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510(k) Data Aggregation
(147 days)
ASPECT MEDICAL SYSTEMS ZIPPREP EEG SENSOR
The Aspect EEG Sensor is applied directly to the patient's skin to enable recording of electro-physiological (such as EBG) signals.
The Aspect Medical Systems, Inc. Zipprep™ Disposable EEG Sensor (hereafter referred to as the Aspect EEG Sensor, is a rectangular shaped, pre-gelled array of three (3) Zipprep electrodes that is applied to the patient's skin to record electro-physiological (such as EEG) signals. It is a low impedance, single patient use, disposable electrode sensor that is designed for application to the frontal/temporal area. The main body of the Aspect EEG Sensor, which houses two (2) electrodes, is placed on the forehead. The satellite, which houses one (1) electrode, is placed over the temple area.
The provided text describes the Aspect Medical Systems, Inc. Zipprep EEG Sensor (K961821). Here's an analysis of the acceptance criteria and supporting studies based on the provided information:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria Category | Specific Criteria | Reported Device Performance |
|---|---|---|
| Biocompatibility | Primary Skin Irritation | Pass; not a primary skin irritant |
| Cytotoxicity | Pass; results indicate the device passes relative to its intended use | |
| Delayed Contact Sensitization | Results due May 31, 1996 (No final "pass" or "fail" reported in this document excerpt) | |
| Electrical Performance | Adherence to American National Standard for Disposable ECG Electrodes (AAMI, ANSI Dec 1991) | Passed all testing in accordance with the Standard |
| Clinical Performance | Impedance evaluation | Performed in the manner intended |
| Signal quality evaluation | Performed in the manner intended | |
| Usability evaluation | Performed in the manner intended | |
| Skin irritation evaluation | Performed in the manner intended |
2. Sample Size Used for the Test Set and Data Provenance
The document mentions "A clinical evaluation was conducted at Ohio State University." However, it does not specify the sample size (number of participants/sensors) used for this clinical evaluation.
Regarding data provenance:
- Country of origin: United States (Ohio State University)
- Retrospective or prospective: The document does not explicitly state whether the clinical evaluation was retrospective or prospective. However, clinical evaluations are generally prospective studies designed to assess device performance in real-world or simulated clinical settings.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document does not provide any information on the number of experts used or their qualifications for establishing ground truth in the clinical evaluation. The assessment of "impedance, signal quality, usability, and skin irritation" would likely involve clinicians and possibly technical experts, but their specific roles and expertise 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, none) for the clinical evaluation. The results are summarized as indicating the sensor "performs in the manner in which it is intended," suggesting a direct assessment rather than a complex multi-reader adjudication process typically seen in image interpretation.
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. This document describes an EEG sensor, not an AI-powered diagnostic device or an imaging product. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and was not performed.
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was done
No. The device is a physical EEG sensor, a medical accessory designed to acquire physiological signals. It does not have an "algorithm-only" performance in the sense of an AI model analyzing data. Its performance is entirely linked to its physical interaction with the patient and its ability to accurately transmit signals for human or automated interpretation.
7. The Type of Ground Truth Used
For the clinical evaluation, the "ground truth" for assessing impedance, signal quality, usability, and skin irritation would be based on:
- Direct measurements: For impedance and signal quality (e.g., comparing to established benchmarks or expected physiological signals).
- Clinical observation and assessment: For usability and skin irritation, likely involving qualified healthcare professionals observing application, removal, and patient skin response.
- User feedback: For usability.
It is not explicitly stated as expert consensus, pathology, or outcomes data in the typical sense of AI algorithm validation.
8. The Sample Size for the Training Set
The document describes a physical medical device (an EEG sensor), not a machine learning model. Therefore, the concept of a "training set" for an algorithm is not applicable to this device. The development and testing focused on engineering specifications, biocompatibility, and clinical performance.
9. How the Ground Truth for the Training Set Was Established
As explained above, the concept of a "training set" and associated ground truth establishment is not applicable to this physical medical device.
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