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Digital Electrocardiographs, IE3/ IE12/ IE12P/ IE15, are intended to acquire ECG signals from adult and pediatric 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.

Digital Electrocardiographs, IE3/ IE12/ IE12/ IE15, are designed to acquire, analyze, display and record ECG signals from patient body surface by ECG electrodes. After been amplified, filtered and analyzed, the ECG signals waveforms and analysis results are displayed in the LCD and recorded in the paper through thermal printer. ECG data, analysis result and patient information could be stored in the memory of the device.
The device consists of three modules, which are power supply module, amplification module, and control module.
The device has three recording mode, AUTO mode, MAN mode and RHY mode,
They are standard twelve leads, including bipolar limb leads, augmented unipolar limb leads and unipolar chest leads.

The provided 510(k) summary for K131262 does not contain information related to a clinical study or performance data against specific acceptance criteria for the device's diagnostic capabilities. Instead, it focuses on demonstrating substantial equivalence to predicate devices through non-clinical testing and technical specifications.

Therefore, many of the requested sections regarding acceptance criteria, study details, and performance metrics cannot be directly addressed from the provided document.

Here's a breakdown of what can be extracted and what is missing:

Description of Acceptance Criteria and Device Performance

The submission focuses on establishing substantial equivalence to predicate devices (K122712 and K123816) based on technical specifications and compliance with international standards, rather than defining specific diagnostic acceptance criteria for the device's analytical function.

The non-clinical tests were conducted to verify that the proposed device met all design specifications and was Substantially Equivalent (SE) to the predicate devices. The test results demonstrated compliance with the following standards:

• IEC 60601-1:1990+ A1:1993+ A11:1993+ A12:1993+ A13:1996, Medical electrical equipment, Part 1: General requirements for safety.
• IEC 60601-2-25:1993+A1:1999. Medical electrical equipment, Part 2-25: Particular requirements for the safety of electrocardiographs.
• IEC 60601-1-2: 2007, Medical electrical equipment, Part 1-2: General requirements for basic safety and essential performance- Collateral standard: Electromagnetic compatibility- Requirements and tests.

Table of Acceptance Criteria and Reported Device Performance (Based on Technical Specifications and Compliance to Standards):

Missing Information from the Provided Document:

1. Sample size used for the test set and the data provenance: Not mentioned. The submission relies on non-clinical testing for compliance with standards and technical equivalency, not clinical diagnostic performance data.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable, as no diagnostic performance study with specific ground truth from experts is described.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
4. 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 for human interpretation, it does not include AI for interpretation or an automated analysis mode (as per "Analysis mode: No" in the comparison table).
5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. The device's "Analysis mode" is listed as "No," indicating it does not perform automated algorithmic diagnosis. It is intended for healthcare professionals to analyze and diagnose heart disease from the acquired ECG records.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable, as no diagnostic performance study with ground truth is presented. The "ground truth" here is compliance with established safety and performance standards for an ECG device.
7. The sample size for the training set: Not applicable, as there is no mention of an algorithm or AI component that would require a training set.
8. How the ground truth for the training set was established: Not applicable.

Conclusion:

This 510(k) summary is for a traditional Digital Electrocardiograph, which is a signal acquisition and display device, not an AI-powered diagnostic system. Its approval is based on demonstrating substantial equivalence to existing legally marketed devices through compliance with industry standards and shared technical specifications. The document does not include data from a clinical performance study measuring diagnostic accuracy against specific acceptance criteria for disease detection or classification by an algorithm. The device "can help users to analyze and diagnose heart disease," implying the diagnostic responsibility remains with the human healthcare professional.

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Digital Electrocardiographs, iE 12A / iE 15S, are intended to acquire ECG signals from adult and pediatric 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.

Digital Electrocardiographs, iE 12A / iE 15 / iE 15S, are designed to acquire, display and record ECG signals from patient body surface by ECG electrodes. After been amplified and filtered, the ECG signals waveforms are displayed in the LCD and recorded in the paper through thermal printer. ECG data result and patient information could be stored in the memory of the device.

All the models. iE 12A / iE 15 / iE 15S, of the proposed device. Digital Electrocardiographs, follow · the same design principle and similar technical specifications.

The provided text describes a 510(k) premarket notification for Digital Electrocardiographs (models iE 12A, iE 15, iE 15S). The submission focuses on demonstrating substantial equivalence to a predicate device (iE 12) rather than presenting a study with specific acceptance criteria related to clinical performance metrics like sensitivity, specificity, or reader improvement.

Therefore, many of the requested details, such as specific acceptance criteria for diagnostic performance, sample sizes for test and training sets for an AI algorithm, expert qualifications, adjudication methods, or MRMC study results, are not available in this document. This document primarily details the technical specifications and safety/EMC standards met by the device.

Here's a breakdown of what is available and what is not:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in the sense of diagnostic performance metrics (e.g., sensitivity, specificity, AUC). Instead, it refers to verifiable technical specifications and compliance with international standards for safety and electrical performance. The "reported device performance" in this context refers to the device meeting these technical specifications and standards.

Study Proving Acceptance Criteria Met:
The document states: "Bench tests were conducted to verify that the proposed device met all design specifications as was Substantially Equivalent (SE) to the predicate device. The test results demonstrated that the proposed device complies with the following standards: EN 60601-1, EN 60601-2-25, EN 60601-1-2, IEC60601-2-51."

The primary "acceptance criterion" for this 510(k) submission is demonstrating substantial equivalence to the predicate device, which is verified by meeting technical specifications and compliance with relevant safety and performance standards. The differences in frequency response and lead type for the proposed devices compared to the predicate were addressed by conducting relative standard tests to demonstrate compliance.

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

Not applicable/Not provided within the scope of this document. This submission focuses on technical specifications and substantial equivalence, not a clinical study with a patient test set for diagnostic algorithm performance.

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

Not applicable/Not provided. No diagnostic algorithm performance study is described.

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

Not applicable/Not provided. No diagnostic algorithm performance study 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

No. This document pertains to the hardware and basic signal acquisition capabilities of an electrocardiograph, not an AI-assisted diagnostic system.

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

No. This document describes the ECG acquisition device itself, not a standalone diagnostic algorithm.

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

Not applicable/Not provided. The "ground truth" here is compliance with engineering standards and technical specifications shown via bench testing.

8. The sample size for the training set

Not applicable/Not provided. No machine learning algorithm is described.

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

Not applicable/Not provided. No machine learning algorithm is described.

In summary: This 510(k) submission is for a traditional Digital Electrocardiograph hardware device. The "acceptance criteria" and "study" described are focused on engineering specifications, electrical safety, and electromagnetic compatibility (EMC) through bench testing, to demonstrate substantial equivalence to a predicate device. It does not involve a clinical study to evaluate diagnostic performance of an AI algorithm.

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