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Diagnostic evaluation of patients with asymptomatic and symptomatic disturbances of the cardiac rhythm such as:

• Dizziness
• Heart race
• Palpitations
• Syncopes of unknown cause

The 3100 Series device is single-channel looping cardiac event recorder for transmitting multiple ECG recordings via land-line or GSM telephony networks to a compatible ECG receiving system, such as "sensor mobile" REMOS ECG Receiving Software (510(k) K050670) or compatible standard acoustic ECG receivers. The 3100 series device is intended for auto-triggered and patient activated event recordings (Bradycardia, Tachycardia and Atrial Fibrillation). It is battery driven and utilizes a loop-memory to capture ECG data with an adjustable pre- and post-event time.

This 510(k) submission for the TMS Telemedizinische Systeme GmbH Loop-Recorder vitaphone 3100 Series does not appear to contain a study specifically demonstrating the device meets acceptance criteria in the manner typically seen for complex AI/ML-based diagnostic devices. Instead, the submission focuses on demonstrating substantial equivalence to predicate devices, which is common for hardware devices with established product codes.

Based on the provided documents, here's an analysis of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not explicitly stated in the provided 510(k) summary. For devices like the vitaphone 3100 Series (a cardiac event recorder), acceptance criteria would typically revolve around:

• ECG Signal Quality: Ability to accurately capture and store ECG signals.
• Event Detection Accuracy: For auto-triggered events (Bradycardia, Tachycardia, Atrial Fibrillation), the accuracy of the device in identifying and logging these events.
• Loop Memory Functionality: Correct operation of the pre- and post-event loop recording.
• Data Transmission Reliability: Successful and accurate transmission of ECG data via land-line or GSM.
• Battery Life: Meeting specified operational hours.
• Safety Standards: Compliance with electrical safety and EMC standards.

The 510(k) summary only states: "The technical specification comparison reveals no substantial differerence between the 3100 Series device and the predicate devices and no differences which affect safety or efficacy." This implies that the device is expected to perform comparably to the predicate devices, which are already on the market and presumably meet established performance standards for cardiac event recorders.

To provide a placeholder, a potential table might look like this (hypothetical, as actual criteria are not given):

2. Sample Size Used for the Test Set and Data Provenance

This information is not provided in the given 510(k) summary. For a hardware device, "test set" might refer to device-level testing (e.g., bench testing, electrical tests, performance verification) rather than a clinical dataset in the way it's used for AI/ML devices. There is no mention of patient data (retrospective or prospective) being used to prove specific performance metrics.

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

This information is not applicable and not provided. The device is a "single-channel looping cardiac event recorder" which captures raw ECG data. The interpretation of this data would typically be done by clinicians (cardiologists, electrophysiologists) after the data is transmitted, not by the device itself establishing "ground truth" for diagnostic purposes. The 510(k) emphasizes the device's role in transmitting recordings for diagnostic evaluation, not performing the diagnosis itself.

4. Adjudication Method for the Test Set

This information is not applicable and not provided. As there's no mention of a human-adjudicated test set, there's no adjudication method described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not done and is not mentioned. Such studies are typically performed for AI/ML diagnostic tools to assess how the AI impacts human reader performance. This device is a hardware recorder, not an AI diagnostic tool.

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

No, a standalone study in the context of an algorithm's diagnostic performance was not done. The device itself has internal algorithms for auto-triggering based on detected heart rate deviations or fibrillation patterns, but the 510(k) focuses on the device's equivalence to predicates for its core function as a recorder and transmitter. Standalone performance for a diagnostic algorithm is not the focus here.

7. The Type of Ground Truth Used

This is largely not applicable in the context of clinical ground truth (e.g., pathology, outcomes data) for diagnosis provided by the device. The "ground truth" for this device's functionality would be based on engineering specifications, electrical performance tests, and comparison to the known operational characteristics of predicate devices. For its event detection capabilities, the ground truth would inherently be based on expert cardiology interpretation of the ECGs it records. However, the 510(k) does not describe a study where the device's event detection was compared against expert consensus or pathology.

8. The Sample Size for the Training Set

This is not applicable and not provided. This device is a hardware medical device with embedded firmware/algorithms, but there is no indication of it being an AI/ML device that underwent a "training" process with a large dataset in the modern sense. Its event detection logic would be based on established physiological thresholds and patterns, manually programmed, not learned via machine learning from a training set.

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

This is not applicable and not provided for the reasons stated above (not an AI/ML device in the context of a training set).

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