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The Cardio WiFi Electrocardiograph is a small portable digital cardiograph intended for use by medical professionals to acquire 12-lead ECG's from single or multiple patients in a clinical or point of care setting. ECG data is first recorded to a secure digital flashcard and then transferred to an ECG management system for review by a physician or other qualified professional.

The Cardio WiFi electrocardiograph continuously records data to a flashcard and displays it at high resolution. It features a large, liquid crystal display ("LCD") that allows a medical professional to periodically check the test subject's full, 12-lead ECG, either at intervals or in the event of a patient symptom. The device is portable and is battery-powered. Data is stored for later review at an ECG review station. Data transfer is accomplished via WLAN 802.11 b or g.

This device, the Datrix Cardio WiFi Electrocardiograph, is a portable electrocardiograph without analysis capabilities. As such, the submission primarily focuses on non-clinical testing to demonstrate substantial equivalence to a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance:

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

This submission did not involve a clinical study with a "test set" in the traditional sense of evaluating diagnostic accuracy or a human-in-the-loop performance. Instead, the evaluation was based on non-clinical testing to established international and national standards for electrocardiographs. Therefore, there is no sample size of patient data or provenance information to report.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Their Qualifications:

Not applicable. The ground truth for this type of device (a data acquisition device without analysis) is adherence to established engineering and performance standards, not diagnostic accuracy. Therefore, no experts were used to establish ground truth from patient data.

4. Adjudication Method for the Test Set:

Not applicable. There was no clinical test set requiring adjudication of patient data.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance:

Not applicable. This device is a basic electrocardiograph without analysis, meaning it does not incorporate Artificial Intelligence (AI) for interpretation or diagnostic assistance. Therefore, no MRMC study was conducted to evaluate human reader improvement with or without AI.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

Not applicable. This device does not have an "algorithm" for diagnostic interpretation in the sense of AI. It acquires and stores ECG data. The performance evaluation was based on the device's ability to accurately and safely perform its intended function of acquiring ECG data, not on the performance of a standalone algorithm for diagnosis.

7. The Type of Ground Truth Used:

The ground truth used for this submission was based on established engineering and performance standards for electrocardiographs, specifically:

• AAMI EC11
• IEC 60601-1
• IEC 60601-1-2
• IEC 60601-2-25
• Internal quality system test procedures for performance, functionality, and reliability.

8. The Sample Size for the Training Set:

Not applicable. This device does not use machine learning or AI, and therefore, there is no "training set" of data.

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

Not applicable as there is no training set.

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The CardioServer ECG Management System software is intended to be marketed to medical professionals and for point-of-care use. The software is designed to provide a database used through out the medical community to store, display, edit and print high resolution ECG data received from devices such as electrocardiographs.

The CardioServer ECG Management System software allows medical professionals responsible for the diagnosis and treatment of patients (adult and pediatric) with heart disease to: review and edit specific patient ECG data including intervals such as QT measurements and algorithm generated preliminary interpretative statements. ECG records are all associated by patient ID and other demographic data. Secure access to the database is provided.

CardioServer ECG Management System is a software only ECG management database that stores, displays, and prints high resolution ECG data transferred from a Datrix Cardio WiFi electrocardiograph device. All ECG records are associated by patient ID, and a final record including physician interpretation can be created.

The software system analyzes data using an ECG analysis algorithm developed under direction of Dr. Peter MacFarlane, University of Glasgow (note: the same algorithm is contained in the predicate device to which equivalency is being claimed). The ECG display is able to show 1, 3, or 12 leads at once, full disclosure, user-selected strips, and interpretations editable bv physician. Hardware requirements: are Windows 2000 or 2003 Server operating system; Pentium IV, 2GHz (minimum); 512 MB RAM (minimum); 10/100 Ethernet (minimum); RAID 5 storage; 1024x768 monitor; and standard back-up technology.

The provided text describes a 510(k) summary for the Datrix CardioServer ECG Management System. However, it does not contain specific acceptance criteria, a detailed study proving the device meets criteria, or the types of quantitative performance metrics typically associated with AI/ML device evaluations.

Instead, the submission focuses on:

• Establishing substantial equivalence to a predicate device (Quinton Pyramis ECG Management System K032038).
• Describing the software's functionality, intended use, and hardware requirements.
• Stating compliance with the FDA Guidance Document: "General Principles of Software Validation."
• Highlighting that the device utilizes the same ECG analysis algorithm (University of Glasgow ECG Algorithm) as the predicate device.

Given this, I cannot provide the requested information in the format specified because the document does not contain it. The provided text primarily addresses regulatory compliance through substantial equivalence, general software validation, and technological comparison to a predicate device. It lacks the details of a performance study with specific acceptance criteria and outcome metrics for algorithmic performance.

Here's what I can extract based on the provided text, and where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Test Set Sample Size: Not specified.
• Data Provenance: Not specified. The document mentions the University of Glasgow ECG Algorithm, implying its origin, but does not detail a specific dataset used for this device's testing or validation of the algorithm's performance.

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

• Not specified. The document does not describe a performance study involving expert-established ground truth. Interpreted statements are "editable by physician," but this pertains to clinical use, not the validation process.

4. Adjudication method for the test set:

• Not specified.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size:

• No, an MRMC study was not described. The document does not mention any studies involving human readers or comparative effectiveness with AI assistance.

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

• A standalone performance study of the algorithm's diagnostic accuracy, as typically reported today, is not detailed. The document states: "The software system analyzes data using an ECG analysis algorithm developed under direction of Dr. Peter MacFarlane, University of Glasgow (note: the same algorithm is contained in the predicate device to which equivalency is being claimed)." This indicates the algorithm itself has a history, but a specific standalone validation study for this device is not provided in terms of performance metrics. The testing focused on software validation and functionality.

7. The type of ground truth used:

• Not specified. For the algorithm (University of Glasgow ECG Algorithm), it is generally understood that such algorithms are developed and validated against expert consensus or clinically confirmed diagnoses, but the details for this submission are absent.

8. The sample size for the training set:

• Not specified. This refers to the training of the University of Glasgow ECG Algorithm, which is an external component, and its training details are not provided in this 510(k) summary.

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

• Not specified. (Refer to point 8).

Summary of Missing Information:

The provided 510(k) summary is primarily concerned with establishing substantial equivalence to a predicate device and demonstrating software validation according to FDA guidance. It leverages the fact that it uses the same ECG analysis algorithm as the predicate device. It does not contain the detailed performance study data (acceptance criteria, test set characteristics, ground truth establishment, expert involvement, specific performance metrics for diagnostic accuracy) that would be expected for a more modern AI/ML device submission demonstrating algorithmic performance.

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The Datrix E-Tac EX-1000 Electrocardiographic Event Recorder device is intended for long-term monitoring of ambulatory cardiac patients who experience intermittent symptoms associated with cardiac arrhythmia. Upon activation by the patient, ECG data are stored for future transmission via telephone to a receiving station. Data transmission is initiated by the patient and confirmed by the receiving station. Once data are transmitted, they are immediately available for review solely by a physician or other qualified medical professionals.

The Datrix E-Tac EX-1000 (here simply: EX-1000) Electrocardiographic [ECG] Event Recorder is intended for long-term monitoring of ambulatory cardiac patients who experience intermittent symptoms associated with cardiac arrhythmia. Lightweight and compact, the EX-1000 is designed to be as non-intrusive as possible to the patient, and can operate up for 30 days on two AAA alkaline batteries. The patient's ECG data are acquired via patient leadwires (two-lead, one-channel). At the onset of an event, the patient presses the [Record] button to store his or her ECG data in the recorder's flash memory. Events are recorded according to one of four user-selectable memory configurations. Up to two events may be recorded before transmission of the data to a compatible receiving station is required. The patient initiates data transmission via telephone by removing the patient leadwires and pressing the [Send] button, upon which the stored data are transmitted. A physician or other qualified medical professional reviews the transmitted data. Feedback on the EX-1000 recorder status is provided to the user (technician and/or patient) via a multi-colored LED and audible tones.

Here's an analysis of the Datrix E-Tac EX-1000 Electrocardiographic Event Recorder's acceptance criteria and the study proving it meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Datrix E-Tac EX-1000 are implicitly defined by its compliance with recognized performance standards and its substantial equivalence to predicate devices, particularly regarding amplitude and timing (frequency) reproduction of ECG signals.

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

• Sample Size: A total of 24 in-house simulated events were recorded. These 24 events were then transmitted twice each (2 x 24 = 48 transmissions) to two different receiving stations.
• Data Provenance: The data was in-house simulated, meaning it was generated under controlled laboratory conditions rather than collected from actual patients.

3. Number of Experts and Qualifications for Ground Truth

• The document does not explicitly state that human experts were used to establish ground truth for this performance testing.
• Instead, the ground truth was based on "known input signals." This implies that the amplitude and frequency characteristics of the signals fed into the devices were precisely defined and known beforehand due to their simulated nature.

4. Adjudication Method for the Test Set

• There was no explicit adjudication method described for the test set in the traditional sense (e.g., 2+1, 3+1).
• Since the study used "known input signals" as the ground truth, the comparison was directly between the device's output and these pre-defined, objective reference signals. Measurements were taken from printouts, implying direct quantitative comparison rather than a qualitative human assessment requiring adjudication.

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

• No MRMC comparative effectiveness study was done. This study focused on the technical performance of the device itself (measurement accuracy) rather than its impact on human reader performance or diagnosis. The ECG data transmission is for a physician or qualified medical professional to review, but the study did not evaluate human interpretation.

6. Standalone (Algorithm Only) Performance

• Yes, standalone performance was evaluated. The study measured the ability of the EX-1000 (and predicate devices) to accurately reproduce amplitude and timing/frequency of known input signals without human interaction in the measurement process (beyond setting up the simulation and taking measurements from printouts). The device's firmware handles the acquisition, storage, and transmission of data autonomously. The comparison was device-to-predicate and device-to-ground truth (known input signals).

7. Type of Ground Truth Used

• The ground truth used was based on precisely controlled and "known input signals." This is a form of objective, engineered ground truth where the exact characteristics (amplitude and frequency) of the test signals are defined prior to testing.

8. Sample Size for the Training Set

• The document does not mention a training set in the context of machine learning or AI. This device appears to be a hardware-based event recorder with firmware, not a learning algorithm. The "firmware" was designed, documented, and validated according to design controls, implying traditional software engineering development and testing, not a dataset-driven training process.

9. How Ground Truth for the Training Set Was Established

• As there was no stated training set for a machine learning model, this question is not applicable. The device's functionality is based on its direct electronic processing of ECG signals according to its engineering design and firmware instructions.

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The VX3 digital Holter recorder is intended for the recording of ECG data collected from ambulatory patients. The recorder can collect data in the presence of implanted pacemaker pulses, and can detect and record the occurrence of signals characteristic of pacemaker pulses. The recorder is used under the order of a physician, who reviews the data after downloading and processing by a Holter playback system. The physician determines the presence of normal and abnormal ECG data as well as pacemaker pulses during the events of the patient's daily activity.

The VX3 is a lightweight, compact, digital Holter recorder designed for the recording of ECG data collected from ambulatory patients. A derivative of the Datrix DR512 digital Holter recorder, the VX3 has enhanced features, including an LCD to verify leadwire hookup and display recorder status and error messages, optional keypad for selection of various options, and optional pacemaker pulse detection. Various channel and lead configurations are accommodated by using the appropriate leadwire set without additional recorder reconfiguration. Data are recorded on industry standard compact flashcards for subsequent download and to a Holter playback system. Sampling rates are factory programmable to accommodate compatibility with various OEM Holter playback systems.

The provided document is a 510(k) Premarket Notification Summary for the Datrix VX3 Series Digital Holter Recorder. This type of submission is for demonstrating substantial equivalence to a predicate device, not typically for reporting on a new performance study with detailed acceptance criteria and expert reviews of an AI/algorithm-based device.

Therefore, the document does not contain the information requested in the prompt, such as detailed acceptance criteria for an AI device, sample sizes for test sets, data provenance, number or qualifications of experts, adjudication methods, MRMC study results, standalone performance, type of ground truth used, or details about the training set for an algorithm.

The document primarily focuses on:

• Device Description: The VX3 is a digital Holter recorder for ECG data collection from ambulatory patients, with features like an LCD, optional keypad, and optional pacemaker pulse detection.
• Intended Use: Recording ECG data and pacemaker pulses for physician review after processing by a Holter playback system.
• Predicate Device Comparison: A table comparing the Datrix VX3 to the Datrix DR512 and Braemar DXP1000 across functional, physical, environmental, and electrical specifications. This comparison is used to establish substantial equivalence, meaning the safety and effectiveness of the new device are similar to legally marketed devices.

Conclusion: The provided text describes a medical device's technical specifications and its comparison to predicate devices for regulatory clearance, but it does not detail acceptance criteria or a study proving device performance in the context of an AI/algorithm-based system as requested.

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The DR512 digital Holter recorder is intended for the recording and storage of long-term ECG data collected from ambulatory patients. The recorder is used under the order of a physician, who reviews the data after downloading and processing by a Holter playback system. The physician determines the presence of normal and abnormal ECG data during the events of the patient's daily activity.

The DR512 is a lightweight, compact, digital Holter recorder designed for ambulatory ECG applications. Three channels of ECG data are collected via a 5 or 7 lead wire set and silver-chloride electrodes. The recorder is battery powered to ensure electrical isolation of enclosure coated with an electromagnetic interference spray. The DR512 has internal calibration, and a patient activated event marker button. ECG data are collected for 24 hours.

Here's a breakdown of the acceptance criteria and the study information for the DATRIX DR512 Digital ECG Holter Recorder, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not explicitly list "acceptance criteria" for the device, but rather describes a series of comprehensive tests performed to ensure the device met its "design specifications." Therefore, the "acceptance criteria" below are inferred from the tests conducted, and the "reported device performance" is a summary of the general findings.

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

• Sample Size for Test Set: The document does not explicitly state the number of subjects or the specific sample size used for the "subject testing" mentioned. It only refers generally to "subject testing."
• Data Provenance: The document does not specify the country of origin of the data. It mentions "subject testing," which is generally prospective, but no further details are provided.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• This information is not provided in the document. The study focuses on the technical performance and accuracy of the device itself rather than its diagnostic interpretation by humans.

4. Adjudication Method for the Test Set

• This information is not applicable/not provided. The study tested the recorder's technical performance and data recording capabilities, not direct diagnostic output requiring adjudication of human-interpreted results.

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

• No, an MRMC comparative effectiveness study was not done. The submission describes testing the technical specifications and functionality of the recorder, not evaluating the diagnostic performance of human readers with or without AI assistance.

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

• Yes, in effect, a standalone study was performed in terms of the device's technical functionality. The testing detailed (analog and digital hardware verification, firmware testing, compression algorithms, environmental tests) assessed the device's performance purely as a recording instrument, independent of human interpretation or a "human-in-the-loop" for its operation. The device's primary function is to record and store data, which is then processed by a separate "Holter playback system" and reviewed by a physician. The study confirmed the accuracy of this recording and storage.

7. The Type of Ground Truth Used

• The "ground truth" for the various tests was primarily based on:
  • "Design specifications": The device's performance was measured against its intended technical specifications.
  • Known input signals: For example, compression algorithms were tested with "line segment data, calibration signals, and simulated ECG waveforms," where the expected output would be known.
  • Expected functional responses: For tests like event button circuits, battery polarity, RAM write-read, and environmental tests, the "ground truth" was whether the device performed as designed and expected under specific conditions.
  • Compatibility with existing systems: For playback systems, the ground truth was successful data transfer and processing.

8. The Sample Size for the Training Set

• This information is not provided and is likely not applicable in the context of this device. The DR512 is a hardware recorder with firmware, not a machine learning or AI algorithm that requires a "training set" in the typical sense for diagnostic purposes. The firmware and algorithms (e.g., compression) would have been developed and tested against engineering specifications and simulated data during design, rather than "trained" on a dataset.

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

• As a "training set" in the machine learning context is not applicable, the ground truth for any internal development or testing of firmware/algorithms was established based on engineering specifications, mathematical models, and known characteristics of ECG signals and data compression techniques.

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