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The physlQ Heart Rhythm Module is intended for use by a physician or other qualified medical professionals for the calculation of heart rate and heart rate variability and the detection of atrial fibrillation using ambulatory ECG data. The physlQ Heart Rhythm Module supports receiving and analyzing single-lead ECG signals recorded in a compatible format from FDA-cleared ECG biosensor devices using "wet" electrode technology when assessment of rhythm is desired. The phys\Q Heart Rhythm Module is for use in subacute clinical settings for remote patient monitoring. The physlQ Heart Rhythm Module is not for use in patients requiring or life-sustaining systems or ECG Alam devices.

The physIQ Heart Rhythm Module (Version 1.0) is a computerized all-software callable function library in the Python programming language that is designed for calculating heart rate and heart rate variability and for detecting atrial fibrillation determined by automated analysis of any single electrocardiogram (ECG) channel collected by commercially-available ECG biosensor devices. This Heart Rhythm Module will be integrated by the customer organization into an end-to-end system (biosensor data collection to clinician display) that makes calls into the product, most typically via a Python middleware script. The "middleware" accesses the source ECG data from a customer's data collection system, most likely via its own application programming interface (API), and makes calls to the physIQ Heart Rhythm Module to input ECG for processing into the vital sign outputs of the product. These outputs are returned to the middleware, which may insert these results into a downstream monitoring system for clinical use.

Here's an analysis of the acceptance criteria and supporting studies for the physIQ Heart Rhythm Module (Version 1.0), based on the provided FDA 510(k) document:

phyIQ Heart Rhythm Module (Version 1.0) Acceptance Criteria and Performance

1. Table of Acceptance Criteria and Reported Device Performance

The provided document states that "Performance testing following guidelines of ANS/AAMI EC572012: Testing and Reporting Performance Results of Cardiac Rhythm and ST segment Measurement Algorithms has been applied to each of the algorithms. The performance testing results for all algorithms were compared to physIQ's defined acceptance criteria for performance testing. All algorithms met their corresponding acceptance criteria."

However, the specific quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) for each algorithm (Heartbeat Detector, Heart Rate, Heart Rate Variability, and Atrial Fibrillation Detector) are not explicitly detailed in the provided text. Similarly, the exact reported performance metrics (e.g., the achieved sensitivity/specificity values) are also not provided in a summarized table within this document. The document only confirms that "All algorithms met acceptance criteria."

Therefore, an exact table with numerical acceptance criteria and reported performance cannot be generated from the given text.

2. Sample Size Used for the Test Set and Data Provenance

The document states:
"further supportive clinical validation testing of the physIQ Heart Rhythm Module was performed using electrocardiography (ECG) signals captured from ambulatory patients using a wearable single-lead biosensor device which were annotated by medical experts in cardiology."

• Sample Size for Test Set: Not explicitly stated. The document only refers to "ambulatory patients" without specifying the number of patients or the duration/amount of ECG data.
• Data Provenance: The ECG signals were "captured from ambulatory patients" using two commercially available FDA-cleared patches: HealthPatch (K152139) manufactured by VitalConnect Inc. and BodyGuardian (K121197; K151188) manufactured by Preventice Inc. The country of origin of the data is not specified, but the use of FDA-cleared devices typically implies data collected in regions where such devices are marketed, often the US. The data appears to be retrospective, as it was "captured from ambulatory patients" and then annotated.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: Not explicitly stated. The document mentions "medical experts in cardiology."
• Qualifications of Experts: They were described as "medical experts in cardiology." Specific experience level (e.g., "10 years of experience") is not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The document only says the data was "annotated by medical experts in cardiology." It does not specify if multiple experts independently annotated and then reached consensus, or if a single expert provided the ground truth, or if a specific adjudication process (like 2+1 or 3+1) was used.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not mentioned or described in the provided text. The testing focused on the standalone performance of the algorithm against expert annotations.
• Effect Size of Human Improvement with AI Assistance: Not applicable, as no MRMC study was described.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

• Standalone Performance: Yes, a standalone performance evaluation was conducted. The document states: "this testing did not use any patch-generated vitals, but instead compared physIQ Heart Rhythm Module outputs to annotations by cardiology experts using ECG captured from two commercially-available patches..." This indicates the algorithm's output was directly compared to the expert-derived ground truth without human intervention in the device's output.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert Consensus (or Expert Annotation). The document explicitly states the ECG signals were "annotated by medical experts in cardiology."

8. Sample Size for the Training Set

• Sample Size for Training Set: Not mentioned in the provided text. The document focuses on the performance testing and clinical validation rather than the development and training details of the algorithms.

9. How the Ground Truth for the Training Set Was Established

• Ground Truth for Training Set: Not mentioned in the provided text. As with the training set size, the document does not delve into the methodology for establishing ground truth for any training data used for the algorithms.

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