Search Results

The neo monitor is an 8-channel electroencephalograph (EEG) acquisition software. The device is intended to record and display EEG and aEEG signals for monitoring the brain status of neonatal patients (defined as from birth to 28 days postdelivery, and corresponding to a postconceptual age of 24 to 46 weeks). The device is to be used in a hospital environment by qualified clinical practitioners. The device does not provide any diagnostic conclusion about the patient's condition to the user.

The nëo™ system is a reduced montage neonatal electroencephalograph device that acquires, displays, stores, and archives electroencephalographic signals from the brain. By application of electrodes at specific location on the cranium,using up to 10 surface electrodes placed at specific locations the system functions to measure and record electrical activity of the brain by acquisition of electroencephalograph data and amplitude-integrated electroencephalograph (electroencephalograph signals that have been filtered and displayed in a specific manner).

The neo System is an electromedical device incorporating software. The device itself has no patient contact, but is intended for use with FDA-cleared ECG electrodes. nëo™ Monitor software, which, when installed into a compatible touchscreen PC and paired with a physiological signal amplifier (eego model EE-411) forms the nëo™ Monitor System. The system is a compact and easy-to-use and can be set on a bedside table, pole-mounted, or on a cart in the neonatal care areas.

The system, as-delivered to the customer, is comprised of:

• nëo Monitor software (pre-installed)
• 15" Panel PC
• eego EE-411 model amplifier
• Mounting plate
• nëo User Manual .

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

1. Table of Acceptance Criteria and Reported Device Performance

The provided document primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting specific performance metrics against pre-defined acceptance criteria for the entire device. Instead, it compares the technological characteristics of the nëo Monitor System to its predicate, the Olympic Brainz Monitor.

The table below summarizes the technical specifications compared, which implicitly serve as acceptance criteria for functionality. It also includes the performance for the burst suppression detection software, which is a key differentiating feature with its own implicit performance.

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

The provided 510(k) summary does not detail a clinical study with a specific test set, sample size, or data provenance (e.g., country of origin, retrospective/prospective data for clinical validation). The submission relies on non-clinical testing against recognized standards and a comparison of technological characteristics to a predicate device.

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

Since a clinical test set for performance validation (beyond technical specifications) is not described, there is no information on:

• The number of experts used.
• The qualifications of those experts.

4. Adjudication Method for the Test Set

As no specific clinical test set for performance evaluation is described, there is no information on any adjudication method.

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

No MRMC comparative effectiveness study is mentioned in the provided document. The focus is on demonstrating substantial equivalence based on technical specifications and non-clinical testing, not on human reader performance with or without AI assistance.

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

For the specific functions like aEEG signal processing and burst suppression detection, the document describes the algorithms and their technical specifications (e.g., aEEG filter specification, presence of BSR/IBI). The fact that these are listed as device features implies standalone algorithm functionality. However, a formal "standalone performance study" with specific metrics (e.g., sensitivity, specificity for burst detection against a human-annotated ground truth) is not detailed in this document. The comparison is largely at a functional and technical specification level.

7. The Type of Ground Truth Used

For the technical specifications, the ground truth is implicitly defined by:

• Engineering specifications and test methodologies for electrical performance (e.g., sampling rate, resolution, noise, impedance).
• Standardized test signals (e.g., sine wave inputs for aEEG filter specification).
• Reference standards like IEC 60601-1, IEC 60601-2-26, etc. for safety and essential performance.

There is no mention of expert consensus, pathology, or outcomes data being used as ground truth for a clinical validation study in this summary.

8. The Sample Size for the Training Set

The document does not mention any training set size as it concerns hardware/software specifications and regulatory compliance rather than an AI/machine learning model whose performance on classification/detection tasks is being clinically validated against a dataset. While the burst suppression detection software might involve algorithms, the document doesn't delve into its development data.

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

Since no training set is described, no information is provided on how its ground truth might have been established.

Ask a specific question about this device

The QP-160AK EEG Trend Program is a software-only device intended to calculate and display EEG data obtained from the Nihon Kohden specified host device. The QP-160AK is intended to be used by qualified medical practitioners, trained in electroencephalography, who will exercise professional judgment when using the information. The intended use is as follows:

· The EEG and aEEG waveforms are intended to help the user monitor the state of the brain.

· The user-defined Fast Fourier Transform (FFT) parameters of this software (FFT power) are intended to help the user analyze the EEG waveform.

• The burst suppression parameters of this software (interval and bursts per minute) are intended to aid in the identification and characterization of areas of burst-suppression pattern in the EEG.

• The seizure detection component of QP-160AK is intended to mark previously acquired EEG waveforms of adult (greater than or equal to 18 years) that may correspond to electrographic seizures in order to aid in identification of seizure events and help review and annotation of EEG traces by user. EEG should be recorded with full scalp montage at the standard 10/20 system. The notifications for seizure detection are provided. QP-160AK notifications cannot be used as a substitute for real time monitoring of the underlying EEG by a trained expert.

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

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.

The QP-160AK EEG Trend Program is a software-only device. When installed in Nihon Kohden neurology products with EEG measurement function, the device calculates and displays EEG data obtained from the neurology product's system. In addition, the QP-160AK EEG Trend Program identifies trends in the EEG data over extended periods of time in order for trained healthcare professionals to observe changes over time.

The program's existing main features are listed below:

• Calculate and display aEEG, DSA, FFT and burst suppression ratio trend .
• Display up to 64 channel EEG ●
• . Display SpO2 and ETCO2 trends
• Operation by touch panel buttons
• . Data management with Neuro Workbench software

The modification is to add:

• Seizure detection and notification .

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

Device: Nihon Kohden QP-160AK EEG Trend Program
Specific New Feature Tested: Seizure detection component.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the seizure detection component are implicitly defined by the reported performance statistics in comparison to the predicate device or generally accepted clinical utility. While explicit pass/fail thresholds are not given for Nihon Kohden QP-160AK, the comparison to "a similar level of performance" to the predicate, Persyst 12, is key.

The provided graphs (Figures 0 and 3 on page 9) visually compare the QP-160AK's PPA and FDR to several other devices, including its predicate (Persyst12). This comparison serves as evidence for the "substantially equivalent" claim, suggesting that the QP-160AK's performance falls within an acceptable range relative to legally marketed predicate devices.

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

• Sample Size: 139 patients.
• Data Provenance: Archived EEG recordings from patients (18 years and older) admitted to an Epilepsy Monitoring Unit. The country of origin is not explicitly stated in the provided text. The data is retrospective, being "archived EEG recordings."
• Total EEG Recording Time: 556 hours (139 recordings * approximately 4 hours per recording).
• Total Seizures Identified: 145

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

• Number of Experts: Three independent EEG experts.
• Qualifications: "EEG experts" – no further specific qualifications (e.g., years of experience, board certification) are provided in the text.

4. Adjudication Method for the Test Set

• Adjudication Method: Two-third majority rule. This means that for an event to be considered ground truth (a "true seizure"), at least two of the three independent EEG experts had to agree on its presence.

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

• Was an MRMC study done? No, a traditional MRMC comparative effectiveness study where human readers' performance with AI assistance is compared to without AI assistance was not explicitly described.
• Effect size of human readers improvement with AI vs. without AI assistance: Not applicable, as this type of study was not reported. The study focused on the standalone performance of the algorithm against expert-established ground truth.

6. Standalone Performance Study

• Was a standalone performance study done? Yes. The seizure detection component of the QP-160AK was evaluated by comparing its output directly against the "clinical reference standard (ground truth)" established by the expert consensus. The reported PPA and FDR metrics quantify this standalone performance.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. Specifically, "a two-third majority rule" among three independent EEG experts.

8. Sample Size for the Training Set

• The text does not explicitly state the sample size used for the training set. It only describes the test set.

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

• The text does not describe how the ground truth for the training set was established. It focuses solely on the ground truth establishment for the test set used for performance evaluation.

Ask a specific question about this device

1. Cadwell AmpliScan is a software-only device indicated for use with electroencephalographic (EEG) data from Cadwell Arc application software. Cadwell AmpliScan is distributed solely for use with Cadwell Arc software.
2. The Cadwell AmpliScan device is for prescription use only by qualified medical practitioners, trained in Electroencephalography, who will exercise professional judgement when using the information.
3. This device does not provide any diagnostic conclusion about the patient's condition to the user.
4. Cadwell AmpliScan uses electroencephalographic (EEG) data to calculate and display a quantitative aEEG measure. This quantitative measure should always be interpreted by the user in conjunction with review of the original EEG waveforms. The aEEG quantitative measure of Cadwell AmpliScan is intended to monitor the state of the brain.

Cadwell AmpliScan is a software-only device distributed solely for use with the application software commonly known and marketed as Cadwell Arc software. Cadwell Ampliscan software is installed with installation of Arc software, and does not require installation or removal separate from the Arc application. The Cadwell AmpliScan software-only device applies the Amplitude-Integrated EEG (aEEG) algorithm referred to as the Cerebral Function Monitor (CFM) to stored data within the Cadwell Arc software, and stores and displays the results.

The provided text appears to be a 510(k) summary for the Cadwell AmpliScan, a software-only device. This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study with specific acceptance criteria and detailed performance metrics as one might find in a clinical trial report for an innovative device.

Therefore, the information requested by the user, particularly regarding acceptance criteria, sample sizes, expert qualifications, and detailed performance metrics, is not explicitly provided in the document in the format anticipated for a standalone clinical study. The document focuses on comparing the Cadwell AmpliScan to a predicate device based on technological characteristics and software verification/validation.

Here's a breakdown of what can be extracted based on the provided text, and what cannot:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state acceptance criteria or quantify device performance in terms of metrics like sensitivity, specificity, accuracy, or similar measures commonly found in a study proving a device meets specific criteria. Instead, it relies on demonstrating substantial equivalence to a predicate device.

The "Performance Data" section states: "In the Substantial Equivalence Discussion, a comparison of outputs from Cadwell AmpliScan and the predicate with like input data demonstrate the resulting equivalence of analysis and display." This implies that the 'acceptance criterion' was that the output of Cadwell AmpliScan should be equivalent to that of the predicate device when given the same input data.

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

The document does not specify a "test set" in terms of patient data. The evaluation was likely performed using various EEG data files (the "like input data") to compare outputs, but the number of such files or their origin (country, retrospective/prospective) is not mentioned. Given it's a software device processing existing data, the data would inherently be retrospective.

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

The concept of "ground truth" established by experts for a test set is not discussed in this document. The evaluation was focused on the software's ability to produce equivalent outputs to an existing, legally marketed device (the predicate). The assessment of equivalence typically involves technical comparisons of algorithm implementation and output, not expert clinical interpretation of novel results.

4. Adjudication Method for the Test Set:

Not applicable, as no expert-adjudicated test set is described.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done:

No, an MRMC study was not done. The document does not mention human readers or AI assistance in a comparative effectiveness study. The device is software that calculates and displays a quantitative aEEG measure, not an AI to assist human readers directly in diagnosis.

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

Yes, this was a standalone (algorithm only) evaluation. The device itself is "software-only" and its performance was assessed by comparing its outputs to a predicate device. The document explicitly states, "Cadwell AmpliScan uses electroencephalographic (EEG) data to calculate and display a quantitative aEEG measure." It also clarifies that "This device does not provide any diagnostic conclusion about the patient's condition to the user." and "This quantitative measure should always be interpreted by the user in conjunction with review of the original EEG waveforms." This indicates that the device operates as an algorithm generating a measure, with interpretation left to a qualified medical practitioner.

7. The Type of Ground Truth Used:

The "ground truth" in this context is the output generated by the predicate device for the same input data, as the study aims to show equivalence. The document states a "comparison of outputs from Cadwell AmpliScan and the predicate with like input data demonstrate the resulting equivalence of analysis and display." This implies the predicate's output served as the reference for equivalence.

8. The Sample Size for the Training Set:

The document does not mention any "training set." This type of 510(k) submission, particularly for a device implementing a known algorithm (Cerebral Function Monitor/CFM), typically doesn't involve machine learning training on a large dataset. The substantial equivalence is based on the algorithm's implementation matching that of the predicate.

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

Not applicable, as no training set or ground truth for a training set is mentioned.

Ask a specific question about this device

The Background Pattern Classification algorithm is intended for:
· Neonatal patients, defined as from birth to 28 days post-delivery, and corresponding to a post-conceptual age of 37 to 46 weeks, in clinical environments such as the intensive care unit, operating room, and for clinical research.
• To analyze and identify background patterns in aEG, including continuous and discontinuous activity, burst suppression, low voltage, and inactive patterns. The aEEG must be obtained from a pair of parietal electrodes located at positions corresponding with P3 and P4 of the International 10/20 System. The background pattern classification algorithm must be reviewed and interpreted by qualified clinical practitioners.
The device does not provide any diagnostic conclusion about the patient's condition.

BPc™ is a software only product that identifies background patterns seen on aEEG signal recorded from a pair of parietal electrodes (P3-P4) in neonates, defined as from birth to 28 days post-delivery, and corresponding to a post-conceptual age of 37 to 46 weeks. The classification of aEEG background pattern into one of five different classes is done in accordance with the scoring scheme described in the following table:

1. Continuous (C): Continuous activity with lower (minimum) amplitude around (5 to) 7 to 10 µV and maximum amplitude of 10 to 25 (to 50) µV.
2. Discontinuous (DC): Discontinuous background with minimum amplitude variable, but below 5 µV, and maximum amplitude above 10 µV.
3. Burst-suppression (BSA): Discontinuous background with minimum amplitude without variability at 0 to 1 (2) µV and bursts with amplitude >25 µV. BS+ denotes burst density >100 bursts/h, and BS- means burst density

1. Acceptance Criteria and Reported Device Performance

Detailed PPA and FDR by Pattern:

2. Sample Size for the Test Set and Data Provenance

• Sample Size: Not explicitly stated but derived from the information on "EEG studies" for the clinical validation. Given the gender distribution (36 female/28 male), the test set involved 64 patients/EEG studies.
• Data Provenance: Not explicitly stated, but the study was conducted by Natus Medical Incorporated in Canada, suggesting the data may be from Canada or a similar clinical environment. The study is retrospective, as it uses de-identified, randomized EEG studies that were provided to experts.

3. Number of Experts and Qualifications

• Number of Experts: 3
• Qualifications of Experts: "board certified neurophysiologists"

4. Adjudication Method for the Test Set

The adjudication method was not explicitly a "2+1" or "3+1" approach. Instead, it seems to have used a "consensus-based" ground truth methodology. The "panel of 3 EEG board certified medical professionals" independently, blindly, and manually marked background pattern states. The "Gold standard" for comparison was defined as "background pattern as classified by a panel of 3 EEG board certified medical professionals." While the exact mechanism for how the three independent markings were combined to form the "gold standard" is not detailed (e.g., majority vote, discussion to consensus), it implies a form of expert consensus without a clear formal adjudication rule like 2+1. The results report "Inter Rater Performance" for each reviewer against a collective "gold standard" (likely the consensus or majority of the other two, though not explicitly stated for this table).

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

No MRMC comparative effectiveness study was done involving human readers with and without AI assistance. The study focuses purely on comparing the standalone performance of the AI algorithm against a "gold standard" established by human experts. It also includes inter-rater variability among human experts.

6. Standalone (Algorithm Only) Performance

Yes, a standalone performance study was done. The "Algorithm Performance Comparison" table directly reports the diagnostic performance (PPA and FDR) of the BPc™ algorithm when compared to the "gold standard" established by the panel of experts.

7. Type of Ground Truth Used

The type of ground truth used was expert consensus. It was established by "a panel of 3 EEG board certified medical professionals" who independently, blindly, and manually marked background pattern states.

8. Sample Size for the Training Set

The sample size for the training set is not provided in the document. The document describes the clinical validation dataset (test set) but no information regarding the dataset used to train the algorithm.

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

As the sample size and nature of the training set are not provided, how the ground truth for the training set was established is also not detailed in the document.

Ask a specific question about this device

The AE-918P EEG Neuro Unit is an 8 channel EEG measuring unit that connects to a Nihon Kohden patient monitor and is intended to monitor brain function. The unit amplifies and analyzes EEG and displays the EEG waveform and the result of analysis on the patient monitor.

The AE-918P EEG Neuro Unit includes the calculation of a set of quantitative measures intended to monitor and analyze the EEG waveform. These include quantitative EEG functions such as SEF, MDF, PPF, TP, CSA, DSA, %Theta, %Alpha, %Beta, %Gamma, Abs Delta, Abs Alpha, Abs Beta, and Abs Gamma. These quantitative EEG measures should always be interpreted in conjunction with review of the original EEG waveform. The aEEG functionality included in the AE-918P EEG Neuro Unit is intended to monitor the state of the brain.

The device is intended for use by medical personnel in any location within a medical facility, physician's office, laboratory, clinic or nursing home under direct supervision of a medical professional.

The AE-918P is an 8 channel digital Electroencephalography (EEG) that connects to a Nihon Kohden patient monitor. It receives EEG data from patients and digitizes the signals, the waveforms and analysis are displayed on a Nihon Kohden Patient Monitor.

The AE-918P is contained in a small enclosure that contains the EEG amplifier and digital circuitry. This enclosure is mounted to the bottom of the patient monitor and connects to the patient monitor through the multilink cable.

The Nihon Kohden AE-918P Neuro Unit is an 8-channel EEG measuring unit that connects to a Nihon Kohden patient monitor. It amplifies and analyzes EEG and displays the EEG waveform and analysis results on the patient monitor, intended for monitoring brain function.

Here's an analysis of its acceptance criteria and the study proving its performance, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list "acceptance criteria" in a quantitative manner for specific performance metrics like accuracy, sensitivity, or specificity. Instead, the performance testing focuses on ensuring the device meets established safety and performance standards for electroencephalographs and demonstrating substantial equivalence to predicate devices. The "reported device performance" is largely qualitative, stating that the device "performed within specifications" and was "equivalent in safety and effectiveness" to its main predicate.

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

The document explicitly states: "No Clinical testing was required." This indicates that the performance testing for this device primarily relied on bench testing, comparison to predicate devices, and internal validation/verification testing according to design control processes. Therefore, there is no test set of patient data and consequently, no specified data provenance (country of origin, retrospective/prospective).

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

As no clinical testing was performed and no patient-specific "test set" was used, there were no experts used to establish ground truth for such a set. The "ground truth" for the device's technical specifications and performance was based on engineering validation against established standards and comparison to well-characterized predicate devices.

4. Adjudication Method for the Test Set

Since no clinical test set was used, there was no adjudication method employed for a test set.

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. This device is an EEG measuring unit, not an AI-assisted diagnostic tool that would typically involve human readers interpreting AI output. Its function is to amplify, analyze, and display EEG waveforms and quantitative measures for medical personnel to interpret in conjunction with the original waveform.

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

The device's functionality involves "amplifies and analyzes EEG and displays the EEG waveform and the result of analysis on the patient monitor." The quantitative EEG measures "should always be interpreted in conjunction with review of the original EEG waveform." This implies the device provides analytical output that is not intended to be used in a standalone, algorithm-only fashion without human interpretation and review of the raw EEG data. The performance testing verified the device's ability to calculate and display these measures and trends accurately, rather than its diagnostic performance in a standalone mode.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The "ground truth" for demonstrating the device's performance was primarily:

• Engineering specifications and design requirements: Verifying that the device met its intended technical parameters.
• Regulatory standards: Conformance to IEC 60601 series for safety and performance of medical electrical equipment, particularly for electroencephalographs.
• Predicate device characteristics: The comparison testing aimed to show "substantial equivalence" of the calculation and display of EEG trends to those of legally marketed predicate devices. This indicates that the established, accepted performance of existing devices served as a benchmark for "ground truth."

8. The Sample Size for the Training Set

The document does not mention any "training set" of data. As previously noted, no clinical testing was required, and the device's validation appears to be based on engineering tests and comparison to existing technologies, not on machine learning or AI models that require specific training data.

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

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

Ask a specific question about this device

The intended use of the CerebraLogik is to monitor the state of the brain by acquisition of EEG signals and display the stored EEG in a compressed form of Amplitude Integrated EEG - aEEG and in conjunction with other clinical data.

The CerebraLogik consists of a dual channel EEG amplifier that is put near the monitored patient. The amplifier is connected, using an interface cable, to a Mennen Medical patient monitor via the UIM input of the monitor. The monitor has display options for both real time EEG and history of Amplitude Integrated EEG - aEEG. The monitor stores both EEG and aEEG signals for the duration of the EEG monitoring.

The provided documentation describes the CerebraLogik aEEG device, intended to monitor brain activity by acquiring EEG signals and displaying them as Amplitude Integrated EEG (aEEG). The submission is a Traditional 510(k) for the addition of this aEEG functionality to Mennen Medical's existing VitaLogik monitor family, asserting substantial equivalence to the Olympic CFM 6000 (K031149).

Here's an analysis of the acceptance criteria and the study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of "acceptance criteria" for clinical performance. Instead, it focuses on demonstrating substantial equivalence to a predicate device (Olympic CFM 6000) through bench testing and an animal study, comparing device characteristics and output patterns. The reported "performance" is that the CerebraLogik produced "same aEEG graphs" and "same aEEG pattern" as the predicate device under specific testing conditions.

Here's a table summarizing the comparative characteristics and the reported findings for direct comparison points:

Note: The acceptance criteria are implicitly drawn from the predicate device's specifications and the expectation that the new device should perform equivalently or better without raising new safety/effectiveness concerns.

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

• Bench Test (Simulated Use): EEG signal from one Grass EEG Simulator model EEG SIM. The signal was inserted in parallel to both devices for periods of 3 hours. Data provenance for the simulated signal is not explicitly stated but implies a synthetic source, not patient data.
• Animal Study: EEG signals from anesthetized piglets. The number of piglets is not specified but it states "piglets" (plural). The recordings were made in parallel on both devices for periods of 3 and 7 hours. Data provenance is an animal model, not human patients. This data is retrospective for the purpose of the study as it was collected for comparison.

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

The document does not mention the use of human experts to establish ground truth for either the bench test or the animal study. The "ground truth" seems to be the output of the predicate device itself, with the new device's output being compared against it.

4. Adjudication Method for the Test Set

Since human experts were not used to establish ground truth or compare outcomes, there was no adjudication method described. The comparison was based on direct observation of "same aEEG graphs" and "same aEEG pattern" by the study's researchers/engineers.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study described focuses on technical equivalence and functional comparison of the device's output to a predicate device and simulated/animal signals, not on human reader performance with or without AI assistance.

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

The studies described are essentially standalone performance evaluations of the device's signal acquisition and processing capabilities. The device's output (aEEG graphs/patterns) was compared directly to the predicate device and the input signals, without a human in the loop affecting the device's generation of the aEEG display. The product itself, CerebraLogik aEEG, is a standalone module integrated into a monitor, providing processed EEG data for clinicians to interpret, but its output generation is algorithm-only.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• For the bench test, the ground truth was effectively the known electrical signal from the Grass EEG Simulator and the output of the predicate device (Olympic CFM 6000) when fed this signal.
• For the animal study, the ground truth was the physiological EEG signal from anesthetized piglets, and the comparative output of the predicate device (Olympic CFM 6000) under the same conditions.

In both cases, it's a form of empirical comparison against a known input or a legally marketed predicate device's output, rather than an expert consensus, pathology, or outcomes data from human patients.

8. The Sample Size for the Training Set

The document does not mention any training set. The CerebraLogik aEEG module appears to be based on fixed algorithms for filtering, rectifying, and compressing EEG signals, rather than a machine learning model that would require a distinct training set. The descriptions focus on the implementation of these algorithms and their output comparison, not on their development or training.

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

Since no training set is mentioned and the device's technology appears to be based on established signal processing rather than machine learning, this question is not applicable based on the provided text.

Ask a specific question about this device

The Olympic Brainz Monitor (OBM) is a three channel electroencephalograph (EEG) acquisition system intended to be used in a hospital environment to record, collect, display and facilitate manual marking of aEEG recordings.

• The signals acquired from P3-P4, C3-P3 and C4-P4 channels are intended for use only with neonatal patients (defined as from birth to 28 days post-delivery, and corresponding to a postconceptual age of 24 to 46 weeks) to display aEEG for monitoring the state of the brain.
• The signals acquired from P3-P4 channel is intended to assist in the assessment of Hypoxic-Ischemic Encephalopathy severity and long-term outcome, in full term neonates (postconceptual age of 37-46 weeks) who have suffered a hypoxic-ischemic event.
• The RecogniZe seizure detection algorithm is intended to mark sections of EEG/aEEG that may correspond to electrographic seizures in only the centro-parietal regions of full term neonates (defined as from birth to 28 days post-delivery, and corresponding to a postconceptual age of 37 to 46 weeks). EEG recordings should be obtained from centro-parietal electrodes (located at P3, P4, C3 and C4 according to 10/20 system). The output of the Recognize algorithm is intended to assist in post hoc assessment of EEG/aEEG traces by qualified clinical practitioners, who will exercise professional judgment in using the information.
  The Olympic Brainz Monitor does not provide any diagnostic conclusion about the patient's condition.

The Olympic Brainz Monitor is a three-channel electroencephalograph (EEG) system, as per 21 CFR §882.1400: a device used to measure and record the electrical activity of the patient's brain by placing two or more electrodes on the head. The device does not introduce, transfer or deliver any type of energy to the patient. As any other electroencephalograph the device passively record the electroencephalographic activity from the patient trough the hydrogel electrodes and then process the signal for display, analysis and archiving.
The Olympic Brainz Monitor system consists of the following:

• Data Acquisition Box (DAB)
• Touchscreen Monitor
• Roll Stand or optional Desktop Stand
• 9 Neonatal Sensor set (K033010)
• Software
  These components have equivalent configuration and functions to those described in K093949 for the OBM Monitor. The Neonatal Sensor set (cleared on K033010) is an accessory to the device that is the only part that enters into contact with the patient. The sensor guarantees acquisition of the electroencephalographic signal and passively transfers it to the main unit. This is a set of five hydrogel skin electrodes that are attached to the patient's head at one extreme and to the Data Acquisition Box at the other extreme using standard touch-proof connectors.
  The device allows practitioners to acquired, store, review and archive EEG activity from 4 centroparietal locations corresponding to C3, C4. P3 and P4 of the international 10-20 System. The device displays the recorded activity in form of the raw EEG and as amplitude integrated EEG (aEEG).
  In addition the device now includes a seizure detection algorithm (i.e RecogniZe) to allow automated analysis of the recorded EEG. The RecogniZe Seizure Detection Algorithm identifies sections of the EEG trace where seizure activity is detected. The algorithm comprises filtering of the EEG signal, fragmentation of EEG signal into waves, wave-feature extraction, and elementary, preliminary and final detection. The main idea behind the algorithm is to detect heightened regularity in EEG wave sequences using wave intervals, amplitudes and shapes, as increased regularity is the major distinguishing feature of seizure discharges.

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

Acceptance Criteria and Device Performance for RecogniZe Seizure Detection Algorithm

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document argues that RecogniZe is "substantially equivalent to the performance of medical experts confronted with similar task and amount of data" and therefore substantially equivalent to the predicate devices, despite the numerical differences compared to the predicate devices themselves. The comparison is made against expert inter-rater agreement for the specific limited-channel montage.

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

• Number of Events: 421 seizure events
• Total Number of Patients: 82
• Number of Hours (of EEG recordings): 621 hours
• Data Provenance: Retrospective clinical evaluation from neonatal patients seen for routine clinical evaluation at the Neonatal Intensive Care Unit of St. Louis Children's Hospital, USA.

The study included recordings from full term neonates (post-conceptual age of 37 to 46 weeks, defined as from birth to 28 days post-delivery).
To avoid over-weighting, a maximum of 13 events per limited-channel recording were permitted.

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

• Number of Experts: 3
• Qualifications of Experts: Board certified neurophysiologists.

4. Adjudication Method for the Test Set

The document describes how the ground truth was established by experts:

• Experts independently, blindly, and manually marked seizures (no seizure detection algorithm was allowed) in the same manner they would normally do in clinical practice.
• Initially, experts reviewed the full cohort of standard montage recordings (157 of them) marking seizure onset and topography.
• After a 4-week wash-out period, the same reviewers were provided with the limited-channel (C3-P3, C4-P4, and P3-P4) recordings for marking.
• The ground truth used for comparison with the algorithm was the outcome of the expert review on these limited-channel recordings.
• For the inter-rater agreement of experts themselves, individual expert markings were compared against each other (e.g., Rater 1 vs Rater 2, Rater 1 vs Rater 3, Rater 2 vs Rater 3). It isn't explicitly stated if a consensus (e.g., 2+1, 3+1) was used to define the final "gold standard" truth for the algorithm comparison, but rather "the gold standard, defined as seizures detected by a panel of 3 EEG board certified medical professionals" was used. The reported PPA and FDR for the algorithm are compared to the average inter-rater agreement of these experts on the limited-channel montage.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of Human Improvement with AI vs. Without AI Assistance

A classic MRMC comparative effectiveness study, directly comparing human reader performance with AI assistance versus without AI assistance, was not explicitly described for the RecogniZe module.

The study compared the standalone performance of the RecogniZe algorithm against the performance of human experts (who were themselves establishing the ground truth) on the limited-channel montage. It also reported inter-rater agreement among the human experts.

The document states: "RecogniZe is intended as a tool to aid in the assessment of long EEG recordings to help reduced the amount of time devoted to review." However, it does not quantify this reduction or demonstrate increased accuracy of humans when using the AI.

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

Yes, a standalone performance study was done for the RecogniZe algorithm. The algorithm's PPA and FDR were calculated by comparing its output directly against the "gold standard" established by the panel of 3 experts on the limited-channel EEG recordings.

7. The Type of Ground Truth Used

The ground truth used was expert consensus / expert marking. Specifically, it was defined as "seizures detected by a panel of 3 EEG board certified medical professionals" who independently marked seizures on de-identified and randomized EEG recordings.

8. The Sample Size for the Training Set

The document does not report the sample size used for the training set for the RecogniZe algorithm. It only details the "Testing Dataset."

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

The document does not describe how the ground truth for the training set was established, as it does not provide details on the training set itself. The information provided pertains solely to the clinical validation (testing) dataset.

Ask a specific question about this device

The QP-160AK Trend program is a software-only device intended to be installed on the EEG-1200A series electroencephalograph to record, calculate, and display EEG data obtained from the EEG-1200A system. This device is intended to be used by qualified medical practitioners, trained in Electroencephalography, who will exercise professional judgment when using the information.

The intended use for each of the software's outputs is as follows:

• The EEG and aEEG waveforms are intended to help the user monitor the state of the brain.
• The user-defined Fast Fourier Transform (FFT) parameters of this software (FFT power) are intended to help the user analyze the EEG waveform.
• The burst suppression parameters of this software (interval and bursts per minute) are intended to aid in the identification and characterization of areas of burst-suppression pattern in the EEG.

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

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.

The OP-160AK Trend program is a software-only device intended to be installed on the EEG-1200A series electroencephalograph to record, calculate, and display EEG data obtained from the EEG-1200A system. The QP-160AK EEG Trend program is the same as the previous version of QP-160AK cleared under 510k but has two new trends available (DSA Asymmetry trend and FFT Power Asymmetry trend).

Here's an analysis of the acceptance criteria and study information for the Nihon Kohden QP-160AK EEG Trend Program, based on the provided 510(k) summary:

This 510(k) submission, K120485, is for an updated version of an existing device (K092573, also Nihon Kohden QP-160AK EEG Trend Program) with the addition of two new trends: DSA Asymmetry trend and FFT Power Asymmetry trend.

The regulatory approach taken is substantial equivalence to the previous version and to other predicate devices (BrainScope Zoom-100DC and Applied Neuroscience NeuroGuide Analysis System) that already include these new trend functionalities.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of this 510(k) submission, where the new features leverage existing, cleared technology, the "acceptance criteria" are primarily based on the functional equivalence and proper operation of these features. There are no explicitly stated numerical performance metrics (e.g., sensitivity, specificity, accuracy) akin to what might be seen for a diagnostic AI device.

Study Information

This submission does not involve a traditional clinical study with patient cohorts or expert assessments in the way an AI diagnostic algorithm might. Instead, the "study" demonstrating performance is primarily non-clinical verification and validation testing, and comparison to predicate devices, focusing on functional equivalence.

1. 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 specify the sample size for any test set or the provenance of data used for verification and validation. It only mentions "comparison testing to ensure the substantial equivalence of the calculation and display of EEG trends." This type of testing would typically involve a set of pre-recorded EEG data, but the details are not provided.
   • It is not clear if "test set" here refers to specific patient data or internal engineering test cases. Given the context, it's more likely the latter.

2. 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. There is no mention of external experts or establishing ground truth based on expert review for specific patient cases in a clinical study context. The "ground truth" for functional verification would be the expected output of the algorithms as derived from engineering specifications and comparison to the predicate device's known outputs.

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

   • Not applicable. No clinical adjudication method is described or implied.

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 comparative effectiveness study was done or reported. This device is an EEG trend program, which assists qualified practitioners in analyzing EEG data, but it is not presented as an AI-powered diagnostic tool that directly "improves" reader performance in a quantifiable clinical trial. It provides visualization tools for interpretation.

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

   • Yes, in a sense. The "comparison testing" and "validation and verification testing" would represent a standalone evaluation of the algorithm and its display capabilities against predefined specifications and the predicate device's output. The device itself is "software-only" and is intended to be installed on an EEG system. However, its intended use is always with a "qualified medical practitioner, trained in Electroencephalography." It does not provide diagnostic conclusions independently.

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

   • The "ground truth" for this type of submission is likely the expected computational output as derived from the established algorithms used in the predicate devices and the mathematical principles of DSA and FFT. It's not a clinical ground truth like pathology or expert consensus on a diagnosis. It's about the accurate calculation and graphical representation of EEG features.

7. The sample size for the training set:

   • Not applicable. This is not an AI/ML device that requires a training set in the typical sense. It implements established signal processing algorithms (DSA, FFT) that do not learn from data.

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

   • Not applicable, as there is no training set for an AI/ML algorithm.

Ask a specific question about this device

The QP-160AK Trend program is a software-only device intended to be installed on the EEG-1200A series electroencephalograph to record, calculate, and display EEG data obtained from the EEG-1200A system. This device is intended to be used by qualified medical practitioners, trained in Electroencephalography, who will exercise professional judgment when using the information.

The intended use for each of the software's outputs is as follows:

• . The EEG and aEEG waveforms are intended to help the user monitor the state of the brain.
• . The user-defined Fast Fourier Transform (FFT) parameters of this software (FFT power) are intended to help the user analyze the EEG waveform.
• The burst suppression parameters of this software (interval and bursts per . minute) are intended to aid in the identification and characterization of areas of burstsuppression pattern in the EEG.

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

The QP-160AK EEG Trend program is a software program stored on electronic media such as CD Rom.

The EEG-1200A OP-160AK Trend program is a device which is installed on the electroencephalograph EEG-1200A Series and records the EEG waveforms and identifies trends in the EEG data over extended periods of time in order for trained health care professionals to observe changes over time.

The QP-160AK design features are as follows:

• Trend display of aEEG and Burst suppression ratio .
• Display of EEG waveform maximum of 64 channels
• DC Trend display including analog inputs .
• Operations of functions by control buttons adapted to touch panels .
• Data management by NeuroWorkbench .

The Nihon Kohden QP-160AK EEG Trend Program is a software-only device intended to be installed on the EEG-1200A series electroencephalograph to record, calculate, and display EEG data. It assists qualified medical practitioners in monitoring brain state, analyzing EEG waveforms using FFT power, and identifying/characterizing burst-suppression patterns. The device does not provide diagnostic conclusions.

Here's an analysis of the acceptance criteria and the study performed, based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative "acceptance criteria" for the QP-160AK EEG Trend Program in terms of specific performance metrics (e.g., accuracy, sensitivity, specificity) for its outputs (aEEG, burst suppression parameters, FFT power). Instead, the performance testing focuses on conformance to design requirements and comparative equivalence to predicate devices.

The "acceptance criteria" are implied to be that the device:

• Performs within its specifications (which are not detailed beyond its functional description).
• Demonstrates substantial equivalence in calculation and display of burst suppression ratio and aEEG compared to predicate devices.

The conclusion of substantial equivalence states: "The comparison of technological characteristics and performance testing of the QP-160AK EEG Trend Program demonstrate that its safety, effectiveness, and performance are equivalent to the specified predicate devices." |

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

The document does not specify the number of EEG recordings or the sample size used for the performance or comparison testing. It also does not mention the country of origin of the data or whether the data was retrospective or prospective.

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

The document does not specify the number of experts used to establish ground truth or their qualifications. The device is intended to be used by "qualified medical practitioners, trained in Electroencephalography, who will exercise professional judgment." This implies that expert judgment would be the basis for evaluating results, but the specifics of an expert panel for testing are not detailed.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) used for establishing ground truth or evaluating the device's performance.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted or reported in this 510(k) summary. The study described focuses on standalone performance and comparison to predicate devices, not on the improvement of human readers with AI assistance.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone performance assessment was done. The document states: "The QP-160AK EEG Trend Program was subjected to safety and performance testing procedures. The QP-160AK EEG Trend Program has undergone validation and verification testing to ensure conformance to all design requirements." This implies that the algorithm's functionality was evaluated on its own merits as designed. Additionally, comparison testing to ensure substantial equivalence of calculations and display to predicate devices also falls under a standalone assessment of the device's output.

7. Type of Ground Truth Used

The type of ground truth used is implicitly expert assessment and comparison to established predicate device outputs, rather than pathology, outcomes data, or a formalized consensus panel. The device is intended to "help the user monitor," "help the user analyze," and "aid in the identification and characterization," suggesting that the "truth" is what expert clinicians would interpret from similar data or how predicate devices present the same information.

8. Sample Size for the Training Set

The document does not specify any sample size for a training set. This is consistent with the device type, which is a trend program for an existing EEG system, rather than a machine learning model that typically requires a large training dataset. Its functionality appears to be based on established signal processing algorithms for aEEG, FFT, and burst suppression.

9. How Ground Truth for the Training Set Was Established

Since a distinct "training set" for a machine learning model is not applicable or mentioned, the method for establishing ground truth for such a set is also not described. The device's performance validation relies on conformance to design requirements and comparison to predicate devices, implying that its underlying algorithms are based on established physiological principles and methods.

Ask a specific question about this device

The Olympic Brainz Monitor (OBM) is a three channel electroencephalograph (EEG) acquisition system intended to be used in a hospital environment to record, collect, display and facilitate manual marking of aEEG recordings.

• . The signals acquired from P3-P4, C3-P3 and C4-P4 channels are intended for use only with neonatal patients (defined as from birth to 28 days post delivery, and corresponding to a postconceptual age of 24 to 46 weeks) to display aEEG for monitoring the state of the brain.
• The signals acquired from P3-P4 channel is intended to assist in the prediction of and severity . of Hypoxic-Ischemic Encephalopathy and long-term outcome in full term neonates (postconceptual age of 37-46 weeks) who have suffered a hypoxic-ischemic event.
  The Olympic Brainz Monitor does not provide any diagnostic conclusion about the patient's condition.

a three channel electroencephalograph (EEG) acquisition system intended to be used in a hospital environment to record, collect, display and facilitate manual marking of aEEG recordings.

This document, a 510(k) clearance letter for the Natus Medical Incorporated Olympic Brainz Monitor, does not contain the detailed information required to describe the acceptance criteria and the study that proves the device meets those criteria.

The letter is the FDA's clearance, indicating substantial equivalence to a predicate device. However, it does not include:

• A table of acceptance criteria and reported device performance.
• Details about the sample size, data provenance, number/qualifications of experts, or adjudication method for a test set.
• Information on MRMC comparative effectiveness studies or human reader improvement data.
• Results from standalone algorithm performance studies.
• The type of ground truth used in a study.
• Sample size or ground truth establishment methods for a training set.

The letter primarily confirms that the device, an electroencephalograph acquisition system, is substantially equivalent to existing devices for its stated indications for use, which include recording, displaying, and facilitating manual marking of aEEG recordings in neonatal patients, and assisting in the prediction of and severity of Hypoxic-Ischemic Encephalopathy in full-term neonates.

To obtain the information requested, one would typically need to review the actual 510(k) submission document (K093949) to the FDA, which would contain the performance summary and details of any studies conducted to support the substantial equivalence claim.

Ask a specific question about this device