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510(k) Data Aggregation
(256 days)
KVIKNA EHF
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:
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):
Feature/Characteristic | Subject Device Performance (Lifelines iEEG) | Predicate Device (DG Nervus/NicoletOne) |
---|---|---|
Intended Use | Acquisition, display, archive, storage, and analysis of physiological signals. Help user monitor and analyze EEG. Does not provide diagnostic conclusion. | Acquisition, display, store, and archive electroencephalographic signals. |
Intended User | Qualified medical practitioner trained in Electroencephalography | Qualified medical practitioner trained in Electroencephalography |
Population Age | All age groups | All age groups |
Use Environment | Hospital, clinics, patients home | Hospital, clinic, patients home |
Regulation Number | 21 CFR 882.1400 | 21 CFR 882.1400 |
Product Code | GWQ, OLT | GWQ |
Device allows acquisition of physiological signals | Yes | Yes |
Device allows display, archive, review, and analysis of physiological signals | Yes | Yes |
Identifies spikes | No | Yes |
Identifies seizures | No | Yes |
Displays calculated EEG measures | Yes | Yes |
Calculated EEG measures displayed | Spectrum, Power Spectrum Density, band power, spectral edge, peak frequency | Spectrum, Spectrogram, band power, peak frequency, spectral edge |
Users can add/delete events | Yes | Yes |
Number of EEG channels | Software: up to 128; Hardware: up to 32 | Up to 512 |
Type of EEG recording supported | EDF, NicoletOne, Lifelines iEEG | EDF, NicoletOne |
Type of EEG analysis | Clinical, ambulatory, long term monitoring | Clinical, ambulatory, long term monitoring |
Photic activation of the EEG | Yes | Yes |
Differential input impedance | >20 Mohms | > 20MΩ |
Common mode input impedance | >100 Mohms | > 100MΩ |
Channel equivalent input noise |
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(117 days)
KVIKNA EHF
The Lifelines iEEG is a software system that displays physiological signals. The intended user of this product is a qualified medical practitioner trained in Electroencephalography. This device is intended to be used by qualified medical practitioners who will exercise professional judgment in using the information.
- The Lifelines iEEG software allows display, archive, review and analysis of . physiological signals.
- Lifelines iEEG also includes the display of a quantitative EEG plot, 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 software system used to manage and review EEG examinations. It works on data acquired by third party EEG equipment that is imported into the system. 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 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 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 two main components of Lifelines iEEG are iEEG Centrum and iEEG Review.
Here's an analysis of the provided text regarding the acceptance criteria and study information for the Lifelines iEEG device:
Based on the provided K123665 510(k) Summary, the Lifelines iEEG is a software system for managing and reviewing EEG examinations. It displays physiological signals and provides analysis features like FFT and Artifact Removal. Crucially, the document does not detail specific acceptance criteria or a study proving that the device meets such criteria in terms of clinical performance or accuracy thresholds. Instead, the submission focuses on demonstrating substantial equivalence to predicate devices based on functional similarities and safety considerations.
The device does not provide any diagnostic conclusion and is intended for use by qualified medical practitioners who will exercise professional judgment. This implies that the software acts as a tool for presentation and analysis, rather than a diagnostic algorithm requiring performance metrics like sensitivity, specificity, or AUC.
Here's a breakdown of the requested information based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
Acceptance Criteria (If explicitly stated) | Reported Device Performance |
---|---|
Not explicitly stated as performance metrics. The focus is on demonstrating functional equivalence to predicate devices and safety. | The device performs display, archive, review, and analysis of physiological signals, including quantitative EEG plots (spectrum, band power, spectral edge). It uses conventional signal processing such as re-montaging and band pass filtering, similar to predicate devices. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not describe a specific test set with a defined sample size for evaluating the clinical performance of the Lifelines iEEG in terms of diagnostic accuracy or other performance metrics. The comparison is primarily functional.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
Since there is no described test set for clinical performance evaluation, there's no mention of experts establishing ground truth for such a set.
4. Adjudication Method for the Test Set
As there is no described test set requiring a ground truth, there is no adjudication method mentioned.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of Human Reader Improvement with AI vs. Without AI Assistance
An MRMC study was not conducted or described in this 510(k) summary. The device's purpose is not to assist human readers in a diagnostic task that would be directly compared to unassisted reading, as it doesn't provide diagnostic conclusions or AI-driven interpretations beyond basic signal processing.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
The document does not describe any standalone performance study in terms of diagnostic effectiveness for the algorithm itself. The device is a software system intended to be used by a qualified medical practitioner.
7. The Type of Ground Truth Used
Given the lack of a clinical performance study with acceptance criteria, no specific type of ground truth (e.g., expert consensus, pathology, outcomes data) is mentioned as being used for performance evaluation.
8. The Sample Size for the Training Set
The document does not mention a training set or any deep learning/machine learning models that would require one. The functions described (re-montaging, band pass filtering, FFT analysis, Artifact Removal) are conventional signal processing techniques, not typically "trained" on data in the way AI algorithms are.
9. How the Ground Truth for the Training Set Was Established
As no training set is described, this information is not applicable and not provided.
Summary of Device Evaluation Approach:
The K123665 submission for Lifelines iEEG primarily relies on demonstrating substantial equivalence to predicate devices based on functional characteristics and safety considerations stemming from standard signal processing methods. The key argument is that the Lifelines iEEG performs similar functions (display, archive, review, analysis of EEG signals) to the predicate devices, despite lacking the acquisition and specific spike/seizure detection functionalities. The safety and effectiveness are inferred from the use of conventional, industry-standard methods for signal processing, which are assumed to be similar enough to predicate devices such that the device does not raise new questions of safety or effectiveness. No specific clinical performance metrics or studies proving diagnostic accuracy are presented because the device's intended use is as a display and analysis tool, not a diagnostic aid providing conclusions.
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