The AI-ECG Tracker is intended to be used by qualified healthcare professionals in hospitals and healthcare facilities for the assessment of arrhythmias using ECG data acquired from adults (age 22 and older) without pacemakers. The product supports downloading and analyzing data recorded from electrodes with conductive paste/gel placed on standard location in compatible formats from any device used for the arrhythmia diagnostics such as Holter, event recorder, 12-lead ambulatory ECG devices when assessment of the rhythm is necessary. The AI-ECG Tracker provides ECG signal processing and analysis on a beat by beat basis, QRS detection, Supraventricular Ectopic Beat detection, QRS feature extraction, interval measurement, and rhythm analysis. The AI-ECG Tracker is not for use in life supporting or sustaining systems or ECG monitoring and Alarm devices.

The AI-ECG Tracker interpretation results are not intended to be the sole means of diagnosis for any abnormal ECG. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms, and other diagnostic information.

Device Description

The AI-ECG Tracker is a distributed ECG auto analysis system designed to assist physicians and qualified healthcare professionals in measuring and interpreting ambulatory ECG data. The interpretations by the analysis program can be confirmed, edited, modified, or deleted by the physician and qualified healthcare professionals. The program is intended for use by qualified healthcare professionals in hospitals and other healthcare facilities for the assessments of common cardiac arrhythmias using ECG data acquired from adults (age 22 and older) without pacemakers.

The AI-ECG Tracker receives ECG waveform data uploaded by a user, analyzes ECG data and automatically interprets on a computer server. The ECG measurement, interpretation and waveform data are then downloaded to the Physician Diagnostic Client for a physician to review, modify, confirm the analysis statements, and print the report. The original ECG waveform data is stored permanently in the user's server computer securely.

The system uses a machine learning based process (convolutional neural network or CNN) only for development of the AI ECG algorithm. The AI ECG algorithm is only used for beat classification. After the AI ECG algorithm is developed, the AI-based beat classification model is locked in the released product which means the marketed device doesn't have active machine learning or selflearning features.

AI/ML Overview

Here's a breakdown of the acceptance criteria and study information based on the provided document:

1. Acceptance Criteria and Reported Device Performance

The document primarily focuses on demonstrating substantial equivalence to a predicate device rather than explicitly stating acceptance criteria with specific performance metrics for the AI-ECG Tracker. However, it does reference compliance with performance standards.

Acceptance Criteria (Implied / Referenced Standard)	Reported Device Performance (Compliance)
Basic safety and essential performance	Comply with IEC 60601-2-25
Measurement performance (Cardiac Rhythm and ST-Segment algorithms)	Comply with AAMI/ANSI EC57 and IEC 60601-2-47

Note: The document states that "Bench tests were conducted to verify that the subject device met all design specifications, as was Substantially Equivalent (SE) to the predicate device." This suggests that the device's performance was evaluated against the predicate device's capabilities and relevant standards to establish substantial equivalence. Specific quantitative performance values (e.g., sensitivity, specificity for arrhythmia detection) are not provided in this document.

2. Sample Size for Test Set and Data Provenance

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective nature of the data). It mentions "bench tests," which typically cover technical performance and compliance with standards rather than clinical validation on a specific dataset.

3. Number of Experts and Qualifications for Ground Truth

The document does not provide details on the number of experts used or their qualifications for establishing ground truth for the test set.

4. Adjudication Method for the Test Set

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

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

There is no indication that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was conducted or reported in this document. The focus is on the device's standalone performance and its equivalence to a predicate device.

6. Standalone (Algorithm Only) Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was performed. The document states:

"The AI-ECG Tracker provides ECG signal processing and analysis on a beat by beat basis, QRS detection, Supraventricular and Ventricular Ectopic Beat detection, QRS feature extraction, interval measurement, heart rate measurement, and rhythm analysis."
"Bench tests were conducted to verify that the subject device met all design specifications..."
The "AI-ECG Tracker" is presented as an automated analysis system that "automatically interprets on a computer server."

This indicates that the algorithm's performance in analyzing ECG data was evaluated independently.

7. Type of Ground Truth Used

The specific type of ground truth used for performance evaluation is not explicitly stated. However, given the context of "bench tests" and compliance with standards like AAMI/ANSI EC57, it is highly likely that the ground truth for algorithm performance was established through:

Reference annotated ECG databases: Standardized ECG databases with expert-adjudicated annotations are commonly used for validating ECG analysis algorithms.
Expert consensus: For specific cases or discrepancies, expert cardiologists would establish the true rhythm or beat classification.

8. Sample Size for the Training Set

The document does not provide the sample size used for the training set. It mentions that the "system uses a machine learning based process (convolutional neural network or CNN) only for development of the AI ECG algorithm."

9. How Ground Truth for the Training Set Was Established

While the document states that a CNN was used for "development of the AI ECG algorithm" (for beat classification), it does not explicitly detail how the ground truth for this training set was established. In typical machine learning development for medical devices, this would involve:

Expert annotation: Cardiologists or trained technicians meticulously annotating ECG recordings to label various beat types and rhythms.
Validated databases: Leveraging existing, publicly available, or proprietary ECG databases that come with expert-verified annotations.

Summary

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March 20, 2020

Shenzhen Carewell Electronics Co., Ltd. % Arthur Goddard President FDA Regulatory and Quality Systems Consultant 31853 Cedar Road Mayfield Heights, Ohio 44124-4445

Re: K200036

Trade/Device Name: AI-ECG Tracker Regulation Number: 21 CFR 870.1425 Regulation Name: Programmable Diagnostic Computer Regulatory Class: Class II Product Code: DQK, DPS Dated: January 3, 2020 Received: January 8, 2020

Dear Arthur Goddard:

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,

for

Jessica Paulsen 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) K200036

Device Name AI-ECG Tracker

Indications for Use (Describe)

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)

CONTINUE ON A SEPARATE PAGE IF NEEDED.

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Section 5: 510(K) Summary

This summary of 510(K) safety and effectiveness information is submitted in accordance with the requirements of SMDA 1900 and 21 CFR 807.92.

The assigned 510(K) Number:K200036

ട. 510(K) Summary

5.1. Date of Preparation: January 3rd, 2020

5.2. Sponsor

Shenzhen Carewell Electronics Co., Ltd.

Floor 4, BLD 9, Baiwangxin High-Tech Industrial Park, Songbai Road, Xili Street, Nanshan District 518108, Shenzhen, P.R. China Establishment Registration Number: 3010089768 Contact Person: Chang Liu Position: General Manager Tel: +86-755-86170389 Fax: +86-755-86170478 Email: standard@carewell.com.cn

5.3. Submission Correspondent

Mr. Arthur Goddard 31853 Cedar Road, Ohio, 44124-4445, U.S.A. Tel: (216) 233-5722 Email: asjgoddard@aol.com

Subject Device Identification 5.4.

Subject Device Name: AI-ECG Tracker Edition: S Edition Common name: ECG Analysis Software Classification Name(s): Programmable Diagnostic Computer / Electrocardiograph Product Code: DOK, DPS Regulation Number: 21 CFR 870.1425 Review Panel: Cardiovascular Classification: II

5.5. Predicate Device

510(k) Number: K062282

Device Name: Monebo Automated ECG Analysis and Interpretation Software Library, Version 3.0 Manufacturer: Monebo Technologies, Inc.

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Reference Device 5.6.

510(k) Number: K113485

Device Name: Electrocardiograph Manufacturer: Shenzhen Carewell Electronics Co., Ltd.

5.7. Indications for use

The AI-ECG Tracker is intended to be used by qualified healthcare professionals in hospitals and healthcare facilities for the assessment of arrhythmias using ECG data acquired from adults (age 22 and older) without pacemakers. The product supports downloading and analyzing data recorded from electrodes with conductive paste/gel placed on standard location in compatible formats from any device used for the arrhythmia diagnostics such as Holter, event recorder, 12-lead ambulatory ECG devices when assessment of the rhythm is necessary. The AI-ECG Tracker provides ECG signal processing and analysis on a beat by beat basis, QRS detection, Supraventricular and Ventricular Ectopic Beat detection, QRS feature extraction, interval measurement, heart rate measurement, and rhythm analysis. The AI-ECG Tracker is not for use in life supporting or sustaining systems or ECG monitoring and Alarm devices.

5.8. Contraindications

Not suitable for diagnosis of non-cardiac abnormalities. Not suitable for diagnosing patients with pacemakers. Not suitable for diagnosing patients age 21 and under. Not suitable for diagnosis other than cardiac arrhythmia. The device must not be used as a physiological monitoring of vital signs.

5.9. Special Requirement

The input ECG data is required to contain at least one of leads I, II, III, V1, and V5.

5.10. Device Description

The AI-ECG Tracker receives ECG waveform data uploaded by a user, analyzes ECG data and automatically interprets on a computer server. The ECG measurement, interpretation and waveform

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data are then downloaded to the Physician Diagnostic Client for a physician to review, modify, confirm the analysis statements, and print the report. The original ECG waveform data is stored permanently in the user's server computer securely.

Item	Subject DeviceAI-ECG Tracker	Predicate DeviceK062282 MoneboAutomated ECG Analysisand Interpretation SoftwareLibrary, Version 3.0	Remark
Indicationsfor use	The AI-ECG Tracker is intended to be used by qualified healthcare professionals in hospitals and healthcare facilities for the assessment of arrhythmias using ECG data acquired from adults (age 22 and older) without pacemakers. The product supports downloading and analyzing data recorded from electrodes with conductive paste/gel placed on standard location in compatible formats from any device used for the arrhythmia diagnostics such as Holter, event recorder, 12-lead ambulatory ECG devices when assessment of the rhythm is necessary. The AI-ECG Tracker provides ECG signal processing and analysis on a	The Automatic Analysis and Interpretation Software Library is intended for use by qualified medical professionals for the assessment of arrhythmias using historic ambulatory EGG data. The product supports downloading and analyzing data recorded in compatible formats from any device used for the arrhythmia diagnostics such as Holter, Event Monitor, 12 lead ambulatory or resting EGG devices, or other similar devices when assessment of the rhythm is necessary. The Automatic Analysis and Interpretation Software Library can also be electronically interfaced, and perform analysis with data	No substantial difference
Item	Subject DeviceAI-ECG Tracker	Predicate DeviceK062282 MoneboAutomated ECG Analysisand Interpretation SoftwareLibrary, Version 3.0	Remark
	beat by beat basis, QRS detection, Supraventricular and Ventricular Ectopic Beat detection, QRS feature extraction, interval measurement, heart rate measurement, and rhythm analysis. The AI-ECG Tracker is not for use in life supporting or sustaining systems or ECG monitoring and Alarm devices.	transferred from other computer based EGG systems, such as an EGG management system. The Automatic Analysis and Interpretation Software Library provides EGG signal processing and analysis on a beat by beat basis, QRS and Ventricular Ectopic Beat detection, QRS feature extraction, interval measurement, heart rate measurement, and rhythm analysis for up to sixteen(16) leads of captured data. The library is not for use in life supporting or sustaining systems or EGG monitoring and Alarm devicesThe product can be integrated into computerized EGG monitoring devices. In this case the medical device manufacturer will identify the indication for use depending on the application of their device.
	The AI-ECG Tracker interpretation results are not intended to be the sole means of diagnosis for any abnormal ECG. It is offered to physicians and clinicians on an advisory basis only in conjunction with the physician's knowledge of ECG patterns, patient background, clinical history, symptoms, and other diagnostic information.
Algorithm	Proprietary Algorithm	Proprietary Algorithm	Different
Level ofConcern ofthe software	Major	Moderate	Different
DiagnosticStatement	Heart rate determination fornon-paced adultQRS DetectionNon-paced arrhythmia interpretationfor adult patients	Heart rate determination fornon-paced adultQRS DetectionNon-paced arrhythmiainterpretation for adult patients	Same
Item	Subject DeviceAI-ECG Tracker	Predicate DeviceK062282 MoneboAutomated ECG Analysisand Interpretation SoftwareLibrary, Version 3.0	Remark
	Non-paced ventricular arrhythmiacalls for adult patientsIntervals measurementVentricular ectopic beat detection	Non-paced ventricular arrhythmiacalls for adult patientsIntervals measurementVentricular ectopic beat detection
Fundamentalscientifictechnology	The AI-ECG Tracker consists of:- Interfaces which provide tools to measure, analyze, interpret, review ECGs, and to generate and print reports.- Automated ECG interpretation algorithms that measure and analyze ECGs to provide supplementary information for ECG diagnosis.The device is developed in C# and C/C++ language, supported by Microsoft .Net framework and .Net Core runtime.Components and libraries can be accessed through the Application Programming Interface (API). CarewellAI-ECG Tracker requires local wired or wireless network	The predicate device is a collection of callable functions that have been complied into machine code or IDL code of the computer on which they execute.The predicate device consists of a basic application for viewing, analyzing and annotating ECG data, and a callable object library built on the Microsoft .Net framework.An application software can be written to invoke some or all the functions in an object library.The library can be accessed through an Application program Interface (API) as a callable function. This allows the library to be used as an accessory to an ECG management application or as a stand-alone product	No substantial difference.The subject device AI-ECG Tracker and the predicate device K062282 both built with Microsoft .Net framework with similar application architect, both have user's interface for viewing, analyzing and interpreting ECG data, and both allow the core algorithm library to be accessed via APIs.

5.11. Predicate Devices and Subject Device Comparison

Table 3-1 Feature Comparison with Predicate Devices

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Shenzhen Carewell Electronics Co., Ltd.

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Shenzhen Carewell Electronics Co., Ltd.

Table 3-2 Performance Comparison

Item	Subject DeviceAI-ECG Tracker	Predicate DeviceK062282 Monebo Automated ECGAnalysis and InterpretationSoftware Library, Version 3.0	Remark
Basic safetyand essential	Comply with IEC 60601-2-25	Comply with IEC 60601-2-25	Same

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Traditional 510(k) Premarket Notification AI-ECG Tracker

Item	Subject Device	Predicate Device	Remark
	AI-ECG Tracker	K062282 Monebo Automated ECGAnalysis and InterpretationSoftware Library, Version 3.0
performance
Measurementperformance	Comply with AAMI/ANSI EC57 andIEC 60601-2-47.	Comply with AAMI/ANSIEC57 and IEC 60601-2-47.	Same

5.1. Non-Clinical Test Conclusion

Bench test were conducted to verify that the subject device met all design specifications, as was Substantially Equivalent (SE) to the predicate device. The test results demonstrated that the subject device complies with the following standards.

AAMI ANSI EC57:2012 Testing And Reporting Performance Results Of Cardiac Rhythm And ST-Segment Measurement Algorithms;

IEC 60601-2-47:2012 Medical Electrical Equipment - Part 2-47: Particular Requirements For The Basic Safety And Essential Performance of Ambulatory Electrocardiographic Systems;

IEC 60601-2-25:2011, Medical Electrical Equipment -- Part 2-25: Particular requirements for the safety of electrocardiographs;

IEC 62304 Edition 1.1 2015-06, Medical device software - Software life-cycle;

ISO 14971:2007, Medical devices-Application of risk management to medical device.

5.2. Substantially Equivalent Conclusion

The subject device, AI-ECG Tracker, is determined to be Substantially Equivalent (SE) to the predicate device, Monebo Automated ECG Analysis and Interpretation Software Library, Monebo Technologies, Inc. K062282.

Regulation Number and Section

§ 870.2340 Electrocardiograph.

(a)
Identification. An electrocardiograph is a device used to process the electrical signal transmitted through two or more electrocardiograph electrodes and to produce a visual display of the electrical signal produced by the heart.(b)
Classification. Class II (performance standards).