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EEG-1260A Neurofax System
The EEG-1260A series Neurofax is intended to record, measure and display cerebral and extracerebral activity for EEG and Sleep Studies. These data may be used by the clinician in sleep disorders, epilepsies and other related disorders as an aid in diagnosis. The device is intended for use by medical personnel in any location within a medical facility, laboratory, clinic, or nursing home or outside of a medical facility under direct supervision of a medical professional.

JE-940A EEG Amplifier Unit
The EEG amplifier unit is intended to acquire and record the EEG and other biological signals (ECG, EMG, respiration, EOG, snoring, body position, SpO2, pulse waveform) of a patient, and transmit the measurement data to the electroencephalograph. The transmitted measurement data is displayed on the electroencephalograph screen and provides information to evaluate the functional state of the brain, brain-related diseases and disorders, and sleep disorders.

The device is intended for use by qualified medical personnel within a medical facility, or by staff in an equivalent facility under the direct supervision of qualified medical personnel.

The device is available for use on any patient as determined by qualified medical personnel.

LS-940A Photic Light
The photic stimulator is a light source for photic stimulation to confirm the responsiveness of EEG to photic stimulation during EEG tests and to test visual evoked potentials.

The device is intended for use by qualified medical personnel within a medical facility, or by staff in an equivalent facility under the direct supervision of qualified medical personnel.

The device is available for use on any patient as determined by qualified medical personnel.

The EEG-1260A Neurofax is an electroencephalograph system specifically designed for use in healthcare facilities. This device is designed to measure and display the patient's electroencephalogram (EEG) and polysomnography (PSG) signals, providing information and analysis of brain electrical activity.

The JE-940A EEG amplifier unit is a new amplifier unit and is an input unit of the EEG-1260A. The JE-940A Amplifier unit acquires and measures EEG and other polysomnography signals (ECG, EMG, respiration, EOG, snoring, body position, SpO2, and pulse waveforms) associated with EEG/PSG testing, and transmits the acquired data to the EEG-1260A Neurofax. The JE940A operates on AC power or on battery power for mobile EEG measurements. The JE-940A offers an option to connect with the JE-944A Mini electrode junction box, which enhances the operational efficiency and mobility in EEG measurements.

The LS-940A Photic stimulator is a device which provides visual stimuli in the form of flashing light and is used to assess a patient's EEG responses to light stimulation. The parameters for flashing the light signal are controlled by the EEG-1260A Neurofax.

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The VEEGix EEG System, is intended to be used for measuring and recording the electrical activity of a patient's brain, obtained by placing electrodes on the forehead and wirelessly transmitting the electroencephalographic (EEG) signals for storage and display.

The VEEGix EEG System, is intended for use in the acquisition of EEG signals, displaying them in real time and storing them for later review and analysis.

The VEEGix EEG System, is intended for use in a hospital Operating Room. Post Anesthesia Care Unit, Intensive Care Unit, Emergency Department, or in other medical facilities such as inpatient and outpatient (ambulatory) surgery settings.

The VEEGix EEG System is indicated for use on patients 18 years of age or older and is to be used by licensed medical professionals who have been adequately trained in the use and interpretation of EEG data for determination of brain state.

The VEEGix EEG System does not provide any diagnostic conclusion about the patient's condition.

The VEEGix EEG System is not to be used as a stand-alone in the evaluation or diagnosis of a disease or other condition.

The VEEGix EEG System is not intended for use in life support systems.

The VEEGix™ EEG system (subject device) is a Electrocochleographic (EEG) device to allow healthcare practitioners, nurses, neurologists and qualified technicians working in hospital Operating Room, Post Anesthesia Care Unit, Intensive Care Unit, Emergency Department, or in other medical facilities such as inpatient and outpatient (ambulatory) surgery settings to observe a patient's EEG signals and its derivatives.

The use of the subject device is a straight-forward procedure of applying the VEEGix Electrode, which is a lightweight band, onto the patient's scalp in which the electrodes acquire the patient's EEG signals. Those signals are transmitted via the VEEGix EEG module) to the VEEGix iOS app (application) which display the patient's EEG measurements in real-time to allow healthcare practitioners, nurses, neurologists and qualified technicians to monitor and record those EEG measurements.

Here's a breakdown of the acceptance criteria and study details for the VEEGix EEG System, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list a table of quantitative acceptance criteria with corresponding performance values for the software component of the device. Instead, the "Technology Comparison" table highlights specific technical characteristics where the VEEGix EEG System aligns with or exceeds its predicates, implying these are the implicit acceptance criteria for technical equivalence. The clinical performance section describes the methodology used to demonstrate comparable EEG signal quality to a conventional clinical EEG system, which serves as the overarching performance goal.

Implicit Acceptance Criteria and Reported Device Performance (based on "Technology Comparison" and "Clinical Performance Testing" sections):

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

The document states: "Bench testing was performed in hospital to compare the quality and medical EEG feature similarity of electroencephalography (EEG) signals obtained by the NeuroServo (NS) device and those recorded with a conventional clinical EEG system (N1) (NicoletOne, Natus Medical Inc., Pleasanton, CA) in an ICU setting."

• Sample Size for Test Set: Not explicitly stated as a number of patients or recordings. The description focuses on signal processing and comparison, implying data was collected from one or more patients, but a specific count is missing.
• Data Provenance:
  • Country of Origin: Not explicitly stated, but the study was performed "in hospital" in an "ICU setting," suggesting prospective data collection.
  • Retrospective or Prospective: Prospective, as the testing was described as being "performed in hospital" to compare signals.

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

The document mentions "experts" in the context of "10-second epochs as this is a common temporal range used by experts for the offline or online visualization of data." However, it does not state the number of experts used to establish the ground truth for the test set, nor their specific qualifications. The comparison was primarily quantitative (Pearson's correlation) and visual ("substantial overlap between the waveforms").

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

No explicit adjudication method (e.g., 2+1 or 3+1 for clinical consensus on ground truth) is mentioned for the test set. The comparison relies on a quantitative statistical measure (Pearson's correlation coefficient) and visual assessment of waveform overlap against a predicate device.

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 multi-reader multi-case (MRMC) comparative effectiveness study involving human readers or AI assistance effect size is reported. The VEEGix EEG System "does not provide any diagnostic conclusion about the patient's condition" and "is not to be used as a stand-alone in the evaluation or diagnosis of a disease or other condition." Its purpose is to measure, record, and display EEG signals for interpretation by licensed medical professionals.

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

The clinical performance test described is a standalone (algorithm only) performance evaluation, although it's comparing the VEEGix system's signal acquisition and processing directly against a predicate system. The study quantified the similarity of EEG signals obtained by the VEEGix system with a conventional clinical EEG system (NicoletOne, N1) using Pearson's correlation coefficient. The output of the VEEGix system (processed EEG signals) was directly compared to the output of the N1 system.

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

The "ground truth" for the clinical performance testing was the EEG signals obtained from a "conventional clinical EEG system (NicoletOne, Natus Medical Inc., Pleasanton, CA)". This predicate device's output served as the reference standard against which the VEEGix device's signal quality was compared.

8. The sample size for the training set

The document does not provide any information regarding a training set sample size. The VEEGix EEG System appears to be a signal acquisition and display system without explicit mention of deep learning or AI models that would require a dedicated training set in the conventional sense. The "Automatic Artifact Identification" feature might implicitly involve some learned algorithms, but no details on training data are provided.

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

As no training set is mentioned or detailed, there is no information provided on how ground truth for a training set was established.

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The Neuron-Spectrum-AM system with Neuron-Spectrum.NET software is intended for use as a digital neurophysiological system for recording, processing, and displaying biopotential signals such as Electroencephalography (EEG) and Polysomnography (PSG) derived from Electroencephalography (EEG) by the means of a dedicated software module and dedicated electrodes.

The device is portable and can register up to 21 EEG channels, 4 polygraphic channels, and 1 direct current channel.

The device does not provide alarms, does not provide automated event marking and does not provide to the user any diagnostic conclusion about the patient's condition. They are in patient care institutions, diagnostics centers, neurosurgical hospitals, experimental laboratories. The device can also be used as a home use device under supervision of qualified personnel. The patient group includes all ages and sexes.

The Neuron-Spectrum-AM with Neuron-Spectrum.NET Software (Subject device) is an ambulatory wireless digital neurophysiological device capable of recording, processing, and displaying electroencephalography (EEG), video EEG, long-term monitoring (LTM), and polysomnography (PSG) biopotential signals.

As listed in Table 1, The NS-AM device comprises an Electronic Unit, a Battery Adaptor, an External Rechargeable Battery Bank, a Carrying Pouch and the Neuron-Spectrum.NET desktop application software. The Neuron-Spectrum.NET Software application was cleared in the Predicate's 510(k) submission (K133995).

The Subject device supports up to 21 EEG channels, 4 wide-band polygraphic channels, 1 breath channel, and 1 direct current channel for a total of 27 analog input channels.

The Electronic Unit acquires, records and transmits biopotential signals such electroencephalography (EEG) and polysomnography (PSG). The built-in 2.4GHz Wi-Fi radio allows real-time transfer of the collected biopotential signals to the computer running the Neuron-Spectrum.NET software. The biopotential signals are also recorded onto a removable SD memory card as back up for later post analysis.

The Electronic Unit includes a front panel ON/OFF power button, a light sensor and a User Interface (UI). The UI consist of a liquid crystal display and three menu navigation buttons. Figure 4 shows the UI main functions. The Electronic Unit can be powered by replaceable internal AA batteries or by the External Rechargeable Battery Bank.

The Electronic Unit and the External Rechargeable Battery Bank reside side-by-side inside the Carrying Pouch. The pouch is strapped and worn over the patient clothing while in use.

The Neuron-Spectrum.NET software is a computer application that receives, records, processes, and displays the biopotential signals collected by the electronic unit on the PC display. The main operations provided by the Neuron-Spectrum.NET software are:

• EEG Acquisition
• · EEG Review, Editing, Storing, Exporting.
• EEG Analysis
• Creation of Exam Reports
• · Program Setup

The electronic unit measures (141 H x 96 W x 36 D) mm and weighs about 270 grams. The External Rechargeable Battery Bank unit measures (165 H x 106 W x 25 D) mm and weighs about 570 grams. The combined weight for the electronic unit, the battery adaptor and the External Rechargeable Battery Bank is approximately 880 grams.

The provided FDA 510(k) summary (K220254) for the Neurosoft Ltd. Neuron-Spectrum-AM with Neuron-Spectrum.NET Software describes the device and its claimed substantial equivalence to a predicate device (K133995). Given the information, here's a breakdown of the requested details:

1. Table of Acceptance Criteria and Reported Device Performance

This 510(k) summary does not explicitly state specific, quantifiable acceptance criteria (e.g., "sensitivity must be > X%") for the device's diagnostic performance, nor does it report specific performance metrics like sensitivity, specificity, accuracy, etc., for the device's clinical efficacy.

Instead, the acceptance criteria are implicitly met through demonstrating substantial equivalence to a predicate device by showing that it "is as safe, as effective, and performs as well as or better than the Neuron-Spectrum-4/P with Neuron-Spectrum.NET Predicate device".

The "reported device performance" is focused on passing various non-clinical engineering and software tests to demonstrate this substantial equivalence.

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

The 510(k) summary explicitly states under "8.2. Clinical Testing": "The substantial equivalence for the Subject device will not be demonstrated by results of clinical testing. Therefore, no clinical testing was performed."
This means there was no test set of patient data used for clinical performance evaluation. The data provenance is therefore not applicable as no clinical data was used in the assessment for this submission.

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

Since no clinical testing was performed and no test set of patient data was used, this information is not applicable.

4. Adjudication Method for the Test Set

As no clinical testing was performed, an adjudication method for a test set is not applicable.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

No MRMC study was done, as explicitly stated that "no clinical testing was performed." Therefore, information on the effect size of human readers improving with AI vs. without AI assistance is not applicable. The device does not include an AI diagnostic component for interpretation; it is a signal acquisition and processing system.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

No standalone performance study was done for an algorithm's diagnostic capability, as the device's function is to record, process, and display biopotential signals (EEG/PSG), not to provide automated diagnostic conclusions or interpretations. The device "does not provide automated event marking and does not provide to the user any diagnostic conclusion about the patient's condition."

7. The Type of Ground Truth Used

Given that no clinical testing was performed, there was no clinical ground truth (expert consensus, pathology, outcomes data, etc.) established for the purpose of demonstrating clinical performance. The "ground truth" for the engineering performance tests would be defined by the known specifications of the test equipment and the expected outputs under controlled conditions.

8. The Sample Size for the Training Set

Since this is a medical device for recording and processing biopotential signals and not a machine learning/AI diagnostic algorithm that requires training on patient data for clinical interpretation, there is no training set in the context of typical AI algorithm development. The device's "training" refers to its engineering design and manufacturing processes.

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

As there is no training set as typically understood for an AI algorithm, this information is not applicable. The "ground truth" for the device's development lies in engineering specifications, regulatory standards, and the functional performance of the predicate device.

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The Pascall Systems, Inc. Wireless EEG System, is intended to be used for measuring the electrical activity of a patient's brain, obtained by placing electrodes on the forehead and wirelessly transmitting the electroencephalographic (EEG) signals for storage and display. The Pascall Systems, Inc. Wireless EEG System, is intended for use in the acquisition of EEG signals, displaying them in real time and storing them for later review and analysis. The Pascall Systems, Inc. Wireless EEG System, is intended for use in a hospital Operating Room, Post Anesthesia Care Unit, Intensive Care Unit, Emergency Department, or in other medical facilities such as inpatient and outpatient (ambulatory) surgery settings.

The Pascall Systems, Inc. Wireless EEG System is indicated for use on patients 18 years of age or older and is to be used by licensed medical professionals that have been adequately trained in the use and interpretation of raw EEG data for determination of brain state during anesthesia. The Pascall Systems, Inc. Wireless EEG System does not provide any diagnostic conclusion about the patient's condition.

The Pascall Systems Wireless EEG system is a brain monitoring or Electroencephalographic (EEG) device. The system is designed to record and display four channels of electroencephalograms (EEGs) obtained from noninvasive electrodes placed on a patient's head. The acquired EEG waveforms (4 channels) and processed EEG variables which include a Spectrogram, Artifact index, electromyography (EMG) index, burstsuppression probability, and phase-amplitude modulogram are continuously displayed by the system. Licensed medical professionals which include anesthesiologists, nurse anesthetists, etc. combine and interpret the data presented by the Pascall Systems Wireless EEG with information provided by other instruments in the operating room. The primary purpose of the Pascall Systems Wireless EEG is in the acquisition of electroencephalographic (EEG) signals, displaying them along with the processed variables in real time and storing them for later review and analysis. The Wireless EEG system is intended for use in a hospital Operating Room, Post Anesthesia Care Unit, Intensive Unit, Emergency Department, or in other medical facilities such as inpatient and outpatient (ambulatory) surgery settings.

The provided text describes the Pascall Systems, Inc. Wireless EEG System and its substantial equivalence to predicate devices, but it does not contain a specific study demonstrating direct acceptance criteria for an AI/algorithm's performance. Instead, it focuses on the device's overall performance as an EEG system against established medical device standards and a comparison to predicate devices, particularly regarding its functional components and display features.

However, the document does mention testing related to the "Burst Suppression Ratio" (BSR) which involves a comparison to "manual review of the raw EEG timeseries." This can be interpreted as a form of algorithm performance evaluation.

Here's an attempt to extract and synthesize the information based on your request, focusing on the BSR "study" as the most relevant part, and acknowledging the limitations in the provided text:

Study and Acceptance Criteria Description (Focused on Burst Suppression Ratio - BSR)

The Pascall Systems, Inc. Wireless EEG System underwent non-clinical bench performance testing, which included a specific test to establish substantial equivalence of its Burst Suppression Ratio (BSR) output.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: 49 segments of EEG signals from 10 patients.
• Data Provenance: Not explicitly stated (e.g., country of origin). The data is implicitly retrospective as it involved "segments of EEG signals from 10 patients."

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified, but the ground truth was "manual review of the raw EEG timeseries," implying review by qualified professionals (likely neurologists or neurophysiologists experienced in EEG interpretation, though not explicitly stated).

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. However, since the ground truth was "manual review," it implies direct human interpretation rather than a multi-reader consensus method like 2+1 or 3+1. It's possible a single expert or an unspecified number of experts performed the "manual review."

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

• Was it done?: No. The document does not describe an MRMC study comparing human readers with and without AI assistance. The testing focuses on the device's internal performance against a manual ground truth.

6. Standalone (Algorithm-Only) Performance Study

• Was it done?: Yes, for the Burst Suppression Ratio (BSR) calculation. The device's algorithm for BSR was tested by "repeatedly inputting 49 segments of EEG signals" and comparing its output to "manual review." This represents a standalone performance evaluation of the BSR computation. The validation of the real-time Spectrogram, Phase-Amplitude display, and EMG Index using "known simulated input signals" also constitutes a standalone performance evaluation of these specific algorithms/outputs.

7. Type of Ground Truth Used

• Ground Truth Type: Expert consensus/manual review (specifically, "manual review of the raw EEG timeseries" for BSR). For other outputs like Spectrogram, Phase-Amplitude, and EMG Index, "known simulated input signals" were used, which acts as a defined ground truth.

8. Sample Size for the Training Set

• Training Set Sample Size: Not specified. The document does not provide details about a training set, implying that either the device's algorithms are not machine learning-based in a way that requires explicit training data disclosure in this context, or that information was not deemed necessary for this 510(k) summary.

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

• Ground Truth Establishment for Training Set: Not applicable as a training set is not described.

Summary of Device Performance (General)

Beyond the specific BSR study, the document broadly describes the Pascall Systems, Inc. Wireless EEG System's performance in comparison to predicate devices and relevant standards:

• Non-Clinical Bench Performance Testing: The system was tested in accordance with performance standards for electroencephalographic devices (IEC 60601-1, IEC 60601-1-2, IEC 80601-2-26, ISO 10993 Parts 5 and 10).
• Test Objectives: To verify claimed ranges of measurement, essential performance requirements (amplitude/rate accuracy, dynamic range, noise, frequency response, common mode rejection).
• Test Methods: Bench testing on end-to-end systems with simulated input.
• Pass/Fail Criteria: Compliance with claimed range and precision in labeling.
• Results Summary: The system met all applicable specifications and was found substantially equivalent to the predicate device.
• Biocompatibility: Patient-contacting materials passed ISO 10993 tests for cytotoxicity, skin sensitization, and skin irritation.

Overall Conclusion from the document: The Pascall Systems, Inc. Wireless EEG System is substantially equivalent to the predicate device based on review of test results and comparison of characteristics and specifications.

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The GE EEG module, E-EEGX, the GE EEG headbox, N-EEGX, and accessories are intended to be used with the compatible CARESCAPE monitors for the monitoring of electroencephalograph (EEG), frontal electromyograph (FEMG), and auditory evoked potentials (AEP) of all hospital patients. The device is intended for use by qualified medical personnel only.

The E-EEGX module is a single-width plug-in interface module to be used with CARESCAPE Bx50 V3 patient monitors. It is used with N-EEGX headbox and accessories for monitoring neurophysiological status of all hospital patients by measuring the electroencephalogram (EEG), frontal electromyogram (FEMG) and auditory evoked potentials (AEP).

The E-EEGX module is used with the N-EEGX headbox for monitoring of EEG. FEMG. to stimulate the brain with auditory stimuli, and to measure AEP. The E-EEGX module connects to a N-EEGX headbox which further connects to accessories that connect to the patient.

The EEG, FEMG and AEP measurements are performed by the N-EEGX headbox. The N-EEGX headbox measures the raw EEG waveform data from four real-time EEG waveform channels, FEMG from one channel and AEP from two channels. The N-EEGX headbox is connected to the patient with EEG accessories.

The E-EEGX module transfers the digitized EEG data received from the N-EEGX headbox to the host monitor. The module also generates the stimuli used in the AEP measurement and performs part of the AEP measurement data processing.

Here's a breakdown of the acceptance criteria and the study information for the E-EEGX module, N-EEGX headbox, and accessories, based on the provided FDA 510(k) summary:

The document does not detail specific quantitative acceptance criteria or a study designed to explicitly "prove" the device meets these criteria in the way a clinical trial would for a new therapeutic. Instead, the submission focuses on demonstrating substantial equivalence to a predicate device (K051883 Datex-Ohmeda S/5 EEG Module, E-EEG and Headbox, N-EEG and Accessories) through non-clinical testing.

The "acceptance criteria" can be inferred from the comparison table and the list of standards the device complies with. The "reported device performance" is essentially that the new device's specifications are either identical or acceptably equivalent to the predicate, and that it meets relevant safety and performance standards.

1. Table of Acceptance Criteria and Reported Device Performance

Since specific "acceptance criteria" with numerical targets and a performance study against them aren't explicitly presented, I've constructed a table based on the comparative effectiveness information provided, which serves as the basis for demonstrating substantial equivalence. The "Acceptance Criteria" column reflects the predicate device's specifications (which the new device aims to match or improve within acceptable limits), and the "Reported Device Performance" column shows the new device's specifications.

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 does not describe "test sets" or "data" in the context of clinical or performance data from patients. The evaluation performed was largely based on non-clinical testing (e.g., engineering verification, design validation, safety testing) and comparison of specifications to a predicate device. Therefore, no information on sample size, data provenance, or retrospective/prospective nature of a dataset is provided for this type of submission.

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 information is not applicable to this 510(k) submission. No expert review of patient data to establish ground truth was conducted or reported, as the submission focused on non-clinical testing and substantial equivalence to a predicate device.

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

This information is not applicable to this 510(k) submission, as there was no test set involving expert adjudication of patient data.

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

This information is not applicable to this 510(k) submission. The device (E-EEGX module, N-EEGX headbox and accessories) is an electroencephalograph system, not an AI-assisted diagnostic tool that would typically undergo MRMC studies comparing human reader performance. The submission makes no mention of AI.

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

This information is not applicable to this 510(k) submission. The device is a hardware module and headbox for physiological measurement, not a standalone algorithm.

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

This information is not applicable to this 510(k) submission, as there was no patient outcome or diagnostic ground truth required for its submission based on substantial equivalence and non-clinical testing. The "ground truth" for showing substantial equivalence was the specifications and validated performance of the predicate device and compliance with recognized international standards (e.g., IEC 60601 series) for medical electrical equipment.

8. The sample size for the training set

This information is not applicable to this 510(k) submission. The device does not appear to involve machine learning or AI that would require a "training set."

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

This information is not applicable to this 510(k) submission, as there was no training set for an AI/ML algorithm.

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CNS Envision is intended for use by qualified personnel in the review, and analysis of patient data collected using external physiological monitors. These data are: raw and quantitative EEG, recorded video data, generic vital signs, electrocardiography, electromyography, intracranial pressures, transcranial Doppler measurements, and Glasgow Coma Score.

CNS Envision includes the calculation and display of a set of quantitative measures intended to monitor and analyze the EEG waveforms. These include, for example, frequency bands, asymmetry, and burst suppression. These quantitative EEG measures should always be interpreted in conjunction with review of the original EEG waveforms.

The aEEG functionality included in CNS Envision is intended to monitor the state of the brain.

CNS Envision is intended for use by a physician or other qualified medical personnel who will exercise professional judgement in using the information. It is intended for use on patients of all ages.

This device does not provide any diagnostic conclusion about the patient's condition to the user.

CNS Envision is a Microsoft Windows-based software application that facilitates the review, annotation and analysis of patient data and physiological measurements. Some of these data, such as ECG, are displayed in raw format whereas other types, such as EEG, are analyzed and quantified by the software. The specific type of input data that are reviewable by CNS Envision software are: Raw electroencephalography (EEG) Quantitative EEG trends; density spectral arrays (DSA) spectral edge frequency (SEF), alpha-delta ratio (ADR), and amplitude EEG (aEEG) Video Generic vital signs which are heart rate (HR), respiration rate (RR), pulse oximetry (SpO2), blood pressure, arterial blood pressure (ABP), mean arterial pressure (MAP), and body temperature Electrocardiography (ECG) Electromyography (EMG) Intracranial pressure (ICP) Transcranial Doppler (TCD) measurements (e.g. spectral envelope, peak velocity, and pulsatility index; TCD measurement is collected by the predicate K080217 device's interface module which interfaces with the Spencer TCD device cleared in K002533, which was a predicate to the predicate K080217 Glasgow Coma Score (GCS); this parameter is manually entered on the CNS Monitor (K080217) with 3 total GCS scores by the user; the CNS software automatically sums the 3 scores and stores the data to provide a trend graph

CNS Envision also has several features to enable ease-of-use. For example, users may select customized layouts that provide data displays that can be tailored to their monitoring needs according to data sources. The subject device also offers customizable EEG montages that present raw EEG data to medical personnel for interpretation.

Unlike the predicate device, Component Neuromonitoring System™, the subject device does not perform direct data acquisition. Instead, it offers the ability to review data remotely or adjust the review speed.

The provided text is a 510(k) summary for the CNS Envision device. It describes the device's intended use, technological characteristics, and comparison to predicate devices, but it does not contain information about specific acceptance criteria, device performance metrics (e.g., sensitivity, specificity, accuracy), sample sizes for test or training sets, ground truth establishment details, or any multi-reader multi-case (MRMC) study results.

The document states that "Software verification and validation testing was conducted and documentation provided as recommended by the Guidance for the Content of Software Contained in Medical Devices, issued May 2005. Traceability has been documented between all system specifications to validation test protocols. Verification and validation testing includes module-level testing, integration-level testing, and system-level testing. In addition, tests according to “IEC 62366-1:2015, Medical Devices Part 1—Application of usability engineering to medical devices” were performed."

This indicates that some testing was done to ensure the software functions as intended and meets usability standards, but the specific performance results in terms of clinical accuracy or equivalent metrics are not present in this summary. The summary focuses on establishing substantial equivalence based on intended use and technological characteristics rather than a detailed performance study like those typically expected for AI/ML-based diagnostic devices.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, or details about the sample sizes, expert ground truth, adjudication methods, MRMC studies, or standalone performance for this specific device based on the provided text. The device "CNS Envision" is described as software that analyzes and quantifies EEG, but "does not provide any diagnostic conclusion about the patient's condition to the user" and "does not contain automated detection algorithms," suggesting it's a tool for experts rather than an automated diagnostic AI.

To illustrate what such an answer would look like if the information were available, here's a template:

Hypothetical Example (based on standard AI/ML medical device studies, NOT from the provided text):

Given the provided document does not contain the requested information regarding specific acceptance criteria, performance metrics, training/test set details, or human reader studies, I cannot fill out the detailed table and answer the questions directly from the text.

However, if this were an AI-powered diagnostic device, the requested information would typically be presented as follows:

1. Table of Acceptance Criteria and Reported Device Performance (Hypothetical Example - Data NOT from provided text)

2. Sample size used for the test set and data provenance:
* Test Set Sample Size: [Number] cases (e.g., 500 patient records/EEGs)
* Data Provenance: Retrospective, collected from [e.g., 3 major university hospitals] in [e.g., United States, Germany, Japan].

3. Number of experts used to establish the ground truth for the test set and qualifications of those experts:
* Number of Experts: [e.g., 3 board-certified neurologists specializing in epilepsy]
* Qualifications: [e.g., Each expert had 10+ years of experience interpreting EEG, with specific expertise in neurological disorders relevant to the device's intended use. All were blinded to other expert readings and AI output.]

4. Adjudication method for the test set:
* [e.g., 2+1 adjudication: If two experts agreed, that was the ground truth. If there was a disagreement among the initial two, a third, senior expert (the 'tie-breaker') reviewed the case and made the final determination.]
* Alternatively: Consensus reading where all experts discussed and jointly determined the ground truth.
* Alternatively: No adjudication, each expert independently provided a reading.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, what was the effect size of how much human readers improve with AI vs without AI assistance:
* MRMC Study Done: [Yes/No]
* If Yes: Readers showed a statistically significant improvement in AUC of [e.g., 0.06] when assisted by the AI, moving from an average AUC of [e.g., 0.82] without AI to [e.g., 0.88] with AI assistance (p < 0.001). They also had a [e.g., 7%] increase in sensitivity and maintained specificity.

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:
* Standalone Performance Done: [Yes/No]
* If Yes: The algorithm achieved a standalone sensitivity of [e.g., 90%], specificity of [e.g., 85%], and AUC of [e.g., 0.92] on the independent test set.

7. The type of ground truth used:
* [e.g., Expert Consensus: The ground truth for the test set was established by the consensus of multiple independent medical experts, as described in point 3 & 4. This might be further supported by correlating with patient clinical outcomes where available.]
* Alternative: Pathology results.
* Alternative: Long-term clinical outcomes data.

8. The sample size for the training set:
* Training Set Sample Size: [e.g., 10,000 patient records/EEGs]

9. How the ground truth for the training set was established:
* [e.g., Ground truth for the training set was established through a combination of initial review by trained data annotators overseen by a senior neurologist, followed by verification by a single board-certified neurologist. In ambiguous cases, a secondary review by a different neurologist was conducted. For some conditions, correlation with electronic health record (EHR) data or other clinical markers was used.]

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1. Cadwell Bolt is a software-only device intended for post-hoc analysis of EEG data. This device is intended to be used by qualified medical practitioners who will exercise professional judgment in using the information. This device is intended to be used with EEG data from patients of all ages.

2. Cadwell Bolt includes the calculation and display of a set of quantitative measures intended to monitor and analyze the EEG data. These include the following 10 analyzers:

• -Amplitude Integrated EEG (aEEG),
• -Peak Envelope,
• -Envelope Asymmetry,
• -Spectrogram,
• -Band Power.
• -Power Ratio,
• -Spectral Entropy,
• -Burst Suppression Ratio,
• -Inter-burst Interval, and
• -Bursts-per-Minute.
  These quantitative EEG measures should always be interpreted in conjunction with review of the original EEG data. A minimum of one epoch of data is required to perform Bolt analysis.

3. The aEEG functionality included in Cadwell Bolt is intended to monitor the state of the brain.

4. Cadwell Bolt can provide notifications, based on user-defined thresholds, for quantitative EEG measures such as a notification within the application software that can be used when processing a record during acquisition. Delays can occur between the beginning of an event and when the Bolt notification will be shown to a user. Cadwell Bolt notifications cannot be used as a substitute for real-time monitoring of the underlying EEG data by a trained expert.
   This device does not provide any diagnostic conclusion about the patient's condition to the user as part of its output. The software does not contain automated detection algorithms.

Cadwell Bolt is a software-only device for use with EEG data. The Cadwell Bolt receives EEG data as an input and uses this input to calculate and display the following quantitative measures of electroencephalographic data:
(1) amplitude integrated EEG (aEEG),
(2) peak envelope,
(3) envelope asymmetry,
(4) spectrogram,
(5) band power,
(6) power ratio.
(7) spectral entropy,
(8) burst suppression ratio.
(9) inter-burst interval, and
(10) bursts-per-minute.
In addition to the 10 analyzers mentioned above, Cadwell Bolt calculates if a value is greater than a user-defined threshold. A horizontal black bar is displayed in the trend window and signifies the current threshold setting. The user may move the bar up/down to adjust the threshold.
Thresholding is available for the following trends:
. peak envelope,
. envelope asymmetry,
spectrogram, .
band power, and ●
power ratio. .
The Cadwell Bolt device performs analysis on a userdefined set of EEG channels within the EEG data and displays graphical results to be interpreted by qualified medical practitioners. Additional user-selectable parameters are available depending on the analyzer selected for display. These parameters include frequency ranges or bands for analysis, number of epochs to average, and minimum burst suppression durations. Using the userdefined thresholds, Cadwell Bolt can provide notifications to the user and can optionally highlight the associated EEG data. Notifications and/ or highlights can be accepted or rejected by a qualified medical practitioner. Additional features and user-selectable parameters include:
Output display color
Output display size ●
Output display name
Epoch length ●
Notch filter ●
Selected inputs (bipolar or referential)
. Output display time scale (y-axis)

Here's a summary 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

Specific Performance Data from the Study:

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

The document states that "Arbitrary data of known value was input" and "sample EEG data at various time scales and with various montages and/or frequency bands" were used. However, it does not specify a numerical sample size for the test set or the data provenance (e.g., country of origin, retrospective/prospective nature of the data).

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

This information is not provided in the document. The testing involved "analyzing known arbitrary EEG data and confirming that the expected output was obtained" and comparing results with predicate devices, but it does not mention the involvement or qualifications of experts for establishing ground truth on the test data.

4. Adjudication method for the test set:

This information is not provided in the document. The evaluation involved a comparison of calculated metrics (correlation coefficient and percent difference) against predetermined thresholds, not an adjudication process by experts.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not conducted. The document describes a standalone performance evaluation of the software's quantitative output compared to predicate devices, not impact on human reader performance.

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

Yes, a standalone performance study was done. The study focused on the Cadwell Bolt Software's ability to calculate and display quantitative EEG measures, and its outputs were compared against those of predicate devices. The device is intended for post-hoc analysis by qualified medical practitioners who exercise professional judgment, implying it functions as a tool rather than a diagnostic algorithm that provides conclusions or replaces real-time monitoring by an expert.

7. The type of ground truth used:

The ground truth for this performance study appears to be established through "known arbitrary EEG data" and by comparing the device's output to the output of legally marketed predicate devices, which serve as a reference for "substantial equivalence." The document states that the software "implements public domain (nonproprietary) quantitative analysis measures that are identical or similar to the predicate devices."

8. The sample size for the training set:

This information is not provided in the document. The document describes verification and validation but does not detail a training set for the Cadwell Bolt Software, implying it uses established algorithms rather than a machine learning model that requires a distinct training phase.

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

This information is not provided in the document, as no training set is explicitly mentioned or described.

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The Lifelines iEEG is an EEG system that allows acquisition, display, archive, storage and analysis of physiological signals. The intended user of this product is a qualified medical practitioner trained in electroencephalography who will exercise professional judgment in using the information. The Lifelines iEEG system also includes the display of a quantitative EEG plots, power spectrum, which is intended to help the user to monitor and analyze the EEG. This device does not provide any diagnostic conclusion about the patient's condition to the user.

Lifelines iEEG is medical device used to acquire, display, archive, store and analyze EEG examinations. The EEG is presented in a conventional way and conventional signal processing is applied such as re-montaging and band pass filtering. The system is also capable of acquiring and presenting digital video synchronized to the EEG if this is available. Some advanced analysis methods are provided as an aid: FFT analysis and Artifact Removal. The system software is designed using service oriented architecture enabling the possibility of reviewing data over WAN without the use of additional remote desktop software solutions.

The components of Lifelines iEEG are:

• Lifelines iEEG software:
  • . iEEG Centrum
  • . iEEG Review
  • iEEG Acquisition .
• Lifelines Trackit, Lifelines Ltd, 510(k)#K010460
• Lifelines Photic Stimulator, Lifelines Ltd, 510(k)# K101691 ●
• Off the shelf PC and medical grade power supply ●
• Off the shelf IP Video Camera ●

This 510(k) submission for the Lifelines iEEG 2.0 device primarily focuses on demonstrating substantial equivalence to a predicate device (Natus Medical, Inc., DG Nervus/NicoletOne, K964280) rather than presenting a standalone study with acceptance criteria for clinical performance. The documentation emphasizes software verification and validation, along with conformance to various IEC standards for safety and essential performance.

Therefore, many of the requested sections related to clinical study design, sample sizes for test/training sets, expert adjudication, MRMC studies, and ground truth establishment are not explicitly addressed in this document. The device is an EEG system for acquisition, display, archive, storage, and analysis of physiological signals, and the submission argues that it is substantially equivalent to existing, legally marketed devices.

Here's a breakdown of the available information:

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

No explicit acceptance criteria or reported device performance metrics in terms of clinical accuracy (e.g., sensitivity, specificity for diagnostic tasks) are provided in this document. The submission focuses on demonstrating substantial equivalence based on intended use, technological characteristics, and conformance to safety and performance standards.

The document highlights the following characteristics of the Subject Device (Lifelines iEEG):

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

This information is not provided in the document. The submission details non-clinical performance data, primarily software verification and validation, and conformance to electrical safety and EMC standards. There is no mention of a test set derived from patient data for evaluating clinical performance.

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 information is not provided as there is no described clinical test set or ground truth establishment based on expert consensus for clinical performance.

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

This information is not provided as there is no described clinical test set or adjudication process for clinical performance.

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 MRMC comparative effectiveness study was done regarding the Lifelines iEEG device improving human reader performance. The device is an EEG system for acquisition and analysis, not an AI-assisted diagnostic tool that interprets EEG data for the user. Its quantitative EEG plots (power spectrum) are intended to help the user monitor and analyze the EEG, but it explicitly states: "This device does not provide any diagnostic conclusion about the patient's condition to the user."

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

No standalone performance study (in a diagnostic sense) was conducted or reported. The device's primary function is to acquire, display, and provide tools for analysis to a human expert, not to perform independent diagnostic interpretations. The document states it "does not provide any diagnostic conclusion." The non-clinical performance data focuses on the technical aspects of the software and hardware components.

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

Not applicable for clinical performance evaluation as described in this document. The "ground truth" in the context of the reported non-clinical performance data would be the expected behavior of the software and hardware according to its design specifications and relevant international standards.

8. The sample size for the training set

This information is not provided. The device is an EEG acquisition and analysis system, not a machine learning model that would typically have a distinct "training set" of clinical data for learning diagnostic patterns.

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

This information is not provided as there is no described training set for a machine learning model with established ground truth.

Summary of Device and Performance Context:

The Lifelines iEEG 2.0 is an electroencephalograph (EEG) system intended for qualified medical practitioners trained in electroencephalography. It allows for the acquisition, display, archive, storage, and analysis of physiological signals, including quantitative EEG plots (power spectrum).

The submission argues for substantial equivalence to the predicate device (DG Nervus/NicoletOne) by detailing similarities in intended use, users, population, use environment, regulation, and core principles of operation (signal processing, montage, filtering, data plotting).

The main differences noted are:

• Lifelines iEEG does not offer automated spike and seizure detection, which the predicate device does. The applicant argues these are "nice-to-have features that are not essential to an EEG system."
• Lifelines iEEG supports fewer EEG channels (up to 128 software, 32 hardware) compared to the predicate (up to 512).
• Lifelines iEEG includes the OLT product code for quantitative EEG, and supports its own proprietary (.ieeg) recording type.
• Lifelines iEEG includes wireless communication and video camera support as added features not present in the predicate.
• Minor differences in channel equivalent input noise, frequency band, filter ranges, and sampling rates between the subject and predicate device.

The "study" described is primarily a non-clinical performance evaluation consisting of:

• Software Verification and Validation
• Immunity Verification
• Third-party testing for conformance with IEC 60601-1:2005 (basic safety and essential performance)
• Third-party testing for conformance with IEC 60601-1-2:2007 (electromagnetic compatibility)
• Checklist testing for IEC 62304:2006 (Medical Device Software Life Cycle Processes)
• Checklist testing for IEC 62366:2007 (Usability Engineering)
• Third-party testing for conformance with IEC 60601-2-26:2002 (Particular requirements for the safety of electroencephalographs)

The absence of clinical performance data and acceptance criteria for diagnostic accuracy is consistent with the device's stated indications for use, which explicitly state it "does not provide any diagnostic conclusion about the patient's condition to the user." Its primary role is as a tool to aid qualified practitioners in their analysis.

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The DiscoverEEG System, Model DE-401 is intended to be used for measuring and recording the electrical activity of a subject's brain, obtained by placing non-invasive electrodes on the head. The DiscoverEEG, DE-401 is indicated for use in acquiring electroencephalographic (EEG) signals in the OR, ICU, ER, clinical settings and at home and for clinical research. The medical use of data acquired by the DiscoverEEG is to be performed under the direction and interpretation of a licensed medical professional. The DiscoverEEG, Model DE-401 does not provide any diagnostic conclusion about the subject's condition.

The DiscoverEEG System, Model DE-401, is a wearable, medical-grade EEG device that acquires and stores up to four electroencephalograms (EEGs) obtained from noninvasive electrodes placed on a subject's head. The acquired EEG waveforms, as well as, processed EEG spectral variables are continuously stored by the system for later retrieval. The data can be transferred from the DiscoverEEG hardware to a computer for review. The DiscoverEEG System, Model DE-401 has four main components: Acquisition Module, Memory Module, Disposable Electrode Array, and Data Viewer Software.

The provided text does not contain specific acceptance criteria for performance metrics (like sensitivity, specificity, accuracy) related to the interpretation of EEG signals by the DiscoverEEG System, Model DE-401 for diagnostic purposes. This is explicitly stated in the Indications for Use: "The DiscoverEEG, Model DE-401 does not provide any diagnostic conclusion about the subject's condition."

Instead, the non-clinical testing sections focus on the device meeting its design and functional requirements, safety standards (UL, IEC60601-1, IEC60601-1-11, IEC60601-2-26), and electromagnetic compatibility (IEC60601-1-2). The substantial equivalence claim is based on similar intended use, technological characteristics, and principles of operation to predicate devices, with bench testing demonstrating functional performance without providing specific quantitative metrics for diagnostic accuracy.

Therefore, for the aspects requested, here's what can be extracted from the document:

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

The document does not provide a table of performance-based acceptance criteria (e.g., sensitivity, specificity) for diagnostic accuracy. The "reported device performance" is primarily qualitative, stating that the device "meets its design and functional requirements" and "performed as expected."

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 states "Laboratory testing, performed on identical hardware to the DiscoverEEG subject of this submission, demonstrated that the DiscoverEEG, Model DE-401 meets its design and functional requirements." This indicates bench testing rather than a clinical human subject test set. There's no mention of sample size or data provenance in this context.

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 testing described is non-clinical bench testing, not involving human interpretation for ground truth.

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

Not applicable. There was no test set requiring expert adjudication for ground truth.

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 device "does not provide any diagnostic conclusion about the subject's condition" and the submission states "further clinical data is not required to demonstrate performance for the DiscoverEEG, Model DE-401 for the indication for use subject to this submission." Therefore, no MRMC study was performed to assess human reader improvement with or without AI assistance.

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

Not applicable in the context of diagnostic performance. The device's function is to "measure and record the electrical activity of a subject's brain" and provide "processed EEG spectral variables." The medical use and interpretation are explicitly "to be performed under the direction and interpretation of a licensed medical professional." While the device itself processes signals, the "standalone" performance for diagnostic purposes is not claimed or evaluated. The non-clinical testing focused on hardware functionality and adherence to standards.

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

Not applicable. The testing described is non-clinical/bench testing against design specifications and functional requirements, not against clinical ground truth like expert consensus, pathology, or outcomes data.

8. The sample size for the training set

Not applicable. The document does not describe an AI/ML component that requires a training set for diagnostic or interpretative tasks. The device acquires and processes EEG signals, but its output is for interpretation by a medical professional, not a standalone diagnostic conclusion.

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

Not applicable, as there is no described training set.

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The digital systems Neuron-Spectrum-1, Neuron-Spectrum-2, Neuron-Spectrum-3, Neuron-Spectrum-4 and Neuron-Spectrum- 4/P with Neuron-Spectrum.NET software are intended for use as digital neurophysiological systems intended for recording, processing and display biopotential signals such as Electroencephalography (EEG) and long-latency Evoked Potential (EP). Polysomnography (PSG) derives from Electroencephalography (EEG) by the means of a dedicated software module and dedicated electrodes.

The devices are portable and can register up to 8 (Neuron-Spectrum-1), 16 (Neuron-Spectrum-2), 19 (Neuron-Spectrum-3), 21 (Neuron-Spectrum-4, Neuron-Spectrum-4/P) EEG channels, 1 (Neuron-Spectrum-1, Neuron-Spectrum-2, Neuron-Spectrum-3 and Neuron-Spectrum-4) or up to 4 polygraphic channels (Neuron-Spectrum-4/P: ECG, EOG), 1 breath channel and 2 direct current channels (Neuron-Spectrum-4/P).

Neuron-Spectrum.NET includes the Evoked potentials averaging function and Quantitative electroencephalography (qEEG) , including specific parameters such as Rhythmicity, FFT power ratio and amplitude metrics.

The devices do not provide alarms, do not provide automated event marking and do not provide to the user any diagnostic conclusion about the patient's condition. They are intended for use in the patient care institutions, diagnostics centers, neurosurgical hospitals experimental laboratories and sleep laboratories.The patient group includes all ages and sexes.

The digital systems Neuron-Spectrum-1, Neuron-Spectrum-2, Neuron-Spectrum-3, Neuron-Spectrum-4 and Neuron-Spectrum- 4/P with Neuron-Spectrum.NET software are intended for use as digital neurophysiological systems intended for recording, processing and display biopotential signals such as Electroencephalography (EEG) and long-latency Evoked Potential (EP). Polysomnography (PSG) derives from Electroencephalography (EEG) by the means of a dedicated software module and dedicated electrodes.

The devices are portable and can register up to 8 (Neuron-Spectrum-1), 16 (Neuron-Spectrum-2), 19 (Neuron-Spectrum-3), 21 (Neuron-Spectrum-4, Neuron-Spectrum-4/P) EEG channels, 1 (Neuron-Spectrum-1, Neuron-Spectrum-2, Neuron-Spectrum-3 and Neuron-Spectrum-4) or up to 4 polygraphic channels (Neuron-Spectrum-4/P: ECG, EOG), 1 breath channel and 2 direct current channels (Neuron-Spectrum-4/P).

Neuron-Spectrum.NET includes the Evoked potentials averaging function and Quantitative electroencephalography (qEEG) , including specific parameters such as Rhythmicity, FFT power ratio and amplitude metrics.

The devices do not provide alarms, do not provide automated event marking and do not provide to the user any diagnostic conclusion about the patient's condition. They are intended for use in the patient care institutions, diagnostics centers, neurosurgical hospitals experimental laboratories and sleep laboratories-The patient group includes all ages and sexes

The provided text describes a 510(k) premarket notification for the Neurosoft Neuron-Spectrum series of devices (Neuron-Spectrum-1, -2, -3, -4, and -4/P) with Neuron-Spectrum.NET software. The primary purpose of the submission is to demonstrate substantial equivalence to previously cleared predicate devices, rather than to establish new acceptance criteria through a clinical study. Therefore, the document does not contain a typical "acceptance criteria" table with specific performance metrics (e.g., sensitivity, specificity, AUC) and reported device performance against those criteria as would be expected for an AI/ML device seeking de novo authorization or a PMA.

Instead, the submission focuses on comparing the technological characteristics of the subject device to predicate devices to argue for substantial equivalence. The "acceptance criteria" can be inferred to be that the device performs functionally and safely at least as well as the predicate devices, as demonstrated through technical comparisons and compliance with relevant safety and performance standards.

Here's an attempt to structure the information based on the provided text, acknowledging the limitations due to the nature of a 510(k) submission primarily focused on substantial equivalence:

Inferred Acceptance Criteria and Device Performance (Based on Substantial Equivalence to Predicate Devices)

Study Details:

The provided document describes a 510(k) premarket notification, which relies on demonstrating substantial equivalence to a legally marketed predicate device, rather than a clinical study establishing new performance claims. Therefore, the details requested about sample sizes, ground truth establishment, expert adjudication, and comparative effectiveness studies are not typically found in this type of submission for this class of device.

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

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

   • Test Set Description: The document does not describe a clinical test set of patient data used to evaluate the device's performance against specific acceptance criteria. The evaluation is primarily based on technical specification comparisons and compliance with recognized standards (e.g., IEC tests for electrical safety and electromagnetic compatibility).
   • Data Provenance: Not applicable as a traditional clinical test set was not used. The "performance data" refers to validation tests on the device itself (hardware and software) and compliance with standards. The document mentions "Validation tests (Section 16.3), the Software Requirements Specifications (Section 16.4)" but these sections are not provided in the extract.

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

   • Not applicable. As no clinical test set with ground truth was described for independent device evaluation (beyond the intrinsic safety and performance of the device's components and software features), no experts were involved in establishing ground truth for such a test. The evaluation is against predicate device specifications and regulatory standards.

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

   • Not applicable as no clinical test set requiring expert adjudication for ground truth was used.

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 MRMC study was done. This device is an Electroencephalograph system for recording, processing, and displaying biopotential signals. It "does not provide alarms, do not provide automated event marking and do not provide to the user any diagnostic conclusion about the patient's condition." Therefore, it does not involve AI for interpretation or diagnostic assistance to human readers, and thus such a study would not be relevant.

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

   • No standalone algorithm performance study was done. The device's software (Neuron-Spectrum.NET) performs functions such as signal acquisition, review, editing, storing, and analysis (including quantitative EEG parameters like Rhythmicity, FFT power ratio, and amplitude metrics). However, the document explicitly states that the device "does not provide to the user any diagnostic conclusion about the patient's condition." Its functions are presented as tools for trained medical professionals, implying a human-in-the-loop workflow, where the human interprets the displayed signals and analysis. The document does not describe autonomous algorithmic diagnostic output.

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

   • Not applicable for a clinical test set. The "ground truth" for this submission would relate to whether the device's technical specifications and functional performance meet the engineering and safety standards, and are comparable to predicate devices. This involves engineering measurements and comparisons rather than clinical ground truth diagnoses.

7. The sample size for the training set:

   • Not applicable. The document does not describe a machine learning algorithm that underwent a training phase with a specific dataset.

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

   • Not applicable, as no training set for a machine learning algorithm is described.

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