(28 days)
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.
| Acceptance Criteria Category | Specific Criteria | Reported Device Performance / Justification |
|---|---|---|
| Functional Equivalence (New Trends) | The new DSA Asymmetry trend functions equivalently to the DSA Asymmetry trend in the predicate devices (BrainScope Zoom-100DC, Applied Neuroscience NeuroGuide Analysis System). | The submission explicitly states: "The Brainscope-100DC and the Applied Neuroscience Neuroguide Analysis System (K041263) provide the same DSA Asymmetry trend... as the new QP-160AK." This implies a functional comparison was made and found to be equivalent. |
| The new FFT Power Asymmetry trend functions equivalently to the FFT Power Asymmetry trend in the predicate devices (BrainScope Zoom-100DC, Applied Neuroscience NeuroGuide Analysis System). | The submission explicitly states: "...and the Applied Neuroscience Neuroguide Analysis System (K041263) provide the same... FFT Power Asymmetry trend as the new QP-160AK." This implies a functional comparison was made and found to be equivalent. | |
| System Integration & Safety | The updated QP-160AK EEG Trend Program integrates safely and correctly with the EEG-1200A series electroencephalograph. | "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." |
| Calculation & Display Accuracy | The calculations and display of all EEG trends (including new and existing) are accurate and within specifications. | "...the system has undergone comparison testing to ensure the substantial equivalence of the calculation and display of EEG trends. These tests verified that the device performed within specifications." |
| Intended Use Compliance | The device continues to meet its stated intended use for monitoring, analyzing, and aiding in identification/characterization of patterns, without providing diagnostic conclusions. | The Intended Use statement remains consistent, and the safety and functional testing would confirm that the device operates within the bounds of this intended use. |
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.
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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.
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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.
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Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not applicable. No clinical adjudication method is described or implied.
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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.
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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.
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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.
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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.
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How the ground truth for the training set was established:
- Not applicable, as there is no training set for an AI/ML algorithm.
§ 882.1400 Electroencephalograph.
(a)
Identification. An electroencephalograph is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.(b)
Classification. Class II (performance standards).