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The X4 System is intended for prescription use in the home, healthcare facility, or clinical research environment to acquire, record, transmit and display physiological signals from adult patients. The X4 System acquires, records, transmits and displays electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and/or electromyogram (EMG), accelerometer, acoustical and photoplethesmographic signals. The X4 system only acquires and displays physiological signals, no claims are being made for analysis of the acquired signals with respect to the accuracy, precision and reliability.

The X4 system is used for configurable acquisition of physiological signals. Model X4-E provides for acquisition of three channels of electroencephalography (EEG) and one photoplethesmographic (PPG) signal from a head strip, with an optional channel connected to two sensors via a dual-lead connector with twice the gain. Model X4-M provides four channels of EEG with the dual-lead connector providing the input for reference sensors. Both models measure sound via an acoustic microphone, and movement and position measured via a 3-D accelerometer. The device is designed so it can be affixed by the patient and to record data. Alternatively, a technician can affix the device and display the signals via a wireless connection during acquisition. The X4 system firmware monitors signal quality to ensure that the sensors are properly applied and that high quality signals are being acquired.

The X4 software provides a means to: a) initiate a study and track patient information, b) acquire and save signals to the memory of the device, c) acquire and wirelessly transmit signals from the device, d) upload data saved in the memory of the device to a PC, and e) visually inspect the signal quality.

The acquired signals are saved in a universal data format (European Data Format - EDF). The study record, once saved on the PC, is available for analysis by Advanced Brain Monitoring's Sleep Profiler software application. The X4's downloaded study will reside on either or local PC or a cloud server, which can be a physical or virtual server. Software on the cloud server is accessed via web portal software.

The Advanced Brain Monitoring, Inc. X4 System (K130013) is a device for configurable acquisition of physiological signals, including EEG, EOG, ECG, EMG, accelerometer, acoustical, and photoplethysmographic signals. It is intended for prescription use in various environments to acquire, record, transmit, and display these signals from adult patients.

The documentation indicates that no clinical studies were performed to establish substantial equivalence for this particular submission (K130013). Instead, the determination of substantial equivalence was based on non-clinical tests, specifically focusing on risk management and software testing. The X4 System (K130013) is described as identical to a previously cleared X4 System (K120447), with the only change being the development of a Device Manager Module accessible via a web portal, which duplicates functions already present in the PC software.

Therefore, the following information is based on the provided text, and it's important to note that the acceptance criteria and performance are related to the software functionality as evaluated for this specific 510(k) submission, rather than the accuracy of physiological signal acquisition, for which no new claims are being made.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size: The document does not specify a numerical sample size for the test set. The evaluation was based on "thoroughly tested through verification of specifications and validation, including software validation." This typically implies a systematic testing process of software functions rather than a patient-based test set size.
• Data Provenance: Not applicable in the context of this software-focused non-clinical evaluation. The tests were performed on the software itself.

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

Not applicable. The ground truth for this software evaluation was the expected functional behavior of the software, as defined by its specifications. The evaluation confirmed whether the software performed these functions identically between two interfaces (desktop vs. web portal). This does not involve expert interpretation of data.

4. Adjudication method for the test set

Not applicable. As the testing focused on software functionality and identical performance, adjudication methods typically used in clinical studies (e.g., 2+1, 3+1) were not employed. The performance was assessed by direct comparison of software behavior.

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. An MRMC comparative effectiveness study was not done. The device does not involve AI for interpretation or human-in-the-loop assistance. The device is for signal acquisition and display only, and "no claims are being made for analysis of the acquired signals with respect to the accuracy, precision and reliability."

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

Yes, in a sense, but not for diagnostic interpretation. A standalone software functionality verification and validation was done for the Device Manager module. This evaluated the module's ability to perform its specified tasks (formatting device, uploading study info, downloading data, uploading firmware) independently and identically across different interfaces (desktop vs. web portal). This is an algorithm/software-only evaluation for its operational functions, not for clinical diagnostic performance.

7. The type of ground truth used

The ground truth used was the functional specifications and expected behavior of the Device Manager software. The verification and validation activities confirmed that the software's performance matched these predefined specifications, particularly that its functions were identical between the desktop and web portal versions.

8. The sample size for the training set

Not applicable. As this submission focused on software functionality and its equivalence to a predicate, there was no machine learning component, and thus, no "training set."

9. How the ground truth for the training set was established

Not applicable, as there was no training set.

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