(238 days)
Digital Electrocardiographs, ECG-1210 / ECG-1230 / ECG-3010 / ECG-6010, 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, ECG-1210 / ECG-1230 / ECG-3010 / ECG-6010, are designed to acquire, display and record ECG signals from patient body surface by ECG electrodes. After been amplified, filtered, the ECG signals waveforms are displayed in the LCD and recorded in the paper through thermal printer. ECG data and patient information could be stored in the memory of the device. All the models, ECG-1210 / ECG-1230 / ECG-3010 / ECG-6010, of the proposed device, Digital Electrocardiographs, follow the same design principle and same technical specifications: They consist of four modules, which are power supply module, signal collection module, amplification module, and control module.
The provided text describes a 510(k) premarket notification for Digital Electrocardiographs (ECG-1210 / ECG-1230 / ECG-3010 / ECG-6010). However, the document does NOT contain the detailed information required to answer all the questions about acceptance criteria and the specific study proving the device meets them. The text primarily focuses on the regulatory submission, intended use, device description, and the determination of substantial equivalence to a predicate device.
Here's what can be extracted and what is missing:
1. A table of acceptance criteria and the reported device performance
The document states: "Performance testing was conducted to validate and verify that the proposed devices met all design specifications and was substantially equivalent to the predicate device." However, it does not provide the specific acceptance criteria (e.g., accuracy, sensitivity, specificity for specific ECG features) or the reported performance metrics.
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
This information is not provided in the document.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
This information is not provided in the document.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
This information is not provided in the document.
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
There is no indication that an MRMC comparative effectiveness study involving AI or human reader improvement was conducted. The device is a Digital Electrocardiograph, which is a signal acquisition and display device, not an AI-powered diagnostic tool. The document focuses on hardware performance and substantial equivalence to a predicate ECG device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device itself is a standalone electrocardiograph. The "algorithm" in this context refers to the device's ability to acquire, amplify, filter, display, and record ECG signals. The performance testing would inherently be assessing the device's standalone functionality. However, specific performance metrics for this standalone functionality are not detailed. It's not an AI algorithm in the common sense of the term for this type of device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
This information is not provided in the document. For an ECG device, ground truth for performance testing would typically involve a reference standard for signal accuracy, frequency response, common mode rejection, etc., which might be established by calibrated equipment or known physiological signals.
8. The sample size for the training set
This is not applicable as the device is not described as using a machine learning model that requires a training set. It's a standard medical device for signal acquisition.
9. How the ground truth for the training set was established
This is not applicable for the same reason as point 8.
Summary of available information:
The document confirms that performance testing was conducted to ensure the device met design specifications and was substantially equivalent to the predicate device (Smart ECG (SE) Series Electrocardiograph, SE-12, K091513). However, it lacks the specific details of these tests, including acceptance criteria, performance results, sample sizes, and how ground truth was established, which would typically be found in a more detailed study report, not a 510(k) summary.
§ 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).