The Atrial Fibrillation (AFib) History Feature is an over-the-counter ("OTC") software-only mobile medical application intended for users 22 years of age and over who have a diagnosis of atrial fibrillation (AFib). The feature opportunistically analyzes pulse rate data to identify episodes of irregular heart rhythms suggestive of AFib and provides the user with a retrospective estimate of AFib burden (a measure of the spent in AFib during past Apple Watch wear).

The feature also tracks and trends estimated AFib burden over time, and includes lifestyle data visualizations to enable users to understand the impact of certain aspects of their AFib. It is not intended to provide individual irregular rhythm notifications or to replace traditional methods of diagnosis, treatment, or MFib.

The feature is intended for use with the Apple Watch and the Health app on iPhone.

Device Description

The Atrial Fibrillation History Feature (AFib History Feature) is comprised of a pair of mobile medical apps - one on Apple Watch and the other on the iPhone.

The AFib History Feature is intended to analyze pulse rate data collected by the Apple Watch PPG sensor on Apple Watch Series 4. Series 5. and SE to identify episodes of irregular heart rhythms consistent with AFib and provides the user with a retrospective estimate of AFib burden (a measure of the amount of time spent in AFib during past Apple Watch wear).

The AFib History Feature uses PPG pulse rhythm data from compatible Apple Watches. Apple Watch uses green LED lights paired with light-sensitive photodiodes to detect relative changes in the amount of blood flowing through a user's wrist at any given moment. When the heart beats it sends a pressure wave down the vasculature, causing a momentary increase in blood volume when it passes by the sensor. By monitoring these changes in blood flow, the sensor detects individual pulses when they reach the peripherv and thereby measure beat-to-beat intervals.

The AFib History Feature iPhone App is part of the Health App. which allows users to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.

The AFib History Feature provides users visualizations of AFib burden estimate data alongside clinically relevant lifestyle data and presents estimates of AFib burden in three different ways. These visualizations empower users to observe and understand the impact of lifestyle on their AFib burden, and to better understand their condition generally.

· Weekly Estimate an estimate of the amount of time a user was in Atrial Fibrillation over the past calendar week during watch wear, presented to the user as a percentage.
Day of Week Estimate an estimate of the amount of time a user was in Atrial Fibrillation on each day of the week over the previous 42 days during watch wear, presented to the user as a percentage. That is, all Mondays over the past 42 days, all Tuesdays over the past 42 days.
Time of Day Estimate an estimate of the amount of time a user was in Atrial Fibrillation on 4-hour segments of the day over the previous 42 days during watch wear, presented to the user as a percentage. That is, all 12 am - 4 am segments over the past 42 days, all 4 am - 8 am segments over the past 42 days.

The AFib History Feature is intended to serve as an extension of the predicate Irregular Rhythm Notification feature, but has been optimized for users with a diagnosis of Afib.

AI/ML Overview

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided FDA document.

The document describes the Atrial Fibrillation History Feature, a software-only mobile medical application intended for users 22 years and older with a diagnosed Atrial Fibrillation (AFib). The feature analyzes pulse rate data from Apple Watch to provide a retrospective estimate of AFib burden.

1. Table of Acceptance Criteria and Reported Device Performance

The document doesn't explicitly list "acceptance criteria" in a definitive table format with pass/fail thresholds. Instead, it presents performance metrics from development and clinical studies for the classification algorithm and for the AFib burden estimation.

Implicit Acceptance Criteria (Derived from Performance Data and Context):

Rhythm Classification Algorithm Performance: High sensitivity and specificity in differentiating AFib from non-AFib rhythms.
AFib Burden Estimation Accuracy: Close agreement between the device's weekly AFib burden estimate and the reference method.

Reported Device Performance:

Metric	Reported Device Performance (AFib History Feature)	Comparator (IRNF 2.0 - Predicate Device)
Rhythm Classification Algorithm (Development Studies)
Sensitivity	97%	79.6%
Specificity	99.0%	99.9%
Rhythm Classification Algorithm (Clinical Validation Study)
Sensitivity	92.6%	85.5%
Specificity	98.8%	99.6%
Weekly AFib Burden Estimation (Clinical Validation Study)
Bland-Altman Limits of Agreement (Lower/Upper 2 SD)	-11.4% and 12.8%	N/A (The predicate device provides irregular rhythm notifications, not AFib burden estimates, so this metric is not directly comparable. The document states the AFib History Feature is an "extension of the predicate Irregular Rhythm Notification feature, but has been optimized for users with a diagnosis of Afib." The performance comparison for the classification algorithm shows how the underlying algorithm was adapted.)
Average difference (device vs. reference)	0.67%	N/A
% of subjects with weekly AFib burden differences within ±5%	92.9% (260/280)	N/A
% of subjects with weekly AFib burden estimates within ±10%	95.7% (268/280)	N/A

2. Sample Size for the Test Set and Data Provenance

The document refers to two main test sets:

Development Studies Test Set: Data used for evaluating the model during development and as a "last test" on a "Sequestration set" (which functions as a final test set after model locking).
- Sample Size: Part of "over 2500 subjects" and "over 3 million pulse rate recordings". The exact number of subjects or recordings specifically in the test/sequestration sets for the classification algorithm performance is not individually quantified beyond being a split of the total development data.
- Data Provenance: Not explicitly stated (e.g., country of origin, specific institutions). It mentions "demographically diverse populations" in recruitment. The studies are described as "development studies," implying they were specifically conducted for the purpose of algorithm development. The data appears to be prospective given it was collected from recruited subjects.
Clinical Validation Study Test Set:
- Sample Size: 413 enrolled subjects for the study. 280 subjects contributed data to the primary endpoint analysis for AFib burden estimation.
- Data Provenance: Not explicitly stated (e.g., country/region). It describes "enrolled subjects wore an Apple Watch and a reference electrocardiogram (ECG) patch concurrently for up to 13 days," indicating a prospective clinical study specifically for this validation.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document refers to "reference electrocardiogram (ECG) patch concurrently" and "reference weekly burden" for establishing ground truth, both in the development studies and the clinical validation study.

Number of Experts: Not explicitly stated.
Qualifications of Experts: Not explicitly stated. However, the use of "reference electrocardiogram (ECG)" strongly implies that medical professionals (e.g., cardiologists, electrophysiologists) would be involved in reading and interpreting these ECGs to establish the ground truth for AFib presence and burden.

4. Adjudication Method for the Test Set

Adjudication Method: Not explicitly stated. The document refers to "reference electrocardiogram (ECG)" as the ground truth. It's common practice for ECG interpretations, especially in clinical studies, to involve review by multiple experts or an adjudication committee, but the specific method (e.g., 2+1, 3+1) is not detailed.

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

Was an MRMC Comparative Effectiveness Study Done? No, an MRMC study comparing human readers with and without AI assistance was not described. The study focuses on the standalone performance of the device's algorithm against a reference standard (ECG).
Effect Size of Human Reader Improvement: Not applicable, as no MRMC study was performed.

6. Standalone Performance (Algorithm Only)

Was a Standalone Study Done? Yes. The performance metrics presented for both the rhythm classification algorithm and the AFib burden estimation in the development and clinical studies are indicative of the device's standalone (algorithm-only, without human-in-the-loop performance) performance. The device is a "software-only mobile medical application," and its output (AFib burden estimate) is directly compared to the reference standard.

7. Type of Ground Truth Used

Type of Ground Truth: The primary ground truth for both the classification algorithm development/testing and the clinical validation of AFib burden estimation was established using reference electrocardiogram (ECG) data. This is considered a high-fidelity diagnostic standard for cardiac rhythm.

8. Sample Size for the Training Set

Training Set Sample Size: The algorithm was "trained extensively using data collected in a number of development studies. In total, the studies included over 2500 subjects and collected over 3 million pulse rate recordings." The training set was a portion of this total dataset, with the data "split into four sets...: Training, Validation, Test, and Sequestration sets." The exact number of subjects or recordings specifically in the training set is not provided separately.

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

Ground Truth Establishment for Training Set: "The rhythm classification algorithm uses a convolutional neural network based architecture and was trained extensively using data collected in a number of development studies." Similar to the test sets, the ground truth for rhythms in the training data would also have been established using reference electrocardiogram (ECG) data. The document implies that these development studies also involved the collection of "pulse rate recordings on a variety of rhythms including: atrial fibrillation, normal sinus rhythm, sinus arrhythmia, and other ectopic beats (PVCs, PACs)," which would necessitate ECG interpretation to label these rhythms for training the algorithm.

Summary

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June 3, 2022

Apple Inc. Luke Olson Regulatory Affairs 1 Apple Park Way Cupertino, California 95014

Re: K213971

Trade/Device Name: Atrial Fibrillation History Feature Regulation Number: 21 CFR 870.2790 Regulation Name: Photoplethysmograph analysis software for over-the-counter use Regulatory Class: Class II Product Code: QDB Dated: Mav 2, 2022 Received: May 3, 2022

Dear Luke Olson:

We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.

If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.

Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's

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requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (OS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.

Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.

For comprehensive regulatory information about mediation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).

Sincerely,

Jennifer Shih Kozen Assistant Director Division of Cardiac Electrophysiology, Diagnostics and Monitoring Devices Office of Cardiovascular Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health

Enclosure

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Indications for Use

510(k) Number (if known) K213971

Device Name Atrial Fibrillation History Feature

Indications for Use (Describe)

The feature is intended for use with the Apple Watch and the Health app on iPhone.

Type of Use (Select one or both, as applicable)
Prescription Use (Part 21 CFR 801 Subpart D)	Over-The-Counter Use (21 CFR 801 Subpart C)

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510(k) Summary

This summary of 510(k) safety and effectiveness information is submitted in accordance with the requirements of 21 CFR §807.92:

5.1 Submitter

Applicant	Apple Inc.One Apple Park WayCupertino, CA 95014
PrimaryCorrespondent	Luke OlsonRegulatory AffairsPhone: (408) 609-2001Email: luke_olson@apple.com
SecondaryCorrespondent	Dachan KwonRegulatory AffairsPhone: (669) 268-5659Email: dachan_kwon@apple.com
Date Prepared	May 28, 2022

5.2 Device Names and Classifications

Subject Device:

Name of Device	Atrial Fibrillation History Feature
Classification Name	Photoplethysmograph Analysis Software For Over-The-Counter Use,21 CFR 870.2790
Regulatory Class	Class II
Product Code	QDB
510(k) ReviewPannel	Cardiovascular

Predicate Device:

PredicateManufacturer	Apple Inc.
Predicate TradeName	Irregular Rhythm Notification Feature
Predicate 510(k)	K212516

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5.3 Device Description

The Atrial Fibrillation History Feature (AFib History Feature) is comprised of a pair of mobile medical apps - one on Apple Watch and the other on the iPhone.

The AFib History Feature iPhone App is part of the Health App. which allows users to store, manage, and share health and fitness data, and comes pre-installed on every iPhone.

· Weekly Estimate an estimate of the amount of time a user was in Atrial Fibrillation over the past calendar week during watch wear, presented to the user as a percentage.
Day of Week Estimate an estimate of the amount of time a user was in Atrial Fibrillation on each day of the week over the previous 42 days during watch wear, presented to the user as a percentage. That is, all Mondays over the past 42 days, all Tuesdays over the past 42 days.
Time of Day Estimate an estimate of the amount of time a user was in Atrial Fibrillation on 4-hour segments of the day over the previous 42 days during watch wear, presented to the user as a percentage. That is, all 12 am - 4 am segments over the past 42 days, all 4 am - 8 am segments over the past 42 days.

The AFib History Feature is intended to serve as an extension of the predicate Irregular Rhythm Notification feature, but has been optimized for users with a diagnosis of Afib.

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5.4 Indications for Use

The Atrial Fibrillation (AFib) History Feature is an over-the-counter ("OTC") software-only mobile medical application intended for users 22 years of age and over who have a diagnosis of atrial fibrillation (AFib). The feature opportunistically analyzes pulse rate data to identify episodes of irreqular heart rhythms suggestive of AFib and provides the user with a retrospective estimate of AFib burden (a measure of the amount of time spent in AFib during past Apple Watch wear).

The feature also tracks and trends estimated AFib burden over time, and includes lifestyle data visualizations to enable users to understand the impact of certain aspects of their lifestyle on their AFib. It is not intended to provide individual irregular rhythm notifications or to replace traditional methods of diagnosis, treatment, or monitoring of AFib.

The feature is intended for use with the Apple Watch and the Health app on iPhone.

5.5 Comparison with the Predicate Device

Item	Subject Device	Predicate Device
	AFib History Feature	IRNF 2.0 App
Manufacturer	Apple Inc.	Apple Inc.
SubmissionReference	K213971	K212516
Intended Use	Photoplethysmograph analysissoftware for over-the-counter use. Aphotoplethysmograph analysissoftware device for over-the-counteruse analyzes photoplethysmographdata and provides information foridentifying irregular heart rhythms.This device is not intended to providea diagnosis.	Photoplethysmograph analysissoftware for over-the-counter use. Aphotoplethysmograph analysissoftware device for over-the-counteruse analyzes photoplethysmographdata and provides information foridentifying irregular heart rhythms.This device is not intended to providea diagnosis.
	Subject Device	Predicate Device
Item	AFib History Feature	IRNF 2.0 App
Indications forUse	The Atrial Fibrillation (AFib) HistoryFeature is an over-the-counter("OTC") software-only mobile medicalapplication intended for users 22years of age and over who have adiagnosis of atrial fibrillation (AFib).The feature opportunistically analyzespulse rate data to identify episodes ofirregular heart rhythms suggestive ofAFib and provides the user with aretrospective estimate of AFib burden(a measure of the amount of timespent in AFib during past Apple Watchwear).The feature also tracks and trendsestimated AFib burden over time, andincludes lifestyle data visualizations toenable users to understand theimpact of certain aspects of theirlifestyle on their AFib. It is notintended to provide individual irregularrhythm notifications or to replacetraditional methods of diagnosis,treatment, or monitoring of AFib.The feature is intended for use withthe Apple Watch and the Health appon iPhone.	The Irregular Rhythm NotificationFeature is a software-only mobilemedical application that is intended tobe used with the Apple Watch. Thefeature analyzes pulse rate data toidentify episodes of irregular heartrhythms suggestive of atrial fibrillation(AFib) and provides a notification tothe user. The feature is intended forover-the-counter (OTC) use. It is notintended to provide a notification onevery episode of irregular rhythmsuggestive of AFib and the absence ofa notification is not intended toindicate no disease process ispresent; rather the feature is intendedto opportunistically surface anotification of possible AFib whensufficient data are available foranalysis. These data are only capturedwhen the user is still. Along with theuser's risk factors the feature can beused to supplement the decision forAFib screening. The feature is notintended to replace traditionalmethods of diagnosis or treatment.The feature has not been tested forand is not intended for use in peopleunder 22 years of age. It is also notintended for use in individualspreviously diagnosed with AFib
Principle ofOperation	The AFib History Feature acquiresplatform sensor data from AppleWatch. After acquisition, the AfibHistory Feature algorithms analyzepulse rate data to identify episodes ofirregular heart rhythms suggestive ofatrial fibrillation (AFib) and aggregatesthose episodes to provide the userwith an estimate of atrial fibrillationburden during watch wear.	The IRN 2.0 app acquires platformsensor data from Apple Watch. Afteracquisition, the IRN app algorithmsanalyze pulse rate data to identifyepisodes of irregular heart rhythmssuggestive of atrial fibrillation (AFib)and provides notification to the user.
	Subject Device	Predicate Device
Item	AFib History Feature	IRNF 2.0 App
ClinicalPerformance	See below for a discussion of clinicalperformance testing supporting theAFib History Feature.	Apple conducted a clinical validationstudy to assess the performance ofIRNF 2.0 app relative to that of theIRNF 1.0 on a common sensor dataset.IRNF 2.0 person-level sensitivity(88.6%) and specificity (99.3%) wereboth demonstrated to be non-inferiorto those of the IRNF 1.0.
Compatibilitywith IntendedPlatforms	iOS version 16.0 or laterwatchOS version 9.0 or later	iOS version 15.5 or laterwatchOS version 8.5 or later
	Apple Watch Series 4, 5, SEiPhone 6s and later	Apple Watch Series 3, 4, 5, SEiPhone 6s and later

Table 1. AFib History Feature Comparison with the Predicate

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5.6 Performance Testing

The AFib History Feature was verified and validated according to Apple's internal design control processes and in accordance with the special controls for Photoplethysmograph Analysis software for over-the-counter use (21 CFR 870.2790). The testing demonstrated that the device performed according to its specifications and that the technological and performance criteria are comparable to the predicate device.

The AFib History Feature includes a rhythm classification algorithm that leverages machine learning techniques to differentiate between AFib and non-AFib rhythms. The classifier algorithm is the same that is used in the predicate device, but has been optimized for use in the AFib History Feature's indicated use population, where there is an a priori expectation of AFib.

The rhythm classification algorithm uses a convolutional neural network based architecture and was trained extensively using data collected in a number of development studies. In total, the studies included over 2500 subjects and collected over 3 million pulse rate recordings on a variety of rhythms including: atrial fibrillation, normal sinus rhythm, sinus arrhythmia, and other ectopic beats (PVCs, PACs).

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The studies used to train the convolutional network recruited demographically diverse populations with broad representation of age, sex, BMI, race, and skin tones. Table 2 below summarizes approximate development study demographic characteristics:

Age Group (years)
<55	39.5%
>=55 to <65	25.4%
>=65	35.1%
Sex
Male	49.6%
Female	50.4%
BMI (kg/m²)
<18.5	2.2%
>=18.5 to <25.0	32.7%
>=25.0 to <30.0	32.2%
>=30.0	32.9%
Race
White	71.5%
Black or African American	18.0%
Other	10.5%

Table 2. Development Study Subject Demographics

For the purpose of developing the algorithm, the data was split into four sets with matching distributions of rhythms and demographics: Training, Validation, Test, and Sequestration sets. The model was trained on the Training set, with the Validation set used for early stopping and threshold selection. The model was then evaluated on the Testing set at regular intervals during model development. When development was complete the model was locked, and then evaluated on the Sequestration set as a last test to ensure it had not been over-fit to the development data.

The classifier algorithm's performance on the development studies dataset when used in both the AFib History Feature & the Irreqular Rhythm Notification Feature is characterized by the receiver operating characteristic (ROC) curve & table 3 below.

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Image /page/9/Figure/2 description: The image is a plot of sensitivity versus 100 - specificity. The y-axis is labeled "Sensitivity (%)" and ranges from 70 to 100. The x-axis is labeled "100 - Specificity (%)" and ranges from 0.0 to 2.0. The plot shows a green curve that starts at approximately (0.1, 79) and increases to approximately (1.1, 97). The plot also shows two points labeled "IRNF 2.0" and "AFib History".

Table 3. Rhythm Classification Algorithm - Development Studies Performance

	Sensitivity	Specificity
AFib History Feature	97%	99.0%
IRNF 2.0 (predicate)	79.6%	99.9%

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5.7 Clinical Performance

The performance of the AFib History Feature was extensively tested in a clinical study of 413 participants ages 22 and older with a mix of AFib diagnoses (paroxysmal & permanent). Enrolled subjects wore an Apple Watch and a reference electrocardiogram (ECG) patch concurrently for up to 13 days. Study demographic characteristics are summarized in the table below:

	N=413
Age Group (years)
<55	59 (14.3%)
>=55 to <65	99 (24.0%)
>=65	255 (61.7%)
Sex
Male	219 (53.0%)
Female	194 (47.0%)
Ethnicity
Hispanic or Latino	19 (4.6%)
Non-Hispanic or Latino	394 (95.4%)
Race
White	371 (89.8%)
Black or African American	31 (7.5%)
Other	11 (2.7%)

Table 3. IRNF 2.0 Clinical Study Subject Demographics

The objective of the study was to assess the accuracy of the weekly AFib burden estimate generated by the feature compared to a weekly AFib burden reference measurement. To do so, Apple employed a Bland-Altman Limits of Agreement (LoA) approach. A LoA approach is a way of assessing agreement accuracy between two measurement methods.

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Of the 413 enrolled subjects. 280 contributed data to the primary endpoint analysis to determine if the level of agreement between the reference ECG AFib burden and the feature's AFib burden estimate was acceptable. Based on the results of the study, the lower and upper Bland-Altman limits (i.e., two standard deviations from the mean difference) were -11.4% and 12.8%, respectively.

The average difference between the feature's weekly burden estimate and reference weekly burden was 0.67%. 92.9% (260/280) of subjects had paired weekly AFib burden differences within ±5%; 95.7% (268/280) of subjects' weekly AFib burden estimates were within +/- 10%.

The AFib History Feature and the Irregular rhythm Notification Feature (IRNF 2.0) use the same tachogram classification algorithm that leverages machine learning techniques to differentiate between AFib and non-AFib rhythms. The classification algorithm analyzes pulse rhythm samples collected by Apple Watch and uses a convolutional network based architecture. For use in the AFib History Feature the algorithm's operating point was adjusted to prioritize sensitivity. Table 4 below outlines the sensitivity and specificity of the classification algorithm for the AFib History Feature and IRNF 2.0 in the clinical validation study.

	Sensitivity	Specificity
AFib History Feature	92.6%	98.8%
IRNF 2.0 (predicate)	85.5%	99.6%

Table 4. Classification Algorithm - Clinical Validation Study Performance

These results demonstrate that the AFib History Feature is effective in generating accurate AFib burden estimates.

5.8 Human Factors Testing

The AFib History Feature was found to be safe and effective as compared to the predicate for the intended users, uses, and use environments. This conclusion is supported by iterative human factors analyses and evaluations on the Feature, resulting design modifications, and the ultimate analysis of the summative/validation testing results.

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5.9 Conclusion

The AFib History Feature is substantially equivalent to IRNF 2.0 as they are identical with respect to intended use and there are no differences in technological or performance characteristics that raise new questions of safety and effectiveness.

Regulation Number and Section

§ 870.2790 Photoplethysmograph analysis software for over-the-counter use.

(a)
Identification. A photoplethysmograph analysis software device for over-the-counter use analyzes photoplethysmograph data and provides information for identifying irregular heart rhythms. This device is not intended to provide a diagnosis.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Clinical performance testing must demonstrate the performance characteristics of the detection algorithm under anticipated conditions of use.
(2) Software verification, validation, and hazard analysis must be performed. Documentation must include a characterization of the technical specifications of the software, including the detection algorithm and its inputs and outputs.
(3) Non-clinical performance testing must demonstrate the ability of the device to detect adequate photoplethysmograph signal quality.
(4) Human factors and usability testing must demonstrate the following:
(i) The user can correctly use the device based solely on reading the device labeling; and
(ii) The user can correctly interpret the device output and understand when to seek medical care.
(5) Labeling must include:
(i) Hardware platform and operating system requirements;
(ii) Situations in which the device may not operate at an expected performance level;
(iii) A summary of the clinical performance testing conducted with the device;
(iv) A description of what the device measures and outputs to the user; and
(v) Guidance on interpretation of any results.