretable readings of each ExG signal by the XTR that are equivalent to those of the reference device."
   • The specific adjudication method (e.g., 2+1, 3+1, none) is not detailed in the provided text. It's unclear how "interpretability" and "equivalency" were precisely determined.

4. 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:

   • No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The study was a comparison of the X-trodes System M (the subject device) against another predicate device (X8) and focused on signal acquisition and display, not on the impact of an AI algorithm on human reader performance. The device "only acquires and displays physiological signals, no claims are being made for analysis of the acquired signals with respect to the accuracy, precision, and reliability." Therefore, there is no AI assistance component to measure the effect size for human readers.

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

   • Yes, in spirit, a standalone performance assessment was conducted for the device's signal acquisition capabilities. The study directly compared the signals acquired by the X-trodes System M to a reference device. It's "algorithm only" in the sense that the device itself acquires and processes the signals for display, and its performance (interpretable readings, RMS values) was directly evaluated without a human-in-the-loop task for diagnosis. However, it's crucial to note that the device does not perform analysis or provide diagnostic output; it just collects and displays signals.

6. The type of ground truth used:

   • The "ground truth" was established by an FDA-cleared clinical electrophysiology device (X8). The X-trodes System M's acquired physiological signals (EEG, EOG, ECG, EMG) were compared for "interpretable readings" and "equivalency" against the signals simultaneously acquired by the X8 device. This is a comparative ground truth against an established device, rather than a clinical outcome or pathology report.

7. The sample size for the training set:

   • The document does not provide information on a training set sample size. This makes sense as the device is stated to "only acquires and displays physiological signals, no claims are being made for analysis of the acquired signals," implying it's not an AI/ML-driven device that typically requires a dedicated training set for diagnostic or analytical tasks. The performance evaluation focused on the device's ability to accurately capture and display raw physiological signals.

8. How the ground truth for the training set was established:

   • As no information on an AI/ML training set is provided (and the device is described as not performing analysis), there is no mention of how ground truth for a training set was established.

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The DormoTech Vlab is a physiological data recorder intended to collect and record data from multiple physiological channels for use by clinical software used in polysomnography and sleep disorder studies. It is intended for use by or on the order of a physician is intended for use on adults in a supervised (hospital) or unsupervised (home) environment.

The DormoTech Vlab is a physiological data recorder intended to collect and record data from multiple physiological channels for use by clinical software used in polysomnography and sleep disorder studies. It consists of: The Head Unit, The Body Unit, and The Central Unit.

This FDA 510(k) summary describes the DormoTech Vlab, a physiological data recorder intended for polysomnography and sleep disorder studies. The acceptance criteria and the study proving it meets these criteria are detailed below.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document doesn't explicitly state "acceptance criteria" in a tabular format with specific thresholds. However, it presents a clinical study comparing the DormoTech Vlab to a "gold standard PSG study" by evaluating the agreement of various physiological parameters. The "conclusion" section of the clinical study acts as the implicit acceptance criteria, indicating that "Most parameters show good agreement between the devices, as indicated by the mean difference values close to zero and narrow limits of agreement." This implies that the device is considered acceptable if its measurements are statistically comparable to the gold standard.

The table below summarizes the device's performance based on the clinical study's agreement analysis (Bland-Altman statistics) between the DormoTech Vlab and a gold standard PSG (likely the NOX Sleep System, K192469, which is used as a reference).

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

• Sample Size for Test Set: 47 subjects.
• Data Provenance: Prospective clinical study conducted in two sleep labs in Israel:
  • Shamir Medical Center – Be'er Ya'akov, Israel
  • Millenium Sleep Clinic - Be'er Sheva, Israel
    The study was "comparative, self-controlled, randomized, prospective study designed to assess the Vlab and compare its performance to a gold standard polysomnogram (PSG) conducted over 1 night in a sleep lab."

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

The document does not explicitly state the number of experts used for ground truth establishment or their specific qualifications (e.g., "radiologist with 10 years of experience"). However:

• The "gold standard polysomnogram (PSG)" implies scoring by trained sleep technologists or physicians, as PSG analysis typically requires specialized expertise.
• The "Conclusion" section mentions "the role of human scoring," which suggests human experts were involved in generating the ground truth from the gold standard PSG data.

4. Adjudication Method for the Test Set

The document does not specify the adjudication method used for the test set (e.g., 2+1, 3+1, none). It only mentions that the study compared the Vlab's performance to a "gold standard PSG study" with implied human scoring.

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

No, an MRMC comparative effectiveness study, as typically described (measuring human reader improvement with AI vs. without AI assistance), was not done. The study focused on the standalone performance of the DormoTech Vlab device in comparison to a gold standard PSG system, not on how the Vlab system assists human interpretation.

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

Yes, a standalone performance study was done. The clinical study directly compared the measurements obtained from the DormoTech Vlab ("the device") to those from a "gold standard PSG study." The Vlab device collects and records physiological data for use by "clinical software," and the performance reported is of the device's data collection compared to the gold standard, rather than evaluating the accuracy of any integrated AI for interpretation or how it assists a human.
The 510(k) summary states: "The subject and predicate device are sensor arrays for use in collecting and transmitting data from sleep studies that is analyzed by automated, FDA-cleared software, which is not part of this submission." This reinforces that the Vlab's performance study is focused on its ability to acquire signals accurately, which is then fed into other (cleared) software for analysis.

7. Type of Ground Truth Used

The ground truth used was established via a "gold standard PSG study." This gold standard involves the comprehensive recording of physiological signals during sleep, which are then typically scored and interpreted by trained professionals according to established clinical guidelines (e.g., AASM rules). The measurements from these gold standard PSG studies (e.g., AHI, sleep stages) served as the reference against which the DormoTech Vlab's measurements were compared.

8. Sample Size for the Training Set

The document does not provide any information about a training set or its sample size. This is a performance study comparing the device to a gold standard, not a study describing the development or training of an AI algorithm within the DormoTech Vlab itself. The Vlab is described as a "physiological data recorder," and any software for analysis is mentioned as "clinical software used in polysomnography and sleep disorder studies" that is "FDA-cleared software, which is not part of this submission."

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

As no information about a training set is provided, there is no description of how ground truth for a training set was established.

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SomniCheck is a prescription-use physiological data recorder intended to amplify, digitize, and record data from multiple physiological channels for subsequent transfer to polysomnography systems. The device measures the following signals:

• · EEG (4-channel),
• · PPG (by Maxim),
• · Temperature,
• · 3-axis accelerometer,
• · Gyroscope (position), and
• · Audio/ sound for snoring.

It is intended for use on adult patients (18 and older) and can be used in a hospital, clinic, or home.

SomniCheck is an integrated single-use disposable physiological data recorder intended to amplify, digitize, and record data from multiple physiological channels for subsequent transfer to polysomnography systems for neurophysiology or sleep disorder studies. It is intended for adult use and can be used in a hospital, clinic, or patient home. The associated software There is proprietary software that is currently under development, but the subject device's signal recording capabilities have been validated using the Natus Sleepworks K090277 via bench testing. The proprietary sleep software will be submitted in a future 510(k) Submission.

The device is affixed to the forehead of the patient and designed for continuous wear (e.g. during sleep) for up to approximately 10 hours. Once activated, the device records data for the duration of the wear period. After the wear period, the device is removed and may be thrown away. The device measures the following signals:

• · EEG (4-channel),
• PPG (by Maxim®),
• · Temperature,
• · 3-axis accelerometer,
• · Gyroscope (position), and
• Audio/ sound for snoring.

The provided text describes the predicate device information, applicable standards, and a comparison of the proposed device (SomniCheck) with predicate devices. However, it does not contain details about specific acceptance criteria for performance metrics, nor does it present the results of a study proving the device meets those criteria.

The document states:

• "(b)(1) Non-clinical bench performance tests included System Requirements Verification, Electromagnetic Compatibility (EMC) Verification, and Device Firmware Verification, including applicable clauses from the following standards: [lists various IEC and ISO standards]."
• "(b)(2) Clinical performance testing is not applicable to the device type."
• "(b)(3) The performance testing demonstrates that the device is as safe, as effective, and performs as well as the legally marketed predicate devices identified in paragraph (a)(3) of this section and therefore substantially equivalent."
• "The associated software There is proprietary software that is currently under development, but the subject device's signal recording capabilities have been validated using the Natus Sleepworks K090277 via bench testing. The proprietary sleep software will be submitted in a future 510(k) Submission."

Based on this, the device's clearance is based on bench testing against established standards and substantial equivalence to predicate devices, rather than a clinical performance study with specific acceptance criteria that would typically involve a test set, ground truth experts, and MRMC studies.

Therefore, most of the requested information cannot be extracted from the provided text.

Here's what can be inferred or stated based on the given document:

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

• Acceptance Criteria: Not explicitly stated as specific performance metrics (e.g., sensitivity, specificity) for signal detection or interpretation. The acceptance criteria are implicit in compliance with the listed IEC and ISO standards for electrical safety, EMC, usability, and risk management, and the demonstration of "as safe, as effective, and performs as well as the legally marketed predicate devices."
• Reported Device Performance: The document only states that "performance testing demonstrates that the device is as safe, as effective, and performs as well as the legally marketed predicate devices," and that "signal recording capabilities have been validated using the Natus Sleepworks K090277 via bench testing." No quantitative performance data (e.g., EEG signal fidelity, PPG accuracy) from these bench tests are 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 applicable as "Clinical performance testing is not applicable to the device type." The validation was done via bench testing.
• Data provenance: Bench testing.

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. The ground truth for bench testing involves adherence to engineering standards and comparison with predicate device specifications, not expert interpretation of data.

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

• Not applicable.

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

• No. This device is a physiological data recorder, not an AI-powered diagnostic aide for human readers. It records signals for subsequent transfer to polysomnography systems.

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

• The device's signal recording capabilities were validated via bench testing with Natus Sleepworks K090277. This is fundamentally a standalone hardware performance validation. There is no mention of a standalone algorithm performance in the context of interpretation, as the proprietary sleep software is still "under development" and will be "submitted in a future 510(k) Submission."

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

• The ground truth for this device's validation (signal recording) would be based on engineering specifications, known signal characteristics, and comparison to the performance of the predicate device/system (Natus Sleepworks K090277) under controlled bench conditions. It is not based on clinical "ground truth" such as expert consensus or pathology, as these are related to the interpretation of the collected physiological signals, which is outside the scope of this device's current submission.

8. The sample size for the training set

• Not applicable. This is a hardware/firmware device for signal acquisition, not a machine learning model for which a "training set" would be used in the context of this 510(k). The proprietary software is still under development.

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

• Not applicable. (See #8)

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The NomadAir PMU810 is intended to record physiological signals acquired from a patient for archival in a sleep study. Data may be analyzed on dedicated polysomnography software running on a personal computer by a qualified sleep clinician to aid in the diagnosis of sleep-disordered breathing (SDB).

The NomadAir PMU810 is intended for use within a hospital, laboratory, clinic, nursing home, or patient's home.

The NomadAir PMU810 is intended for use on adults only under the direction of a physician or qualified sleep technician.

The NomadAir PMU810, or any accessory, does not include or trigger alarms, and is not intended to be used alone as, or a critical component of,

• an alarm or alarm system:
• an apnea monitor or apnea monitoring system; or
• a life monitor or life monitoring system.

The NomadAir PMU810 is a portable device that records physiological signals used for sleep studies. The device can be worn on the chest or the wrist and is attached to the patient using a RIP belt or a disposable strap comected directly to the patient also wears a pulse oximeter probe, a nasal cannula, and an optional sensor, such as a RIP belt, thermocouple, EMG electrode, or ECG electrode.

During the typical workflow of using the clinical user configures the device for a patient. The clinician will give the patient instructions on how to attach the device and send the unit and accessories home with the patient. Before going to bed, the patient will connect everything, then go to sleep. The Nomad Air records the signals from the sensors and saves the data to internal memory. The patient may perform up to 3 sleep studies before returning the device. Once the device, they can connect it to a PC using a USB cable and download the study for analysis.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

• Input Impedance: >= 20 MOhm
• Pressure Sensor Accuracy: +/- 1 cmH2O
• Sampling Rate: 250 Hz +/- 0.05%
• Battery Life: minimum of 24 hours
• RIP Driver Test: Peak to trough measurement > 750 uV for 1" deflection of belt

These are engineering and performance metrics, not clinical outcomes, expert consensus, or pathological findings.

8. The Sample Size for the Training Set

The provided text does not mention a training set because the device described, NomadAir PMU810, is a physiological signal amplifier and recorder. It does not appear to employ machine learning or AI algorithms that would require a distinct "training set" for model development. The performance testing focuses on its ability to accurately acquire and record physiological signals according to engineering specifications.

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

Since there is no mention of a training set, the method for establishing its ground truth is not applicable.

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The Quick-20m is intended to be used to acquire the electroencephalogram (EEG) and transmit it wirelessly to a computer.

The Quick-20m is a wireless, battery-operated 10-20 montage EEG headset utilizing dry sensor technology. The headset provides an integrated approach to the wireless acquisition of EEG signals. A seated patient is free to exhibit natural movements while real-time data is collected. The Quick-20m includes advanced amplification and shielding to reject ambient electrical noise.

The headset obtains high-quality EEG with minimal scalp preparation. Patented mechanisms and a range of replaceable dry sensors align to various head shapes and sizes, maintaining sensor positions in a standard 10-20 layout. EEG channels are digitized with 24 bits of resolution at 500 Hz. The Quick-20m is suitable for general-purpose EEG.

Regrettably, the provided text does not contain the information needed to answer your request about acceptance criteria and the study proving the device meets them.

The document is a 510(k) premarket notification letter and summary for a device called "Quick-20m," which is a physiological signal amplifier (EEG headset). While it discusses the device's technical characteristics and claims substantial equivalence to a predicate device (g.Nautilus PRO), it explicitly states the following:

• "Clinical studies were not required to demonstrate that this device is as effective and at least as safe as the predicate device." (page 10)
• "Bench testing on the Quick-20m includes Intertek certifications to 80601-2-26. in addition to internal company design verification testing that successfully demonstrated compliance to user inputs, and in accordance with 21 CFR 820.40. Clinical testing on the Quick-20m was not applicable." (page 10)

This submission relies on technical comparisons to a predicate device and bench testing for safety standards rather than a clinical study or performance data against specific acceptance criteria for a diagnostic/interpretive AI.

Therefore, I cannot extract:

1. A table of acceptance criteria and reported device performance: No such criteria or performance data are presented.
2. Sample size for the test set and data provenance: No test set is described.
3. Number of experts and their qualifications for ground truth: No expert ground truth establishment is described as clinical studies were not performed.
4. Adjudication method for the test set: Not applicable as there's no test set.
5. MRMC comparative effectiveness study or effect size: No clinical study involving human readers or AI assistance is mentioned.
6. Standalone (algorithm only) performance: The device is a hardware amplifier; performance here refers to its ability to acquire and transmit EEG, not an AI algorithm's diagnostic performance.
7. Type of ground truth: Not applicable as no clinical ground truth was established for performance evaluation.
8. Sample size for training set: Not applicable; this is a hardware device, not an AI model requiring a training set in the conventional sense.
9. How ground truth for training set was established: Not applicable.

The document focuses on establishing substantial equivalence based on technical specifications and safety standards, not on a performance study against specific diagnostic or interpretive endpoints.

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The Digital NeuroPort Biopotential Signal Processing System supports recording, processing, and display of biopotential signals from user-supplied electrodes. Biopotential signals include: Electrocorticography (ECoG), electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), electroculography (EOG), and Evoked Potential (EP).

The Digital NeuroPort Biopotential Signal Processing System is used to acquire, process, visualize, archive/record signals as acquired from user-supplied electrodes for biopotential monitoring. Signals are acquired using a headstage relay that attaches to the pedestal interface and digitizes the signal through the hub. The Digital NeuroPort System uses preamplifiers, analog to digital converters, a signal processing unit, and software running on a personal computer to visualize and record biopotentials from electrodes in contact with the body.

The document describes the Digital NeuroPort Biopotential Signal Processing System, which is a physiological signal amplifier. The device's substantial equivalence to a predicate device (K090957, NeuroPort Biopotential Signal Processing System) is affirmed based on various performance data.

Here's an analysis of the acceptance criteria and the supporting studies:

1. Table of Acceptance Criteria and Reported Device Performance:

   Test / Characteristic	Acceptance Criteria	Reported Device Performance
   NeuroPlex E Functional Testing
   Mating	Screws down on pedestal and LED turns green	Pass
   Crosstalk	Isolation resistance of 1kΩ at 500 V DC	Pass
   Label Durability	IEC 60601-1:2005/A1:2012, Edition 3.1 7.1.3	Pass
   Digital Accuracy	Appropriate voltages for different filters (0.02-10 kHz Wide, 0.3-7.5 kHz Standard); Peak-to-peak of 500mV ±10%	Pass
   Input Impedance	≥10MΩ	Pass
   Impedance Measurement	820 ± 15% kOhms and 170 ± 15% kOhms	Pass
   Current Rating

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