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The eMotion Faros ECG sensor is a wearable, externally applied, electrocardiograph recorder and transmitter for the purpose of health monitoring, biofeedback and scientific research.

The eMotion Faros ECG Mobile is intended for use in clinical settings to collect and transmit health parameters to healthcare professionals for monitoring and evaluation.

Health parameters are collected from a variety of commercially available, external plug-in devices such as ECG sensors, Weight Scales, Blood Pressure Meters and Pulse Oximeters.

Indicated for adult patients who require clinical ECG monitoring in healthcare facility environment under supervision of a physician or prescript by the supervising physician to supplement data acquisition in home environment.

The eMotion Faros ECG Mobile does not provide any automatic analysis or diagnosis.

The eMotion Faros ECG Mobile is a mobile device. PC and Internet based telemetry solution for the ambulant monitoring of the pluq-in device data of chronic patients via a mobile network. Pluq-in devices can be ECG devices, blood pressure monitors, weighing scales, etc. The device reads data from the plug-in device via a Bluetooth connection. The mobile device sends the data to a server (Health Gateway) over mobile networks using the secured connection.

Data can be viewed from the Health Gateway server using the Virtual Clinic interface. Monitoring is performed using PC application that reads data from the server over the internet using the secured connection. ECG data is possible to record on eMotion Faros ECG sensors internal memory. Data from ECG sensors can be reviewed with eMotion EDF Viewer or uploaded via USB connection.

Data acquisition is performed using eMotion Faros ECG sensors and applicable plug-in devices. Faros ECG sensors are modified versions of eMotion ECG sensor which the FDA granted clearance on November 26, 2013 (K131699).

The provided text does not contain detailed acceptance criteria or a specific study that proves the device meets such criteria in the format typically found for AI/ML device evaluations. This submission primarily focuses on establishing substantial equivalence to a predicate device, as opposed to demonstrating specific performance metrics for an AI component.

However, based on the Performance Data Summary section and general principles of device clearance, we can infer some aspects and highlight what information is not present that would typically be in an AI/ML device submission.

Inferred Acceptance Criteria and Reported Device Performance (Based on "Performance Data Summary"):

Information NOT available in the provided text (and why it's typically relevant for AI/ML devices but not explicitly discussed here):

The provided 510(k) submission is for an ECG recorder and transmitter device that does not provide any automatic analysis or diagnosis. Therefore, it does not involve an AI/ML algorithm that requires performance metrics against ground truth for diagnostic or analytical capabilities. The "study" mentioned here refers to non-clinical testing to ensure the device's technical specifications and safety standards are met, particularly in comparison to a predicate device.

1. Sample sized used for the test set and the data provenance: Not applicable in the context of an AI/ML algorithm's test set. The "test set" here refers to the device itself being tested for compliance with technical standards. If this were an AI/ML device, details about the number of cases/patients, their demographic breakdown, and where the data originated (e.g., specific hospitals, countries, retrospective/prospective collection) would be crucial.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Since the device does not perform analysis or diagnosis, there's no "ground truth" to establish for its output. If this were an AI/ML device, this section would detail the number of clinicians (e.g., cardiologists), their experience levels, and their role in labeling the data.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. No human adjudication is mentioned as the device's function is data collection and transmission, not interpretation. For an AI/ML diagnostic device, this would describe how expert disagreements in ground truth labeling were resolved.

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. The device does not assist human readers in diagnosis. An MRMC study would be critical for an AI/ML device that provides interpretative assistance.

5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable. There is no standalone algorithm for analysis or diagnosis.

6. The type of ground truth used (expert concensus, pathology, outcomes data, etc): Not applicable. There is no ground truth needed for diagnostic accuracy as the device merely records and transmits.

7. The sample size for the training set: Not applicable. There is no AI/ML algorithm being trained by the device.

8. How the ground truth for the training set was established: Not applicable. As there is no training set for an AI/ML algorithm.

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The eMotion ECG sensor is a wearable, portable, externally applied, electrocardiograph recorder and transmitter for the purpose of health monitoring, biofeedback and scientific research. The eMotion ECG Mobile is intended for use in clinical and non-clinical settings to collect and transmit health parameters to healthcare professionals for monitoring and evaluation. Health parameters are collected from a variety of commercially available, external plug-in devices such as ECG sensors, Weight Scales, Blood Pressure Meters and Pulse Oximeters. The eMotion ECG Mobile does not provide any automatic analysis or diagnosis.

The eMotion ECG Mobile is a mobile device, PC and Internet based telemetry solution for the ambulant monitoring of the plug-in device data of chronic patients via a mobile network. Plug-in devices can be ECG devices, blood pressure monitors, weighing scales, etc. The device reads data from the plug-in device via a Bluetooth connection. The application in the mobile device sends the data to a server (Health Gateway) over mobile networks using the secured connection. Data can be viewed from the Health Gateway server using the Web Monitor. Monitoring is performed using PC application that reads data from the server over the internet using the secured connection.

The Mega Electronics Ltd eMotion ECG Mobile is a device designed for the ambulatory monitoring of chronic patients by collecting and transmitting health parameters from plug-in devices (e.g., ECG sensors, blood pressure monitors, weighing scales) to healthcare professionals. The device itself does not provide automatic analysis or diagnosis.

Here's an analysis of its acceptance criteria and the study that proves it meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The Study Proving Acceptance Criteria are Met:

The device's performance was evaluated through non-clinical performance data (laboratory testing and verification/validation processes).

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

• Sample Size: The document does not specify a distinct "test set" in terms of patient data or clinical cases. Instead, the testing appears to be based on a single device or a set of devices undergoing various laboratory-based performance and safety tests.
• Data Provenance: The data is generated from non-clinical performance testing conducted by the manufacturer, Mega Electronics Ltd., in Finland. This is prospective data generation from device testing, not retrospective clinical data.

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

• This information is not provided as the study is a non-clinical device performance and safety verification, not a clinical study involving expert interpretation of patient data. The "ground truth" here is adherence to engineering specifications and recognized consensus standards (e.g., EN 60601-2-25:1999).

4. Adjudication Method for the Test Set:

• This is not applicable as there was no clinical test set requiring expert adjudication. The "adjudication" in this context would be the pass/fail determination based on predefined engineering and safety specifications within the written test protocols.

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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study was not performed.
• The device explicitly states: "The eMotion ECG Mobile does not provide any automatic analysis or diagnosis." Therefore, there is no AI component providing interpretations that would be compared with or assisting human readers. The device serves solely as a data collection and transmission tool.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study:

• Yes, in a sense, a standalone performance evaluation was conducted for the device's technical specifications. However, it's critical to reiterate that the "eMotion ECG Mobile does not provide any automatic analysis or diagnosis." Therefore, this standalone performance refers to the accuracy and reliability of its data acquisition and transmission capabilities, not the performance of an AI algorithm in interpreting medical data. The performance tests (e.g., A/D conversion, sampling rate, noise, CMRR, waveform shape) represent the standalone technical performance of the device's hardware and embedded software for data capture.

7. Type of Ground Truth Used:

• The ground truth used for the non-clinical performance data is based on engineering specifications, recognized consensus standards (e.g., EN 60601-2-25:1999), and pre-defined pass/fail criteria established in written test protocols. For the connectivity with plug-in devices, the ground truth was "proper functioning" and successful data transmission with those specific commercial devices.

8. Sample Size for the Training Set:

• This is not applicable. The eMotion ECG Mobile does not employ machine learning or AI algorithms that would require a training set for diagnostic or analytical purposes. It is a data collection and transmission device.

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

• This is not applicable as there is no training set for an AI algorithm.

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The FemiScan™ devices are intended as an aid for exercising and training pelvic floor muscle activation, control and optionally coordination with the abdominal muscle group. It is indicated in the treatment of stress and mixed female urinary incontinence under the supervision of a physician. The FemiScan™ Clinic System is used in the physician office, clinic, or hospital environment. The FemiScan™ Personal System is used to supplement these exercises in the home environment.

The FemiScan™ Clinic and Personal Systems consist of a palmsized measuring unit with a computer-based user interface and data acquisition system for data collection and data transfer. The devices feature a 2 channel EMG vaginal probe.

Here's an analysis of the provided text regarding the FemiScan™ Clinic System and FemiScan™ Personal System, focusing on acceptance criteria and supporting studies:

Based on the provided K993411 document, the information regarding study details for establishing performance criteria is limited. The document focuses more on substantial equivalence to predicate devices and general safety/performance testing.

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state specific quantitative acceptance criteria or detailed reported device performance in a numerical format. It broadly states that the device was subjected to safety and performance tests to ensure compliance with applicable recognized standards and to establish performance and reliability characteristics.

Therefore, a table of specific acceptance criteria and reported device performance cannot be created from the given text.

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

• Test Set Sample Size: The document mentions a "user study conducted under the direction of Kuopio University Hospital" for evaluating the FemiScan™ Personal System for suitability as a prescription home use device. However, the specific sample size for this user study is not provided.
• Data Provenance:
  • Country of Origin: Kuopio University Hospital is located in Finland.
  • Retrospective or Prospective: The description "user study conducted" suggests a prospective study design.

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

The document does not specify the number of experts used to establish ground truth for the user study or their qualifications. The study was conducted under the "direction of Kuopio University Hospital," implying medical oversight, but details about individual expert contributions are absent.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for the user study.

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

The document does not mention or describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study to evaluate how human readers improve with AI vs. without AI assistance. The FemiScan™ is a biofeedback monitoring device, not an imaging interpretation or diagnostic AI device in the typical sense that would necessitate such a study design.

6. Standalone (Algorithm Only) Performance Study

The document does not explicitly describe a standalone (algorithm only without human-in-the-loop) performance study in terms of diagnostic accuracy or similar metrics. While "system level tests, integration tests, data calculations, display results" were conducted, these describe internal validation steps rather than an independent performance study of the FemiScan™'s biofeedback algorithm against predetermined clinical outcomes or a gold standard without human interaction. The description of the user study implies human interaction for home use.

7. Type of Ground Truth Used

For the "user study conducted under the direction of Kuopio University Hospital," the ground truth implicitly relates to the suitability and safety for home use, and potentially the effectiveness of the device in aiding "exercising and training pelvic floor muscle activation and control" and "treatment of stress and mixed female urinary incontinence." The document notes "No adverse events occurred during the study," which is a safety outcome. Specific objective clinical outcomes or a "gold standard" for the effectiveness of training are not detailed as ground truth.

8. Sample Size for the Training Set

The document makes no mention of a training set or any machine learning/AI development process that would typically involve a separate training set. The FemiScan™ operates on electromyography (EMG) monitoring technology and biofeedback principles, which are well-established, rather than relying on a complex AI model trained on large datasets.

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

Since there is no mention of a training set, there is no information on how its ground truth would have been established.

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The intended use of the Model ME3000P Professional Muscle Tester is to record EMG sensory feedback signals to be used in various programs of muscle re-education and relaxation training.

The ME3000P is a portable, battery powered EMG recorder. The ME3000P is an electronic device used to gather and display electrophysiological signals obtained from muscle activity. These signals are used in programs related to muscle re-education and relaxation training. The signals are received via conductive adhesive skin electrodes and may be displayed or stored for display at a later time. Signals may be gathered from two or more channels. Data downloading or transfer is via a standard fiber optic cable to a PC. The product does not relay any electrical or stimulatory signals to the body. It is only a signal recorder with display capabilities.

The provided text describes the ME3000P Professional Muscle Tester, an EMG device. However, it does not include information about acceptance criteria or a study that proves the device meets specific acceptance criteria in the way a modern AI/ML device submission would. This document is from 1997, and the regulatory requirements and practices for device testing, especially regarding performance criteria and clinical study methodologies, were different then.

Based on the provided text, here's what can be extracted and what is explicitly not available:

1. A table of acceptance criteria and the reported device performance

Information NOT available in the text for this section:

• Quantitative performance metrics (e.g., accuracy, sensitivity, specificity, signal-to-noise ratio specific to EMG signals).
• Specific numerical thresholds for "acceptance criteria" related to device performance in signal acquisition or display.

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

• Sample Size: Not specified. The "Testing" section only mentions "The ME300P was tested for electrical and EMC safety." There is no mention of a human or patient test set for performance evaluation in the context of its intended use to record EMG signals for muscle re-education and relaxation training.
• Data Provenance: Not applicable, as no test set of human data is described. The safety tests would have been performed on the device itself, likely in a laboratory setting.

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

• Not applicable. No ground truth establishment for a test set of clinical data is described.

4. Adjudication method for the test set

• Not applicable. No 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

• No. This device is a biofeedback device, not an AI-assisted diagnostic or therapeutic tool. Therefore, an MRMC study comparing human readers with and without AI assistance is not described and would not be relevant.

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

• No. The device described, the ME3000P, is a signal recorder and display unit for EMG signals. It is explicitly stated that "The product does not relay any electrical or stimulatory signals to the body. It is only a signal recorder with display capabilities." Its function is to provide raw data to a human user for re-education and training, not to perform an automated standalone diagnostic or therapeutic function.

7. The type of ground truth used

• Not applicable for the EMG signal recording performance, as no clinical performance study is described. For the safety tests, the "ground truth" would be the specifications and requirements of the relevant IEC and EMC standards.

8. The sample size for the training set

• Not applicable. This device is an electronic signal acquisition and display device, not an AI/ML model that requires a training set.

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

• Not applicable, as no training set is relevant for this type of device.

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