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The Withings ECG App is a software-only device intended for use with the ScanWatch to create, record, store, transfer and display a single channel electrocardiogram (ECG) similar to a Lead I ECG. The Withings ECG App determines the presence of atrial fibrillation (AFib), sinus rhythm and high heart rate (no signs of AFib with heart rate 100-150 bpm) on a classifiable waveform. The Withings ECG App is not recommended for users with other known arrhythmias.

The Withings ECG App is intended for over-the-counter (OTC) use. The ECG data displayed by the Withings ECG App is intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from normal sinus rhythm and not intended to replace traditional methods of diagnosis or treatment.

The Withings ECG App is intended to supplement rhythm classification for the purposes of discriminating AFib from normal rhythms. The device is not intended to replace traditional methods or diagnosis.

The ECG acquired by ScanWatch is not intended for manual and/or automated measurement of QT-interval.

The Withings ECG app is not intended for use by people under 22 years old.

The Withings ECG App is a software only mobile medical application that has two components:

• Withings ECG Watch App
• Withings ECG Phone App

The Withings ECG Watch App is integrated on the Withings ScanWatch, model number hwa10. The Withings ECG Watch App analyzes the data collected by electrodes on the Withings ScanWatch to generate an ECG waveform similar to a Lead I, calculate the average heart rate and provide rhythm classification to the user for a given 30 second session.

Withings ECG Watch App consists of a software library called ECG-SW2 library. The ECG-SW2, is a software library that includes an algorithm that processes the raw ECG signals and a tracing filter that filters the ECG signal to provide the user an output on the user interface (watch and smartphone).

The Withings ECG Phone App contains the installation steps, tutorial and the instructions for use that the user must review prior to taking an ECG reading. The Withings ECG Phone App is included in the Withings App, which displays the ECG results and also allows the user to store, manage and share health data.

Here's a breakdown of the acceptance criteria and the study proving the device meets those criteria, based on the provided FDA 510(k) clearance letter for the Withings ECG App:

Overall Summary of the Study:

The Withings ECG App's performance was evaluated through a pivotal, prospective, multi-center clinical trial. This trial assessed the device's ability to accurately classify ECG recordings into Atrial Fibrillation (AFib), sinus rhythm, and high heart rate (no AFib) compared to a cardiologist's rhythm classification from a simultaneously collected 12-lead ECG (the ground truth). Non-clinical testing, including database testing of the algorithm and human factors studies, also supported the device's safety and effectiveness.

1. Table of Acceptance Criteria and Reported Device Performance

The clearance letter does not explicitly state pre-defined "acceptance criteria" as distinct numerical thresholds to be met. Instead, it describes the results achieved by the device, which implicitly serve as the demonstration of acceptable performance to the FDA. The performance metrics presented are sensitivity and specificity for AFib and sinus rhythm classification.

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

• Sample Size for Clinical Test Set: Approximately 626 subjects.
  • 219 in the Atrial Fibrillation cohort
  • 369 in the normal sinus rhythm cohort
  • 33 had other arrhythmias
  • 5 were uninterpretable
• Data Provenance:
  • Clinical Trial: Pivotal, prospective, multi-center clinical trial. The specific countries are not mentioned for the clinical trial itself, but the training data (Deep Train) is described as being from the European Union, and Heartbeats from the United States. Given the FDA clearance, it's highly probable the pivotal clinical trial included data from the U.S. or other regions acceptable to the FDA.

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

• Number of Experts: At least one cardiologist was used for ground truth establishment. The use of "a cardiologist" in the singular suggests a primary determination, though it's common practice for such a determination to be peer-reviewed or confirmed by a panel in high-stakes clinical trials. The document explicitly states: "Rhythm classification of a 12-lead ECG by a cardiologist was compared to the rhythm classification of a simultaneously collected ECG from the Withings ECG App."
• Qualifications of Experts: Cardiologist. Further details on experience (e.g., years of experience, board certification) are not specified in the provided text.

4. Adjudication Method for the Test Set

The adjudication method is implied as comparison to a cardiologist's 12-lead ECG interpretation. While it states "Rhythm classification of a 12-lead ECG by a cardiologist was compared...", it doesn't describe a multi-reader adjudication process (e.g., 2+1 or 3+1). It points to a direct comparison with a single cardiologist's interpretation of the 12-lead ECG as the reference standard.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study involving human readers improving with AI vs. without AI assistance was not described in this document. The study focused on the device's standalone performance in classifying ECG rhythms against a clinical gold standard (cardiologist's 12-lead interpretation).

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone performance study was done. The core of the clinical trial directly assessed the "Withings ECG App's ability to accurately classify an ECG recording" which is its algorithmic performance in classifying AFib, sinus rhythm, and high heart rate. The sensitivity and specificity numbers (99.7% and 99.8% respectively) are metrics of the algorithm's performance in this standalone capacity relative to the ground truth.

7. The Type of Ground Truth Used

The primary ground truth used was expert consensus / clinical diagnosis from a 12-lead ECG. Specifically, "Rhythm classification of a 12-lead ECG by a cardiologist" served as the reference standard.

8. The Sample Size for the Training Set

The document mentions two "user datasets" used for training and testing (via 4-Fold Cross Validation for hyperparameter tuning):

• "Deep train": n = 11,701
• "Heartbeats": n = 5,089

It's important to note that these datasets were used for initial training and hyperparameter tuning (4-Fold cross validations and act as train and test sets). The "clinical study datasets" (HWA08 test, HWA08 CE, WEFA HWA09 part 1) were used as a "first layer of validation sets" to check generalization, and "WEFA HWA09 part 2" was a "second layer of validation after the software freeze". The pivotal clinical trial data (626 subjects) described under "Clinical Testing" was the independent, locked algorithm validation set for regulatory submission.

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

The document provides the following for the "ML algorithm training and testing" datasets ("Deep train" and "Heartbeats"):

• It states these datasets were "user datasets".
• It does not explicitly detail how the ground truth for these training datasets was established. While the clinical trial's ground truth was a cardiologist's 12-lead ECG interpretation, the method for labeling the much larger training datasets is not provided in this excerpt. This is a common omission in 510(k) summaries which focus on the final validation. It's plausible they were labeled by a similar expert review process, potentially leveraging a larger pool of retrospectively acquired data.

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The WHOOP ECG Feature is a software-only mobile medical application intended for use with the WHOOP Strap to create, record, store, transfer, and display a single-channel electrocardiogram (ECG) qualitatively similar to a Lead I ECG. The WHOOP ECG Feature determines the presence of atrial fibrillation (AFib), normal sinus rhythm, low heart rate (≤ 50 beats per minute [bpm]), and high heart rate (≥ 100 bpm) on a classifiable waveform. The WHOOP ECG Feature is not recommended for users with other known arrhythmias.

The WHOOP ECG Feature is intended for over-the-counter (OTC) use. The ECG data displayed by the ECG Feature are intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without the consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from normal sinus rhythm and is not intended to replace traditional methods of diagnosis or treatment.

The WHOOP ECG Feature is intended for use by adults 22 years of age and older.

The WHOOP ECG Feature is a software-only medical mobile application integrated into the consumer (non-device) WHOOP System. It consists of three medical device modules: ECG Strap Module, ECG Phone Module, and ECG Cloud Module.

The feature is designed to create, record, store, transfer, and display a single-channel electrocardiogram (ECG), qualitatively similar to a Lead I ECG. It analyzes ECG recordings collected via the ECG electrodes on the WHOOP Strap.

The ECG Strap Module firmware is integrated within the WHOOP Strap's firmware. The ECG Strap Module firmware contains the FDA-cleared B-Secur HeartKey Software Library (K200884) which is used to provide classification for a 30-second ECG spot check recording into corresponding WHOOP ECG Feature outputs: Normal Sinus Rhythm; AFib; Low Heart Rate; High Heart Rate; Inconclusive; and Unsuccessful Reading. Users must opt in and complete onboarding through the ECG Phone Module within the WHOOP Mobile Application before accessing the ECG Feature.

The ECG Cloud Module processes requests for ECG report generation. Users can download ECG reports in PDF format to their mobile device or share them via applications such as email or messaging.

The WHOOP ECG Feature is not intended to replace traditional diagnostic or treatment methods.

The provided FDA 510(k) clearance letter for the WHOOP ECG Feature offers details regarding its acceptance criteria and the study conducted to prove it meets those criteria. Here's a structured breakdown of the information:

Acceptance Criteria and Reported Device Performance

1. Table of Acceptance Criteria and Reported Device Performance

• Probability of AFib result for true AFib: 87.47%
• Probability of SR result for true SR: 96.59% |
  | Morphology Assessment | Acceptable similarity of waveform morphology to reference ECG. | Comprehensive visual assessment demonstrated acceptable signal quality. Quantitative assessment compared key features (PR interval, QRS duration, R-wave amplitude, RR interval) with 12-lead reference. | Supported by clinical validation; differences do not raise new safety/effectiveness questions. |
  | User Comprehension/Human Factors | Users can correctly use the device, interpret output, and understand when to seek medical care. Users can adequately self-select if the device is intended for them. | Results were positive, demonstrating correct device use, output interpretation, and understanding of when to seek medical care. Testing with 51 participants confirmed adequate user self-selection. | Special Control (4) under 21 CFR 870.2345 addressed. Supports OTC use. |
  | Signal Acquisition Reliability | Reliable acquisition of ECG signals suitable for analysis and display. | Testing on commercial WHOOP system confirmed ability to reliably acquire ECG signals. | Supported by IEC 60601-2-47:2012 compliance. |
  | Database Testing | Compliance with special controls under 21 CFR 870.2345(3) using adjudicated dataset. | Conducted as per ANSI/AAMI EC57:2012(R2020). (Specific performance metrics not detailed, but compliance stated). | |
  | Software Verification | Compliance with FDA guidance for software functions. | Completed as recommended by "Guidance for Industry and FDA Staff Content of Premarket Submissions for Device Software Functions (issued June 14, 2023)." Includes cybersecurity, labeling, and management plan. | Device is a "basic level device" in terms of software. |
  | Electromagnetic Compatibility (EMC) & Electrical Safety | Compliance with relevant standards. | Meets specifications based on IEC 62368-1: 2018, EN 301-489 series, FCC 47 CFR Part 15 Subpart B:2024. | Confirms electrical safety and absence of electromagnetic interference issues. |

Study Information

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

• Test Set Sample Size: Approximately 540 subjects.
  • 255 enrolled in the AFib cohort.
  • 285 enrolled in the normal sinus rhythm cohort.
• Data Provenance: The document does not explicitly state the country of origin. It describes the study as a "clinical trial," which typically implies prospective data collection. There is no mention of it being retrospective.

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

• Number of Experts: One "cardiologist" was used for rhythm classification.
• Qualifications of Experts: The ground truth was established by "a cardiologist." Specific experience (e.g., 10 years of experience) is not detailed in the provided text.

4. Adjudication Method for the Test Set

• The document states that rhythm classification of a 12-lead ECG by "a cardiologist" was compared to the device's classification. This implies a single expert established the ground truth. There is no mention of a multi-reader adjudication method (e.g., 2+1 or 3+1).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly stated as being done within the context of human readers improving with AI vs. without AI assistance. The study described focuses on the device's standalone performance compared to a cardiologist's interpretation of a 12-lead ECG.

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

• Yes, the primary clinical validation described was a standalone (algorithm only) performance assessment. The study "compared to the rhythm classification of a simultaneously collected ECG from the WHOOP feature" against the 12-lead ECG interpreted by a cardiologist. The reported sensitivity, specificity, and inconclusive rates are metrics of the algorithm's performance.

7. The Type of Ground Truth Used

• Expert Consensus (single expert): The primary ground truth for rhythm classification (AFib/Sinus Rhythm) was established by a "cardiologist" interpreting a 12-lead ECG. This is a form of expert interpretation as ground truth.
• Reference Device Comparison: For waveform morphology, a "12-lead reference system" and "12-lead reference ECG" were used for comparison, implying a gold standard for electrical activity measurement.

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 WHOOP ECG Feature 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. It only mentions that the WHOOP ECG Feature integrates the FDA-cleared B-Secur HeartKey Software Library (K200884), which would have had its own training and validation data. The specific methods for establishing ground truth for the WHOOP ECG Feature's training data are not detailed.

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The Samsung ECG app with IHRN is an over-the-counter (OTC) software-only, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone for informational use only in adults 22 years and older. The app analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification suggesting the user record an ECG to analyze the heart rhythm. The Irregular Heart Rhythm Notification Feature is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present; rather the feature is intended to opportunistically acquire pulse rate data when the user is still and analyze the data when determined sufficient toward surfacing a notification.

Following this prompt, or based on the user's own initiative, the app is intended to create, record, store, transfer, and display a single channel ECG, similar to a Lead I ECG. Classifiable traces are labeled by the app as sinus rhythm. AFib. high heart rate (non-AFib), or AFib with high heart rate with the intention of aiding heart rhythm identification.

The app is not intended for users with other known arrhythmias, and it is not intended to replace traditional methods of diagnosis or treatment. Users should not interpret or take clinical action based on the device output without consultation of a qualified healthcare professional.

The Samsung ECG App v1.3 is a software as a medical device (SaMD) that consists of a pair of mobile medical apps: one app on a compatible Samsung wearable and the other on a compatible Samsung phone, both general-purpose computing platforms.

When enabled, the wearable application of the SaMD uses a wearable photoplethysmography (PPG) sensor to background monitor cardiac signals from the user. The application examines beat-to-beat intervals and generates an irregular rhythm notification indicative of atrial fibrillation (AFib). Upon receiving an irregular rhythm notification or at their discretion, the user can record a single-lead ECG using the same wearable. The wearable application then calculates the average heart rate from the ECG recording and produces a rhythm classification. The wearable application also securely transmits the data to the ECG phone application on the paired phone. The phone application shows a time-stamped irregular rhythm notification history with heart rate information; ECG measurement history; and generates a PDF file of the ECG signal, which the user can share with their healthcare provider.

Acceptance Criteria and Device Performance for Samsung ECG App v1.3

1. Acceptance Criteria and Reported Device Performance

Note: The reported performance for Samsung ECG App v1.3's "Sinus rhythm (HR 50-150 BPM)" and "AFib (HR 50-150 BPM)" is presented with the same 95% CI: (94.0%, 97.8%). This might be a transcription error in the document, as specificity and sensitivity for different conditions would typically have distinct confidence intervals. Assuming independent calculations, these values are presented as they appear in the source.

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

• Sample Size: 1,013 subjects. These subjects contributed to 453 AFib recordings (heart rate 50 to 150 BPM) and 691 Sinus rhythm recordings (heart rate 50 to 150 BPM) for the primary endpoint analysis.
• Data Provenance: The study was a multi-center study, implying data from multiple locations, likely within the US given the FDA submission context and the racial demographics provided (predominantly Caucasian). The study was likely prospective as it involved recruiting subjects and collecting data for validation.

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

The document does not explicitly state the "number of experts" or their specific "qualifications" used to establish the ground truth for the test set. It mentions "Clinical Validation showing comparable clinical performance...compared to the reference device" and that the "ECG function accurately classified...compared against the standard Lead I ECG," implying that comparison was made to physician-adjudicated or expertly interpreted ECGs, but the details of the ground truth establishment are not provided.

4. Adjudication Method for the Test Set

The document does not explicitly state the adjudication method used for establishing the ground truth for the test set.

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

There is no mention of a Multi Reader Multi Case (MRMC) comparative effectiveness study being done, or any effect size of how much human readers improve with AI vs without AI assistance. The study focuses on the standalone performance of the device's ECG rhythm classification compared to a reference device.

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

Yes, a standalone study was conducted. The "Clinical Validation" section details the performance of the "ECG rhythm classification of the Samsung ECG App v1.3" in terms of sensitivity and specificity against a clinical ground truth, without explicit human-in-the-loop interaction for the classification task itself. The device "accurately classified" recordings.

7. Type of Ground Truth Used

The ground truth used was clinical diagnosis based on "446 subjects diagnosed with AFib, 536 subjects without AFib, and 31 subjects diagnosed with another type of irregular rhythm." The performance was evaluated by comparing the device's classifications against "standard Lead I ECG" interpretation, implying expert consensus (from qualified healthcare professionals interpreting the standard ECGs) or clinical diagnosis as the ground truth.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It focuses on the validation study.

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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The Samsung ECG Monitor Application with Irregular Heart Rhythm Notification is an over-the-counter (OTC) softwareonly, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone for informational use only in adults 22 years and older. The app analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of atrial fibrillation (AFib) and provides a notification suggesting the user record an ECG to analyze the heart rhythm. The Irregular Heart Rhythm Notification Feature is not intended to provide a notification on every episode of irregular rhythm suggestive of AFib and the absence of a notification is not intended to indicate no disease process is present, rather the feature is intended to opportunistically acquire pulse rate data when the data when determined sufficient toward surfacing a notification.

Following this prompt, or based on the user's own initiative, the app is intended to create, record, store, transfer, and display a single-channel ECG. similar to a Lead I ECG. Classifiable traces are labeled by the app as either AFib or sinus rhythm with the intention of aiding heart rhythm identification.

The app is not intended for users with other known arrhythmias, and it is not intended to replace traditional methods of diagnosis or treatment. Users should not interpret or take clinical action based on the device of the of a qualified healthcare professional.

The Samsung ECG Monitor App with Irregular Heart Rhythm Notification (IHRN) Feature is a software as a medical device (SaMD) that consists of a pair of mobile medical apps: one app on a compatible Samsung wearable and the other on a compatible Samsung phone, both general-purpose computing platforms.

When enabled, the wearable application of the SaMD uses a wearable photoplethysmography (PPG) sensor to background monitor bio-photonic signals from the user. The application examines beat-to-beat intervals and generates an irregular rhythm notification indicative of atrial fibrillation (AFib). Upon receiving an irregular rhythm notification or at their discretion, the user can record a single-lead ECG using the same wearable. The wearable application then calculates the average heart rate from the ECG recording and produces a rhythm classification. The wearable application also securely transmits the data to the ECG phone application on the paired phone device. The phone application shows a time-stamped irregular rhythm notification history with heart rate information; ECG measurement history; and generates a PDF file of the ECG signal, which the user can share with their healthcare provider.

Here's a breakdown of the acceptance criteria and the study details for the Samsung ECG Monitor Application with Irregular Heart Rhythm Notification Feature, based on the provided text:

Acceptance Criteria and Device Performance

The document states that Samsung's algorithm performance for the IHRN function is substantially equivalent to the predicate device (Apple IRN Feature DEN180042) at both subject and tachogram levels, indicating these reported values met the acceptance criteria. For the ECG function, the device inherited the performance from the previously cleared Samsung ECG Monitor App (K201168) and thus the reported values were assumed to meet their prior acceptance criteria.

Study Details

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

• IHRN Clinical Validation (PPG-based notification):

  • Analyzable Dataset for primary and secondary endpoints: 810 subjects (from 888 enrolled).
  • Tachogram-level assessment: 98 subjects with AFib episodes (over an hour) and 101 subjects with less than an hour of AFib or no AFib were randomly selected from the cardiologist-reviewed subjects. Up to 25 positive tachograms with reference ECG data were randomly selected from these subjects.
  • Data Provenance: The document does not explicitly state the country of origin, but it is a clinical study. The phrasing "All recruited subjects were at risk for AFib and had experienced symptoms..." suggests prospective data collection.

• ECG Function (on-demand):

  • No new clinical, human factors, or ECG database tests were conducted as the function was unchanged from the K201168 clearance. Therefore, a new test set was not used for this specific clearance.

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

• IHRN Clinical Validation:
  • Subject-level ground truth: "clinician-adjudicated and cardiologist-reviewed patch ECG data." The exact number of clinicians/cardiologists for this overarching adjudication is not specified, but it implies multiple experts.
  • Tachogram-level ground truth: "Two board-certified cardiologists reviewed each reference ECG for annotation with a third cardiologist serving as tie-breaker."
  • Qualifications: "Board-certified cardiologists."

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

• Tachogram-level ground truth: 2+1 (Two board-certified cardiologists reviewed, with a third serving as a tie-breaker).
• Subject-level ground truth: Not explicitly stated as a specific numerical method (e.g., 2+1), but referred to as "clinician-adjudicated and cardiologist-reviewed," implying a consensus or expert-driven process.

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 MRMC comparative effectiveness study involving human readers with and without AI assistance was mentioned or conducted. The study evaluated the device's performance (IHRN feature) against a clinical ground truth, not the improvement of human readers using the device.

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

• Yes, the clinical validation study for the Irregular Heart Rhythm Notification (IHRN) feature primarily assesses the standalone performance of the PPG-based algorithm in identifying irregular rhythms and generating notifications. The "subject-level irregular rhythm notification accuracy" and "tachogram-level positive predictive value" are metrics of the algorithm's performance without direct human interpretation being part of the primary output.

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

• IHRN Clinical Validation: Expert consensus using reference ECG patch data reviewed and adjudicated by clinicians and board-certified cardiologists.

8. The sample size for the training set:

• The document does not specify the sample size for the training set. It focuses on the validation study.

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

• The document does not specify how the ground truth for the training set (if any) was established. It only details the ground truth establishment for the test/validation set.

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The Garmin ECG app is a software-only, mobile medical application intended for use with compatible Garmin smartwatches to create, record, store, transfer, and display a single-channel electrocardiograph similar to a Lead I ECG. The ECG app determines the presence of atrial fibrillation (AFib) or sinus rhythm (SR) on a classifiable waveform. The ECG app is not recommended for users with other known arrhythmias.

The ECG app is intended for over-the-counter (OTC) use. The ECG data displayed by the ECG app is intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from normal SR, and it is not intended to replace traditional methods of diagnosis or treatment.

The ECG app is not intended for use by people under 22 years old.

The Garmin ECG App (ECG App) is a software-only, mobile medical application that has two components: (1) the Watch ECG App for compatible Garmin smartwatches ("Watch" or "Watches"), and (2) the Smartphone ECG App included within Garmin's consumer health and fitness application ecosystem, Garmin Connect allows users to store, manage, and share their respective health and fitness data.

The ECG App is intended to create, record, store, transfer, and display a single lead ECG signal similar to a Lead | ECG. The Watch ECG App acquires and analyzes the single lead ECG signal from electrodes built into each Watch and detects the presence of atrial fibrillation (AFib) or normal sinus rhythm (SR) in the adult wearer of the Watch ECG App then calculates the average heart rate and displays that value, along with the rhythm classification result, to the user on the Watch screen. The user may annotate the result by choosing from a provided list of symptoms.

Optionally, and only when directed by the user, the Watch ECG App can securely transmit the result to the Smartphone ECG App for the purposes of storing and viewing a history of ECG App results. The user may also export ECG App results as a PDF for easy sharing.

The Garmin ECG App is a software-only, mobile medical application intended for use with compatible Garmin smartwatches to detect the presence of atrial fibrillation (AFib) or sinus rhythm (SR) from a single-channel electrocardiograph (similar to Lead I ECG).

Here's a breakdown of the acceptance criteria and the study that proves the device meets them:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Garmin ECG App's clinical performance were based on its ability to accurately detect AFib and sinus rhythms. The reported device performance is presented in the table below:

2. Sample Size and Data Provenance

• Test Set Sample Size: Approximately 590 subjects.
• Data Provenance: The document does not explicitly state the country of origin or whether the study was retrospective or prospective. However, it describes a "clinical study," which typically implies a prospective design.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Not explicitly stated, but the ground truth for the test set was established by "board-certified cardiologists."
• Qualifications of Experts: Board-certified cardiologists.

4. Adjudication Method for the Test Set

The adjudication method is not explicitly detailed beyond stating that "12-Lead ECG rhythm classifications performed by board-certified cardiologists" were used as the comparison for the ECG app's classifications. It implies that these expert classifications served as the gold standard for comparison.

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

There is no mention of a Multi-Reader Multi-Case (MRMC) comparative effectiveness study being conducted to assess how much human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the Garmin ECG App compared to expert-classified 12-Lead ECGs.

6. Standalone Performance (Algorithm Only)

Yes, a standalone performance study (algorithm only without human-in-the-loop performance) was done. The reported sensitivities and specificities (99.5% AFib sensitivity, 100% SR specificity for classifiable waveforms, and 86.5% AFib detection, 91.1% SR detection overall) are for the device's algorithmic classification.

7. Type of Ground Truth Used

The ground truth used was expert consensus based on 12-Lead ECG rhythm classifications performed by board-certified cardiologists.

8. Sample Size for the Training Set

The document does not explicitly state the sample size used for the training set for the ECG app's algorithm. The provided information specifically refers to the clinical validation study (test set).

9. How Ground Truth for the Training Set Was Established

The document does not explicitly describe how the ground truth for the training set was established. It mentions that "Garmin conducted database testing using a previously adjudicated dataset as per ANSI/AAMI EC57:2012" under "Non-Clinical Testing." While this indicates the use of adjudicated data for some form of testing, it doesn't specify if this was the training dataset or how that adjudication was performed (e.g., expert review, specific criteria).

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The ECG app is a software-only mobile medical application intended for use with the Apple Watch to create, record, store, transfer, and display a single channel electrocardiogram (ECG) similar to a Lead I ECG. The ECG app determines the presence of atrial fibrillation (AFib), sinus rhythm, and high heart rate (no detected AF with heart rate 100-150 bpm) on a classifiable waveform. The ECG app is not recommended for users with other known arrhythmias.

The ECG app is intended for over-the-counter (OTC) use. The ECG data displayed by the ECG app is intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from sinus rhythm and is not intended to replace traditional methods of diagnosis or treatment.

The ECG app is not intended for use by people under 22 years old.

The ECG 2.0 app comprises a pair of mobile medical apps - one on Apple Watch and the other on the iPhone.

The ECG Watch app analyzes data collected by the integrated electrical sensors on a compatible Apple Watch to generate an ECG waveform similar to a Lead I. calculate average heart rate, and provide a rhythm classification to the user for a given 30 second session. When a user opens the ECG Watch app while wearing the Watch on one wrist, and places the finger of the opposite hand on the digital crown, they are completing the circuit across the heart which begins a recording session.

Once the recording session is complete, the ECG Watch app performs signal processing, feature extraction and rhythm classification to generate a session result.

The resulting classification and average heart rate for the session, along with educational information, will be displayed to the user within the ECG Watch app.

The ECG iPhone app contains the on-boarding and educational materials that a user must review prior to taking an ECG reading. The ECG iPhone app is included in the Health App, which allows users to store, manage, and share health and fitness data, and comes pre-installed on every iPhone. The ECG 2.0 app expands the classifiable heart range, introduces new classification results, and introduces minor, non-userfacing algorithm updates. These changes will be reflected in both the Apple Watch app, and also on the corresponding iPhone app within the Health App.

Here's a summary of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of acceptance criteria and the reported device performance

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

• Sample size: Approximately 546 subjects.
  • 305 subjects were in the Atrial Fibrillation cohort.
  • 241 subjects were in the normal sinus rhythm cohort.
• Data provenance: Prospective, multi-center clinical trial. The country of origin is not explicitly stated, but it is a "multi-center" trial, implying diverse participant recruitment.

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

• Number of experts: Not explicitly stated, but the ground truth was established by "a cardiologist." This implies at least one, likely a panel or multiple, to ensure robustness, though the exact number isn't quantified.
• Qualifications of experts: "Cardiologist." Years of experience are not specified.

4. Adjudication method for the test set

• The text states: "Rhythm classification of a 12-lead ECG by a cardiologist was compared to the rhythm classification of a simultaneously collected ECG from the ECG 2.0 app." This indicates that the cardiologist's interpretation of a 12-lead ECG served as the ground truth. It does not explicitly describe an adjudication method like 2+1 or 3+1 if multiple cardiologists were involved. It implies a single definitive classification by the cardiologist.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance

• No, a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with AI assistance versus without AI assistance was not conducted or reported in the provided text. The study focused on the standalone performance of the ECG 2.0 app against a cardiologist's interpretation.

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

• Yes, a standalone performance study was done. The reported performance metrics (sensitivity, specificity, waveform acceptability) reflect the algorithm's direct classification capabilities compared to the ground truth established by a cardiologist. The device is intended for over-the-counter use, and its performance in classifying AFib and sinus rhythm was assessed directly.

7. The type of ground truth used

• The ground truth used was expert consensus / diagnosis from a cardiologist's interpretation of a 12-lead ECG.

8. The sample size for the training set

• The document does not explicitly state the sample size for the training set. It only mentions the test set (clinical trial of 546 subjects).

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

• The document does not explicitly describe how the ground truth for the training set was established. It primarily focuses on the validation study. However, given that it states "Apple conducted database testing using a previously adjudicated dataset" for "ECG Database Testing per EC57," it is highly probable that the training data's ground truth was also established by expert cardiologists adjudicating ECGs in a similar manner to the test set, but this is not explicitly detailed for the training set.

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The Fitbit ECG App is a software-only mobile medical application intended for use with Fitbit wrist wearable devices to create, record, store, transfer, and display a single channel electrocardiogram (ECG) qualitatively similar to a Lead I ECG. The Fitbit ECG App determines the presence of atrial fibrillation (AFib) or sinus rhythm on a classifiable waveform. The AFib detection feature is not recommended for users with other known arrhythmias.

The Fitbit ECG App is intended for over-the-counter (OTC) use. The ECG data displayed by the Fitbit ECG App is intended for informational use only. The user is not interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from normal sinus rhythm and not intended to replace traditional methods of diagnosis or treatment. The Fitbit ECG App is not intended for use by people under 22 years old.

The Fitbit ECG App is a software-only medical device used to create, record, display, store and analyze a single channel ECG. The Fitbit ECG App consists of a Device application ("Device app") on a consumer Fitbit wrist-worn product and a mobile application tile ("mobile app") on Fitbit's consumer mobile application. The Device app uses data from electrical sensors on a consumer Fitbit wrist-worn product to create and record an ECG. The algorithm on the Device app analyzes a 30 second recording of the ECG and provides results to the user. Users are able to view their past results as well as a pdf report of the waveform similar to a Lead I ECG on the mobile app.

Below is the information regarding the Fitbit ECG App's acceptance criteria and the study that proves it, based on the provided document:

1. Table of acceptance criteria and the reported device performance

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

• Sample Size: 475 subjects.
• Data Provenance: Subjects were recruited across 9 US sites. This indicates prospective data collection from the United States.

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

• Number of Experts: For subjects with a known history of AFib, a "single qualified physician" performed the screening and assigned them to the AFib cohort. The document doesn't specify how many experts reviewed the 12-lead ECGs for the ground truth of AFib or Sinus Rhythm (NSR) for all 475 subjects, beyond the single physician for the AFib cohort screening. For the overall study, it implies a 12-lead ECG was the reference, which would typically be interpreted by qualified cardiologists or electrophysiologists.
• Qualifications of Experts: For AFib screening, the expert was referred to as a "single qualified physician." Specific qualifications like "radiologist with 10 years of experience" are not provided.

4. Adjudication method for the test set

The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). It mentions that subjects with a known history of AFib were screened by a "single qualified physician." For the simultaneous 12-lead ECG, it implies a clinical standard interpretation which often involves adjudicated reads, but this is not detailed in the provided text.

5. If a Multi-Reader, Multi-Case (MRMC) comparative effectiveness study was done

No, a Multi-Reader, Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not reported in this document. The study focuses on evaluating the standalone performance of the Fitbit ECG App against a clinical standard (12-lead ECG).

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

Yes, a standalone performance study was done. The document states: "The Fitbit ECG App software algorithm was able to detect AF with the sensitivity and specificity of 98.7% and 100%, respectively." This indicates a direct evaluation of the algorithm's performance.

7. The type of ground truth used

The ground truth was established using a simultaneous 30-second 12-lead ECG. This is a clinical gold standard for rhythm analysis.

8. The sample size for the training set

The document does not provide the sample size for the training set. It only details the clinical testing conducted for validation/evaluation of the device.

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, as it focuses on the validation study.

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The Samsung ECG Monitor Application is an over-the-counter (OTC) software-only, mobile medical application operating on a compatible Samsung Galaxy Watch and Phone. The app is intended to create, record, store, transfer, and display a single channel electrocardiogram (ECG), similar to a Lead I ECG, for informational use only in adults 22 years and older. Classifiable traces are labeled by the app as either atrial fibrillation (AFib) or sinus rhythm with the intention of aiding heart rhythm identification; it is not intended to replace traditional methods of diagnosis or treatment. The app is not intended for users with other known arrhythmias and users should not interpret or take clinical action based on the device output without consultation of a qualified healthcare professional.

The Samsung ECG Monitor Application consists of a pair of mobile medical apps: one on a compatible Samsung wearable and the other on a compatible Samsung phone. The compatible Samsung wearable application captures bioelectrical signals from the user and generates single lead ECG signals, calculates average heart rate and classifies the rhythm. The wearable application securely transmits the obtained data to the phone application on the paired phone device. The phone application shows the ECG measurement history and generates the PDF file for the received ECG signals which can be shared by the user.

The Samsung ECG Monitor Application was proven to be non-inferior to the predicate (Apple ECG App, DEN180044) in terms of rhythm classification accuracy and ECG signal quality sufficiency.

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

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

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

• Sample Size: 544 subjects.
  • 268 AFib patients
  • 261 Sinus Rhythm (SR) patients
  • 15 with other arrhythmias
• Data Provenance: The document does not explicitly state the country of origin. However, the manufacturer is Samsung Electronics Co., Ltd in Korea. The study structure implies prospectively collected data for this clinical validation.

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

• Number of Experts: Unspecified number of cardiologists and three blinded, independent ECG technicians.
• Qualifications of Experts:
  • Cardiologists: Used for comparing the ECG App algorithm detection of AFib and SR to 12-lead ECG reference strips, and for interpreting the ECG Monitor App strips. No specific experience level provided.
  • ECG Technicians: Three blinded, independent ECG technicians were used for fiducial point annotation. No specific experience level provided.

4. Adjudication method for the test set

• For rhythm classification, the ground truth was established by cardiologists' read of 12-lead ECG reference strips. This implies a consensus or authoritative read by these experts.
• For signal quality interpretability and concordance, cardiologists' interpretation served as the reference.
• For fiducial point annotation, three blinded, independent ECG technicians marked the points, implying their individual annotations were compared against the reference or potentially against each other for a form of consensus, though this is not explicitly detailed.

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

The document describes a clinical study where the algorithm's performance (Samsung ECG Monitor App) was compared to a reference standard (cardiologist-read 12-lead ECG), and also against a predicate device (Apple ECG App). It does not describe an MRMC comparative effectiveness study evaluating how human readers improve with AI vs without AI assistance. The focus was on the algorithm's standalone performance compared to expert ground truth and its non-inferiority to an existing cleared device.

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

Yes, a standalone study was done. The clinical study directly evaluated the Samsung ECG Monitor App algorithm's performance in detecting AFib and Sinus Rhythm against a cardiologist-read 12-lead ECG reference strip. The reported sensitivity, specificity, and inconclusive rates are for the algorithm's performance alone.

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

The primary ground truth used was expert consensus / expert interpretation from:

• Cardiologists (for rhythm classification based on 12-lead ECG reference strips and for interpretability and concordance studies).
• Blinded, independent ECG technicians (for fiducial point annotation).

8. The sample size for the training set

The document does not specify the sample size for the training set. It only details the clinical validation study (test set).

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, as it focuses solely on the clinical validation (test set) and device performance evaluation.

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The ECG app is a software-only mobile medical application intended for use with the Apple Watch to create, record, store, transfer, and display a single channel electrocardiogram (ECG) similar to a Lead I ECG. The ECG app determines the presence of atrial fibrillation (AFib) or sinus rhythm on a classifiable waveform. The ECG app is not recommended for users with other known arrhythmias.

The ECG app is intended for over-the-counter (OTC) use. The ECG data displayed by the ECG app is intended for informational use only. The user is not intended to interpret or take clinical action based on the device output without consultation of a qualified healthcare professional. The ECG waveform is meant to supplement rhythm classification for the purposes of discriminating AFib from normal sinus rhythm and not intended to replace traditional methods of diagnosis or treatment.

The ECG app is not intended for use by people under 22 years old.

The device (ECG App) comprises a pair of mobile medical apps — one on Apple Watch (the Watch App) and the other on the iPhone (iPhone App) - intended to record, store, transfer, and display a single lead ECG signal similar to a lead I. The ECG Watch App is intended to analyze this single lead data and detect the presence of atrial fibrillation (referred into this document as AFib or AF) and sinus rhythm in adults. It is also intended to acquire and analyze the single lead ECG recordings for display on the iPhone. The ECG iPhone App is included in the Health App, which is intended to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.

Here's a breakdown of the acceptance criteria and the study that proves the device (ECG App) meets these criteria, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

1. Sample Size and Data Provenance:

   • Clinical Study Test Set Sample Size: 602 total subjects enrolled. After exclusions, 588 eligible subjects were used for analysis (AF Cohort: 301, SR Cohort: 287). For the "Classifiable Analysis Set" (where the algorithm output a diagnosis), 556 subjects were included (488 had a diagnosis).
   • Data Provenance: Not explicitly stated regarding country of origin, but it was a "multi-center" study, implying multiple sites within a geographic region (likely the US, given FDA submission). The study was prospective.
   • Waveform Assessment Analysis Set Sample Size: 139 subjects initially selected, with 126 subjects remaining for analysis after exclusions (60 AF, 65 SR, 99.2%).
   • ECG Database Testing Sample Size: "(b) (4)" records from adjudicated AHA and MIT databases, split into "(b) (4)" 30-second segments. Specific numbers are redacted.

2. Number of Experts and Qualifications for Clinical Study Ground Truth:

   • Number of Experts: Three (3) blinded independent board-certified cardiologists.
   • Qualifications: "board-certified cardiologists." No specific years of experience are listed.

3. Adjudication Method for Clinical Study Test Set Ground Truth:

   • Adjudication Method: "If the readers disagreed on the diagnosis, the final interpretation was determined by the simple majority rule." This is a 3-reader majority rule method.

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

   • The provided text does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance is compared.
   • The study primarily focuses on the standalone performance of the AI algorithm (ECG App) against physician-adjudicated ground truth.
   • There was an "Additional Analysis" involving physician interpretation of ECG App strips compared to 12-lead ECGs, but this was to evaluate the quality of the ECG App recording and not a comparative study of human performance with/without AI assistance.

5. Standalone Performance:

   • Yes, a standalone (algorithm only without human-in-the loop performance) evaluation was done as the primary endpoint of the clinical study. The ECG App algorithm's classification was directly compared against the physician-adjudicated 12-lead ECG ground truth.

6. Type of Ground Truth Used:

   • Clinical Study: Expert Consensus (three blinded independent board-certified cardiologists reviewing 12-lead ECG recordings with majority rule).
   • ECG Database Testing: "The database annotations were used as ground truth." These are typically expert-adjudicated public datasets like AHA and MIT databases.

7. Sample Size for Training Set:

   • The provided text does not specify the sample size for the training set of the ECG App algorithm. It only details the test sets used for validation.

8. How the Ground Truth for the Training Set was Established:

   • The text does not provide information on how the ground truth for the training set was established. It only mentions the ground truth methodology for the test sets (clinical study and database testing).

This detailed analysis covers the requested information based on the provided FDA De Novo submission text.

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