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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

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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