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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.

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.

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.

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.

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CONTECTM Electrocardiographs, ECG100G/ECG300G/ECG1200G, are intended to acquire ECG signals from adult patients through body surface ECG electrodes. The obtained ECG records can help users to analyze and diagnose heart disease. Digital Electrocardiographs shall be used in healthcare facilities by doctors and/or trained healthcare professionals.

The proposed device, CONTECTM Electrocardiograph, has three models: ECG100G, ECG300G and ECG1200G. The three models all have three design modules, which are power module, signal acquisition and processing module and control module. The proposed devices acquire ECG signal via twelve leads simultaneously, display or print waveform of ECG signal via single channel/ three channel/ twelve channel. The proposed device, model ECG100G, has two recording modes, including automatic mode and manual mode; the other two models, ECG300G and ECG1200G have three recording modes, including automatic mode, manual mode and rhythm mode. The proposed devices are designed to acquire, process, display and record ECG signals from patient body surface by ECG electrodes. After been amplified and filtered, the ECG signal waveforms are displayed on the LCD screen and recorded on the paper through thermal printer. ECG data, waveform and patient information could be stored in the memory of the device;

The provided document K131900 is a 510(k) summary for the CONTECTM Electrocardiograph. It outlines the device's technical specifications and compares it to predicate devices to establish substantial equivalence. However, it does not describe a clinical study with acceptance criteria and reported device performance in the way a traditional clinical efficacy study would.

Instead, this document focuses on non-clinical tests to verify that the proposed device meets design specifications and complies with relevant international and national standards. The acceptance criteria are implicit in these standards.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly listed with corresponding "reported device performance" in terms of clinical outcomes or diagnostic accuracy. Instead, the document states that "Non clinical tests were conducted to verify that the proposed device met all design specifications as was Substantially Equivalent (SE) to the predicate device." The "reported device performance" is the statement that it complies with the listed standards and its technical specifications match or are similar to the predicate devices.

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

The document does not describe a clinical "test set" in the context of diagnostic accuracy. The tests performed are non-clinical, related to electrical safety, electromagnetic compatibility, and performance according to engineering standards. There is no mention of patient data or its provenance (country of origin, retrospective/prospective).

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

Not applicable. No clinical test set requiring expert ground truth establishment is described.

4. Adjudication method for the test set

Not applicable. No clinical test set requiring adjudication is described.

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

Not applicable. This device is an electrocardiograph, which acquires and displays ECG signals. It does not appear to incorporate AI for interpretation or assistance with human readers. The document does not mention any "Measurement/Analysis Function" for the proposed device, as shown in the comparison table where it explicitly states "No" for this function, contrasting with one of the predicate devices that has "Yes".

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

Not applicable. The device itself is an acquisition device, not an algorithm for interpretation. It explicitly states "No" for "Measurement/Analysis Function."

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

Not applicable. As described, the "ground truth" for this submission are the engineering and medical device standards (e.g., IEC 60601 series, ANSI/AAMI EC11) which define acceptable electrical and physical performance, rather than diagnostic accuracy against a clinical gold standard.

8. The sample size for the training set

Not applicable. There is no mention of a training set as this is not a machine learning or AI-driven device with an interpretation algorithm.

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

Not applicable. As above, no training set or its ground truth establishment is mentioned.

Summary of the Study:

The "study" described in K131900 is a series of non-clinical bench tests designed to demonstrate that the CONTECTM Electrocardiograph adheres to established electrical safety, electromagnetic compatibility (EMC), and specific performance standards for electrocardiographs. The "acceptance criteria" are the requirements set forth in these international and national standards (IEC 60601-1, IEC 60601-1-2, IEC 60601-2-25, ANSI/AAMI EC11) and the technical specifications listed in the comparison table, which are either identical or similar to legally marketed predicate devices. The "proof" is the statement that "The test results demonstrated that the proposed device complies with the following standards" and the detailed comparison table that shows its technical specifications meet or are equivalent to the predicate devices. This type of submission is common for medical devices that aim to show substantial equivalence based on technical and performance characteristics, without requiring new clinical efficacy data if the intended use and technology are sufficiently similar to approved predicate devices.

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