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The physIQ Heart Rhythm and Respiration Module (Version 2.0) is intended for use by a physician or other qualified medical professionals for the calculation of heart rate variability, the detection of atrial fibrillation and determination of respiration rate using ambulatory ECG and triaxial accelerometer data. The physIQ Heart Rhythm and Respiration Module supports receiving and analyzing single-lead ECG signals recorded in a compatible format from FDA-cleared ECG biosensor devices using "wet" electrode technology and triaxial accelerometers when assessment of rhythm and respiration rate is desired. The physIQ Heart Rhythm and Respiration Module is for use in adult patients in subacute clinical and nonclinical settings for remote patient monitoring. The physIQ Heart Rhythm and Respiration Module is not for use in patients requiring life-supporting or life-sustaining systems or as ECG or respiration alarm devices.

The physIQ Heart Rhythm and Respiration Module (Version 2.0) 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 and determining respiration rate determined by automated analysis of any single electrocardiogram (ECG) channel collected by commercially-available ECG biosensor devices with triaxial accelerometers. The physIQ Heart Rhythm and Respiration 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 and triaxial accelerometer data from a customer's data collection system, most likely via its own application programming interface (API), and makes calls to the phys/Q Heart Rhythm and Respiration Module to input ECG and triaxial accelerometer data 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.

The physIQ Heart Rhythm and Respiration Module (Version 2.0) was assessed for its performance in calculating heart rate variability, detecting atrial fibrillation, and determining respiration rate. The study followed guidelines of ANSI/AAMI EC57-2012 for heart rate variability and atrial fibrillation, while respiration rate was evaluated using internal acceptance criteria and comparison to a predicate device due to a lack of FDA-recognized consensus standards.

Here's a breakdown of the acceptance criteria and study details:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample Size for Test Set and Data Provenance:

The document does not explicitly state the sample size for the test set used for the respiration rate algorithm or the data provenance (e.g., country of origin, retrospective/prospective). It only mentions that "performance validation was performed using clinical and bench testing."

For heart rate variability and atrial fibrillation, performance testing followed ANSI/AAMI EC57-2012 guidelines in a previous submission (K180234), but the specific sample size and provenance for that previous test are not detailed in this document.

3. Number of Experts and Qualifications for Ground Truth:

The document does not specify the number of experts or their qualifications used to establish ground truth for the test set.

4. Adjudication Method:

The document does not specify the adjudication method (e.g., 2+1, 3+1, none) used for the test set.

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

The document does not mention if an MRMC comparative effectiveness study was done or the effect size of human readers' improvement with AI vs. without AI assistance. The device is a "computerized all-software callable function library" and its output is intended for use by medical professionals, suggesting it's an assistive tool, but a comparative effectiveness study with human readers is not detailed.

6. Standalone Performance:

Yes, a standalone (algorithm only without human-in-the-loop performance) study was conducted. The "Performance Testing" section states that the device contains "a collection of algorithms intended to be applied to ECG data." The validation was performed on these algorithms, and the results were compared to acceptance criteria.

7. Type of Ground Truth Used:

The type of ground truth used is not explicitly stated. However, for heart rate variability and atrial fibrillation, the reference to ANSI/AAMI EC57-2012 suggests established standards and potentially expert consensus or validated physiological measurements for ground truth. For respiration rate, it was compared to "internal acceptance criteria" and a "predicate device," implying a form of reference standard or clinical gold standard might have been used for the internal criteria.

8. Sample Size for Training Set:

The document does not provide the sample size used for the training set.

9. How Ground Truth for 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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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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