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Neuronaute is a system intended to acquire, display, store, archive, and periodically transmit EEG signals from the brain using a full montage array to enable review at a physician's office, hospital, or other remote locations. It allows remote access by users via the Neuronaute N-CLOUD which receives EEG signals from Neuronaute Head Module which sends transmissions to the cloud. Neuronaute and its associated software are intended to assist in the diagnosis of neurological disorders. Neuronaute and its components do not provide any diagnostics conclusions or automated alerts of an adverse clinical event about a patient's clinical condition.

The device is for use by trained medical professionals for patients under medical supervision. The device is intended for use on adults (ages 18 and above). Neuronaute is not intended to replace direct communication with healthcare providers. The system data should not be used alone, but should be used along with all other clinical data and exams to come to a diagnosis.

Neuronaute allows up to 24 channels EEG monitoring. It includes the following components: Neuronaute Head Module, Neuronaute High Capacity Battery Module, Neuronaute BioAdapter, Neuronaute Mobile App, Neuronaute N-CLOUD, Neuronaute N-DEO, Neuronaute N-WAY, Neuronaute IceCap.

The provided text describes the Neuronaute device, an electroencephalograph (EEG) system, and its substantial equivalence to a predicate device (AE-120A EEG Head Set, K183529) for FDA clearance. However, it does not contain a specific study demonstrating that the device meets numerically defined acceptance criteria for diagnostic performance (e.g., sensitivity, specificity, accuracy for a particular condition). Instead, the submission focuses on demonstrating substantial equivalence through non-clinical performance testing against recognized standards.

Therefore, many of the requested items (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone performance) are not applicable or cannot be extracted from this document, as a clinical performance study with such metrics was not submitted.

Here's a breakdown of the information that can be extracted or inferred:

1. Table of Acceptance Criteria and Reported Device Performance

As a clinical performance study with specific diagnostic metrics (like sensitivity, specificity, or accuracy) was not submitted, there isn't a table of acceptance criteria for diagnostic performance against specific disease states. Instead, the acceptance is based on meeting technical and safety standards, and demonstrating signal quality, all of which are considered "performance" in this context.

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The WEE-1200 Wireless Input Unit is intended to acquire, store, record, and transmit the cerebral and extracerebral activity for EEG and sleep studies. The device should be used together with Nihon Kohden specified electroencephalograph via wired or wireless communication to display EEG on the electroencephalograph screen. These data may be used by the clinician in sleep disorder, epilepsies and other related disorders as an aid in diagnosis. The WEE-1200 Wireless Input Unit is designed for use by qualified medical facility such as a hospital or clinic on all patient populations including adult, neonate, infant, child and adolescent subgroups.

The WEE-1200 Wireless Input Unit is a system that can be used in routine EEG testing, epilepsy monitoring, or PSG testing to send the measured EEG waves, ECG waves, EMG waves, EOG waves, and respiration waves from the electroencephalograph by wired or wireless LAN. The electrodes are connected to a mini junction box which is connected to the telemetry unit that is worn by patient or placed near patient.

The measured data can also be stored in the telemetry unit of the WEE-1200 Wireless Input Unit.

Additionally, by connecting a SpO2 sensor and SpO2 adapter to the telemetry unit, SpO2 value and waveform can be measured and displayed on both the electroencephalograph screen and the telemetry unit LCD display.

The provided text describes the regulatory submission for the Nihon Kohden Wireless Input Unit WEE-1200, primarily focusing on its substantial equivalence to predicate devices rather than proving its performance against specific acceptance criteria for diagnostic accuracy.

Therefore, many of the requested details about acceptance criteria, study design for proving device performance (like sample size, number of experts, adjudication methods, MRMC studies, standalone performance, and ground truth establishment), and training set information are not available in this document. This submission is for an Electroencephalograph (EEG) wireless input unit, which is a data acquisition and transmission device, not an AI-powered diagnostic tool. The "performance" being validated here relates to its technical functionality, safety, and electromagnetic compatibility.

However, I can extract information related to the non-clinical testing that was performed to demonstrate substantial equivalence and meet regulatory standards.

Here's a breakdown of the available information based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present specific diagnostic performance acceptance criteria (e.g., sensitivity, specificity, AUC) because it is a device for acquiring and transmitting physiological signals (EEG, ECG, EMG, EOG, respiration, SpO2), not a diagnostic algorithm. The acceptance criteria are based on functional performance, safety, and electromagnetic compatibility.

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The device is intended to measure, amplify, and record physiological signals acquired from a patient for archival in a sleep study. The physiological signals and conditioned for analysis and display. The data may be analyzed in real-time or offline on dedicated polysomnography software running on a personal computer by a qualified sleep clinician to aid in the diagnosis of Sleep Disorders.

The device is intended for use by healthcare professionals within a hospital, laboratory, clinic, or nursing home; or outside of a medical facility under direct supervision of a medical professional.

The device is intended for use on both adults and children only under the direction of a physician or qualified sleep technician.

The device or any accessory, does not include alarms, and is not intended to be used as a critical component of an alarm system.

The device or any accessory, is not to be used alone as an apnea monitor or as a component in an apnea monitoring system.

The device or any accessory, is not to be used alone as a life support device or as a critical component of a life support system.

The Polysmith Sleep System, Model NTI6600 is intended to amplify and record physiologic potentials used for Polysomnography (PSG) or Sleep Studies. The device consists of a compatible amplifier, head box, PC, patient sensors, and may include optional external devices, USB DC Box, and audio/video input devices.

Compatible amplifiers may use commercially available sensors and electrodes, an internal SpO2 module, and internal pressure transducers to collect, digitize, and send physiological signals to the host PC.

The Polysmith software may record from video, speaker and microphone equipment. The Polysmith software may also record auxiliary signals from compatible amplifiers or USB DC Box which allow for data inputs from compatible sources.

Polysmith records and displays the data for online or offline review. Qualified practitioners use the information to score Polysomnograms and diagnose Sleep Disorders.

Here's a breakdown of the acceptance criteria and study details for the Polysmith Sleep System, Model NTI6600, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly state "acceptance criteria" as separate performance thresholds the device must meet. Instead, it presents the "Automated Analysis Agreement" as the device's performance, which is then compared against "Average Human Scoring Agreement." Assuming the implied acceptance is substantial equivalence to human performance for these metrics, the table is structured as follows:

In the "Additional Sleep Staging Analysis," which seems to be a more detailed analysis, the total agreement for automated scoring against consensus human scoring is:

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

• Sample Size (Automated Analysis): 8,053 recorded epochs, of which 7,606 were scored.
• Sample Size (Additional Sleep Staging Analysis): 20 sleep studies.
• Data Provenance: The studies were chosen at random from existing sleep studies of acceptable signal quality from accredited labs. For the automated analysis, studies were chosen at random from a sleep lab independent of Neurotronics. The document does not specify the country of origin, but given the FDA submission, it's likely US-based or compliant with US standards. The data is retrospective.

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

• Automated Analysis: Two expert scorers from different organizations. Each is a registered sleep clinician with at least 5 years' experience scoring sleep records.
• Additional Sleep Staging Analysis: Three independent scorers. Each is a registered sleep specialist with at least five years' experience in the field. Each had some prior experience using Polysmith to manually score studies.

4. Adjudication Method for the Test Set

• Automated Analysis: The document states "The human scoring was completed by two expert scorers." It then calculates "Average Human Scoring Agreement," implying an average of their individual scores or comparison. It doesn't explicitly describe an adjudication process for disagreements between the two human scorers to establish a single ground truth for direct comparison with the automated analysis. Instead, it compares the automated system's performance against this average human agreement, and also compares the two human scorers' agreement with each other.
• Additional Sleep Staging Analysis: This section explicitly used a majority method (2/3 majority-rule) to establish the "true sleep staging (consensus)." Epochs where there was no agreement among the human scorers were discarded (non-consensus).

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

• No, a direct MRMC comparative effectiveness study "with AI vs without AI assistance" was not explicitly described in the context of improving human reader performance. The study focused on the standalone performance of the automated analysis system compared to human scoring. While humans scored the data, the goal was to assess the device's accuracy relative to human experts, not to measure the improvement in human performance aided by the device.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, a standalone performance analysis was conducted. The "Automated Analysis Agreement" and the "Results" section for "Additional Sleep Staging Analysis" directly report the performance of the algorithm itself against human expert assessments (either average or consensus). The scorers for the "Additional Sleep Staging Analysis" were explicitly provided with a version of Polysmith that "did not have any automated analysis capability," emphasizing that the human scoring served purely as ground truth.

7. The Type of Ground Truth Used

• Expert Consensus / Expert Scoring: For both analyses, the ground truth was established by human expert scorers. In the "Automated Analysis," it's based on two expert scorings. In the "Additional Sleep Staging Analysis," it's based on a 2/3 majority rule consensus of three expert scorers.

8. The Sample Size for the Training Set

• Not provided. The document states, "The studies used for this testing were not used to train the algorithm," but it does not specify the size or characteristics of the training set used for the algorithm's development.

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

• Not provided. The document focuses solely on the ground truth for the test sets and explicitly states the test sets were not used for training. Information on how the training data's ground truth was established is absent.

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EEG- 1200A 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 Disorder, Epilepsies and other related disorders as an aid in diagnosis.

The PE-210AK Switch Box is an optional accessory for the EEG-1200A device. It is used with the EEG-1200A to switch between EEG recording and cortical stimulation using the same cortical electrode. The software is used for functional brain mapping to support diagnostic and surgical epilepsy procedures.

The device is intended for use within a hospital or medical facility under direct supervision of a medical professional

The PE-210A Switch Box is an optional accessory for the EEG-1200A device cleared under 510k # K080546. The PE-210AK Hardware is made up of electronic switches inside a box enclosure and cable connections on the outside of the box enclosure. The PE210AK Hardware switch box does not have any controls or adjustment for the operator. All settings, controls, and adjustments are made using the PE-210AK software installed on the EEG's (off the shelf) PC. Utilizing the EEG's CPU. Data storage, keyboard, mouse, and display, the PE-210AK switch box software allows the user switch electrode stimulation sites between the EEG recording and the cortical stimulator. The PE-210AK software allows the user to query the data recorded on the EEG's PC and create a report of the stimulation sites, including duration and intensity settings.

The Nihon Kohden PE-210AK Switch Box for EEG-. 1200A is an accessory that facilitates switching electrodes between an electroencephalograph (EEG) and a cortical stimulator, primarily for functional brain mapping in the context of diagnostic and surgical epilepsy procedures. The 510(k) submission K110376 does not explicitly detail acceptance criteria in a quantitative manner as would be expected for a diagnostic or AI-powered device. Instead, the "acceptance criteria" are implied by the demonstration of substantial equivalence to a predicate device, the Excel Tech EMU 128s Switch Matrix (K040360), and verified through design validation and various testing procedures.

The provided document describes the device's functional capabilities and compares them to the predicate device to establish substantial equivalence. The "study" proving the device meets these criteria is a design validation that confirmed the operation of the software and hardware according to design specifications, along with electromagnetic, environmental, safety, and performance testing. However, there is no information provided about a clinical study involving human subjects, diagnostic accuracy metrics (e.g., sensitivity, specificity, AUC), or a comparison against a defined ground truth for specific clinical outcomes.

Here's an attempt to structure the information based on your request, understanding that the given document focuses on functional equivalence rather than quantitative performance metrics for diagnostic accuracy.

Acceptance Criteria and Device Performance

Study Details Proving Acceptance Criteria

The provided 510(k) summary does not detail a clinical study with human subjects, diagnostic performance metrics, or specific quantitative acceptance criteria that would typically be associated with an AI or diagnostic device. Instead, the "study" is a collection of engineering tests and a comparison to a predicate device to establish substantial equivalence.

Here's an analysis based on the available information:

1. Sample size used for the test set and the data provenance: This information is not provided. The document states "Design validation confirmed the operation of the software and hardware of the device is in accordance to the design specifications" and "The device was subjected to electromagnetic, environmental, safety and performance testing procedures. These test verified the proper operation of the device." These are internal engineering and verification tests, not typically clinical trials with defined test sets of patient data.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: This information is not provided. As the "study" described is a design validation and technical testing, there's no mention of expert-established ground truth in the context of clinical accuracy or a diagnostic outcome.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: This information is not applicable/not provided. There is no indication of a clinical test set requiring adjudication of findings.

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: This information is not applicable/not provided. The Nihon Kohden PE-210AK Switch Box is an accessory for switching electrodes and assisting with functional brain mapping, not an AI-powered diagnostic tool that directly interprets data or assists human readers in a diagnostic task in a way that would warrant an MRMC study comparing AI-assisted vs. non-assisted performance.

5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: This information is not applicable/not provided. The device is an accessory controlled by software and is intended for use by medical professionals; it does not operate as a standalone algorithm without human interaction or supervision.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): This information is not provided/not explicitly defined in a clinical context. For the design validation, the "ground truth" would be the engineering and functional specifications that the device's hardware and software were designed to meet. The verification tests confirmed conformance to these specifications.

7. The sample size for the training set: This information is not applicable/not provided. The device is not an AI/ML-based system that requires a "training set" of data in the conventional sense. Its software performs control and display functions based on predefined logic and parameters, rather than learning from data.

8. How the ground truth for the training set was established: This information is not applicable/not provided for the same reasons as point 7.

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