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1. CliniscanSM EEG is a clinical decision support system for medical data, to assist healthcare professionals in the visualization and analysis of EEG data.
2. The Seizure Detection component of CliniscanSM EEG is intended to mark previously acquired sections of adult (greater than or equal to 18 years) EEG recordings that may correspond to electrographic seizures, in order to assist qualified clinical practitioners in the assessment of EEG traces.
3. CliniscanSM EEG is intended for use on EEG recordings acquired using full scalp montage according to the standard 10/20 system.
4. The user may annotate the data, and the annotations are stored with the EEG recording in the cloud.
5. CliniscanSM EEG displays standard qEGG features to the user including FFT Power Spectrum. Coherence Mapping (EEG Activity Mapping), Density Spectral Array, EEG Localization Maps, and Wavelet Subband Decomposition (Delta, Theta, Alpha, Beta, and Gamma).
6. CliniscanSM EEG detects seizure events for review by qualified medical professionals.
7. CliniscanSM EEG is designed to provide analytics data derived from a patient's input EEG data. It is NOT designed to provide definitive diagnoses of diseases or treatments of ailments.

Cliniscan®M EEG, a Picofemto product, is a clinical decision support system for medical data, designed to assist healthcare professionals in the visualization and analysis of EEG data. The device provides seizure event detection and calculates many qEEG (quantitative EEG) parameters for display to the user. All analysis (seizure detection and qEEG) are displayed in conjunction with the original EEG recording for review by experts and are designed to be reviewed in the context of the original data.

The Cliniscan®% EEG visualizer provides a user-friendly environment for displaying the available qEEG parameters along with the original EEG recording (see below). The EEG recording display window, time bar, and User Annotations frames remained fixed within the visualizer; however, the bottom portion of the screen contains tabs for the user to navigate the qEEG features. The user may access the FFT Power Spectrum, Coherence Maps, and EEG Topography Maps under the "Combo" tab; Wavelet Subband Decomposition and the FFT Power Spectrum (again) are located under the "Power" tab; the "DSA" tab contains only the Density Spectral Array display.

After uploaded an EEG in EDF format, the user may view the following qEEG Parameters:

1. FFT Power Spectrum
2. Coherence Maps
3. EEG Topographical Maps
4. Wavelet Subband Decomposition
5. Density Spectral Array (DSA)
6. User Annotations
7. Seizure Event Detection
8. Info

Cliniscan®ª EEG is designed to provide industry standard analytics data derived from a patient's input EEG data. It is NOT designed to provide definitive diagnoses of diseases or treatments of ailments.

Cliniscan§™ EEG detects and marks possible seizure events on previously acquired EEG recordings for review by qualified medical professionals.

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

Acceptance Criteria and Device Performance

The core acceptance criteria for the CliniScanSM EEG, specifically for its Seizure Detection component, revolve around its ability to accurately identify seizures (Positive Percent Agreement - PPA) and limit false alarms (False Positives per Hour - Fp/H) when compared to a ground truth established by experts. While specific numerical acceptance thresholds are not explicitly stated in the provided text as pass/fail criteria, the study results are presented with a 95% confidence interval, indicating an evaluation against expected performance. The comparison to predicate devices also implies that performance within a similar range is considered acceptable.

Study Details

1. Sample size used for the test set and the data provenance:

   • Sample Size: 144 subjects, totaling approximately 293 hours of EEG recordings.
   • Data Provenance: Archived clinical data from multiple sites, specifically from an Epilepsy Monitoring Unit (EMU). The recordings are from subjects 18 years of age and greater. Roughly 20% of the recordings are "normal" (from subjects with no history of seizures), and each recording contains no more than 4 seizures to reduce bias. After ground truth determination, 134 seizures were identified.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

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

3. Adjudication method for the test set:

   • Adjudication Method: A "two-thirds majority rule" was used to determine the ground truth for seizure presence. Each expert manually marked any seizure events, and if at least two out of the three experts marked a seizure in a particular segment, it was considered ground truth.

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, a multi-reader multi-case (MRMC) comparative effectiveness study with human readers assisted by AI was not explicitly described. The study primarily focused on the standalone performance of the algorithm against an expert-established ground truth. The inter-rater performance among the experts themselves was assessed, but not a human-in-the-loop scenario with the AI.

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

   • Yes, a standalone performance evaluation of the CliniScanSM EEG seizure detection algorithm was conducted. The algorithm's results were directly compared to the compiled ground truth, and PPA and Fp/H were calculated. The provided results table ([0.687,0.857] for PPA and [0.325,0.540] for Fp/H) represents this standalone performance.

6. The type of ground truth used:

   • Type of Ground Truth: Expert consensus, specifically a "two-thirds majority rule" among three independent and blinded EEG experts.

7. The sample size for the training set:

   • The document states that "archived clinical data from multiple sites was used to train and validate the internal parameters involved in seizure detection." However, the specific sample size for the training set is not provided.

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

   • The document implies that archived clinical data was used for training, but it does not explicitly detail how the ground truth for this training set was established. It only mentions that the test set ground truth was established by three independent and blinded EEG experts using a two-thirds majority rule.

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