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The ViSi Mobile Monitoring System is intended for use by clinicians and medically qualified personnel for single or multi-parameter vital signs monitoring of adult patients (18 years or older). It is indicated for ECG (3 or 5 lead-wire), respiration rate (RESP), heart rate (HR), non-invasive blood pressure (NIBP), continuous non-invasive blood pressure (cNIBP), non-invasive monitoring of functional oxygen saturation of arterial hemoglobin (SpO2), pulse rate (PR), and skin temperature (TEMP) in hospital-based facilities; including, general medical-surgical floors, intermediate care floors, and emergency departments.
The ViSi Mobile Monitoring System may be used as standalone devices or networked to ViSi Mobile Remote Viewers through wireless 802.11 communication.

The ViSi Mobile Monitoring System is a lightweight, body-worn vital signs monitor featuring a high resolution, full color touch screen display, with visual and audible alarms and alerts. The ViSi Mobile Monitor is designed to continuously non-invasively measure ECG, heart rate, SpO2, blood pressure, pulse rate, respiration rate, and temperature. The ECG, SpO2, and Respiration waveforms are viewable on demand. The ViSi Mobile Monitoring System is capable of one-time and continuous NIBP measurements.

This document is a 510(k) summary for the ViSi Mobile Monitoring System. It describes the device, its intended use, and provides a summary of non-clinical performance testing conducted to demonstrate substantial equivalence to previously cleared predicate devices.

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state formal acceptance criteria in a quantitative manner (e.g., "sensitivity must be > X%"). Instead, it describes performance in terms of improvements or conformance to standards.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: The document mentions the use of the "MIT and AHA databases." These are well-known, publicly available benchmark databases for ECG analysis. The specific number of records or patients from these databases used for testing is not explicitly stated in this summary.
• Data Provenance: The MIT and AHA databases are standard, established datasets. The country of origin and whether the data is retrospective or prospective is not specified in this document but is inherent to the nature of these established benchmark databases (typically retrospective and collected over time from various sources).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

The document does not provide information on the number or qualifications of experts used to establish the ground truth for the MIT and AHA databases. For these widely recognized benchmark databases, the ground truth is typically meticulously annotated by multiple qualified experts over many years.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

The document does not describe the adjudication method used for establishing the ground truth of the MIT and AHA databases. This information is typically detailed in the documentation accompanying those specific databases.

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

There is no mention of a multi-reader multi-case (MRMC) comparative effectiveness study, nor any evaluation of human reader improvement with or without AI assistance. The study described focuses on the standalone performance of the QRS detection algorithm against benchmark databases.

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

Yes, a standalone study was done. The performance testing described, which involved the "new ECG beat-picker" against the MIT and AHA databases, is a standalone evaluation of the algorithm's performance without human-in-the-loop involvement.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

The ground truth for the MIT and AHA databases, which were used to evaluate QRS detection, is based on expert consensus annotations of the ECG waveforms.

8. The sample size for the training set

The document does not provide information regarding the sample size of the training set used for the development or training of the new ECG beat-picker algorithm.

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

The document does not provide information on how the ground truth for the training set was established.

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