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Sleep Profiler is intended for use for the diagnostic evaluation by a physician to assess sleep quality and score sleep disordered breathing events in adults only. The Sleep Profiler is a software-only device to be used under the supervision of a clinician to analyze physiological signals and automatically score sleep study results; including the staging of sleep, detection of arousals, snoring and sleep disordered breathing events (obstructive apneas, hypopneas and respiratory event related arousals). Central and mixed apneas can be manually marked within the records.

The Sleep Profiler is a software application that analyzes previously recorded physiological signals obtained during sleep. The Sleep Profiler software can analyze any EDF files acquired with the Advanced Brain Monitoring X4 System and the X8 System models SP40 and SP29. Automated algorithms are applied to the raw signals in order to derive additional signals and interpret the raw and derived signal information. The software automates recognition of: Sleep stages Rapid Eye Movement (REM) and nREM (N1, N2, N3) and wake, Heart/pulse rate, Snoring loudness, Sleep/wake, Head movement and position, Snoring, sympathetic, behavioral and cortical arousals, ECG,EOG, EMG waveform, SpO2, Airflow, Respiratory Effort, Apneas and Hypopneas, Oxygen desaturations. The software identifies and rejects periods with poor electroencephalography signal quality. The full disclosure recording of derived signals and automated analyses can be visually inspected and edited prior to the results being integrated into a sleep study report. Medical and history information can be input from a questionnaire. Responses are analyzed to provide a pre-test probability of Obstructive Sleep Apnea (OSA). The automated analyses of physiological data are integrated with the questionnaire data, medical and history information to provide a comprehensive report. Several report formats are available depending on whether the user has acquired more than one night of data, wishes to obtain a narrative summary report or provide patient reports. The Sleep Profiler software can be used as a stand-alone application for use on Microsoft Windows 7 & 8 operating system platforms (desktop model). Alternatively, the user interface (i.e., entry or editing of information) can be delivered via a web-portal (portal model). The capability to enter or edit patient information, call the application to generate a study report, and/or download a report is provided using either the desktop PC application or web-portal application. The same analysis and report generation software is used for both the desktop and web-portal applications.

Here's a breakdown of the acceptance criteria and study information for the Sleep Profiler device, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Sizes and Data Provenance for Test Set

• Sample Size (for AHI/OSA detection): 60 subjects for overall AHI, 40 subjects for REM AHI.
• Sample Size (for Sleep Staging): A subset of 43 subjects from the AHI study, with at least 4 hours of raw X8 diagnostic recording time.
• Data Provenance: The document states "signals acquired with the X8 System concurrent to polysomnography (PSG)." This implies the data was prospectively collected for this evaluation, likely from a clinical setting, but the country of origin is not specified.

3. Number of Experts and Qualifications for Ground Truth (Test Set)

• Number of Experts:
  • For AHI/OSA detection: "one rater per study" for PSG results.
  • For Sleep Staging: "weighted majority agreement of three raters" for the 43-subject subset.
• Qualifications of Experts: Not explicitly stated beyond being "rater(s)" for PSG and "expert scoring" for sleep staging. Standard practice for such studies would imply board-certified sleep technologists or physicians experienced in PSG scoring.

4. Adjudication Method for the Test Set

• For AHI/OSA detection: "one rater per study." This implies no formal adjudication/consensus process among multiple independent reviewers, as only a single rater's PSG results were used as ground truth for each case.
• For Sleep Staging: "weighted majority agreement of three raters." This indicates an adjudication method where the consensus of three experts established the ground truth.

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

• No MRMC comparative effectiveness study is explicitly described where human readers' performance with and without AI assistance is compared. The study primarily focuses on the standalone performance of the Sleep Profiler software against expert-scored PSG.

6. Standalone Performance Study

• Yes, a standalone study was performed. The "Sleep Profiler software accuracy was clinically validated with signals acquired with the X8 System concurrent to polysomnography (PSG)." The results presented for AHI and sleep staging are for the algorithm's performance without human intervention, compared to human-scored PSG.

7. Type of Ground Truth Used

• Expert Consensus / Human Scoring: The primary ground truth for both AHI detection and sleep staging was derived from Polysomnography (PSG) results scored by human experts/raters. For sleep staging, it was specifically based on the "weighted majority agreement of three raters."

8. Sample Size for the Training Set

• The document does not explicitly state the sample size used for the training set of the Sleep Profiler software. It describes the clinical validation study (test set) but not the development data.

9. How Ground Truth for the Training Set Was Established

• The document does not provide details on how the ground truth for the training set was established. Typically, for such devices, training data would also be derived from expert-scored PSG, similar to the test set, but this information is not included in the 510(k) summary.

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The X8 System is intended for prescription use in the home, healthcare facility, or clinical research environment to acquire, record, transmit, and display physiological signals from adult patients. All X8 models (SP40, SP29, and XS29) acquire, record, transmit, and/or display electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and/or electromyogram (EMG) signals, with optional accelerometer, acoustical, and photoplethysmographic signals. Model SP29 additionally includes a nasal pressure transducer and cannula (for airflow), thoracic and abdomen respiratory effort, and pulse rate and oxyhemoglobin saturation from the finger. The X8 system only acquires and displays physiological signals; no claims are being made for analysis of the acquired signals with respect to the accuracy, precision, and reliability.

The X8 System is indicated for acquiring, recording/storing, transmitting, and displaying physiological data in patients. It can be used by patients in the home, healthcare facility, or clinical research environment. Patients can move within their home or healthcare environment without having to remove the device (e.g. walk to the restroom).

The X8 System is comprised of the X8 device which is worn on the patient's head and body, accessories, the Device Manager software, and the X-Series Basic-Utility Software. The study records are saved to the PC in EDF format and are available for analysis by third party software applications, such as Persyst Reveal (K011397).

The X8 System combines hardware, firmware, and software to acquire physiological signals. It acquires physiological data through a battery powered headset worn by the patient and provides a flexible platform for applying sensors and acquiring signals from multiple locations on the head or body, transmitting and recording the signals, and providing visual and auditory indications to ensure high quality data are obtained.

Model SP40 Sleep Profiler is applied by the patient to acquire and record electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and/or electromyogram (EMG) signals, with optional accelerometer, acoustical, and photoplethysmographic signals during sleep. This model utilizes the X8 Sleep Profiler Strip.

Model SP29 Sleep Profiler PSG2 is applied by the patient to acquire and record electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and/or electromyogram (EMG) signals, with optional accelerometer, acoustical, and photoplethysmographic signals during sleep. This model additionally includes a nasal pressure transducer and cannula (for airflow), thoracic and abdomen respiratory effort, and pulse rate and oxyhemoglobin saturation from the finger. This model utilizes the X8 Sleep Profiler Strip.

Model XS29 X8 Stat is applied by a technician to acquire, record, transmit, and/or display electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and/or electromyogram (EMG) signals, with optional accelerometer, acoustical, and photoplethysmographic signals acquired during non-sleep conditions. This model utilizes either the X8 Midline or the X8 Referential strip.

The Device Manager software application provides a means to communicate with the X8 Device, transfer study data, and format a device. The software transfers data saved in the memory of the X8 device using either a PC application or a web-based data entry application operating in a cloud server environment.

The X-Series Basic-Utility Software acquires, presents, and stores physiological signals from the X8 Device. The software has a modular architecture that allows the users to interact using either the Graphical User Interface (GUI) provided with the installation, or programmatically via a Software Development Kit. Additional functionalities provided by X-Series Basic-Utility Software include impedance measurements, custom markers, and interface with the Bluetooth Receiving Dongle.

Here's a breakdown of the acceptance criteria and study details based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

Device: X8 System (Sleep Profiler (SP40), Sleep Profiler PSG2 (SP29), Stat X8 (XS29))
Study Type: Prospective Comparison and Self-Application Study of X8 System Model SP29

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

• Test Set Sample Size:
  • For comparison of airflow and respiratory effort signals (part A of the study): The specific number of breathing events analyzed is provided (225 for thorax, 232 for abdomen), but the number of subjects contributing to these events is not explicitly stated.
  • For self-application and signal quality (part B of the study): 11 subjects.
  • For audio alerts acceptance criterion: 10 subjects (one subject was excluded from this analysis).
• Data Provenance: The study was described as "prospective," indicating that the data was collected specifically for this study under controlled conditions defined prior to data collection. The document does not specify the country of origin for the data.

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

The document does not explicitly state the number of experts or their qualifications for establishing the ground truth for this comparative study. However, the ground truth for part A (comparison of signals) was implicitly the readings from the predicate device, Compumedics Somte (K072201), an FDA-cleared device. For part B (self-application and signal quality), the ground truth for signal quality likely involved assessment against predefined criteria or by qualified personnel, but this is not detailed.

4. Adjudication Method for the Test Set

The document does not specify an explicit adjudication method (e.g., 2+1, 3+1). For the comparative study (part A), the comparison was against an "FDA cleared device," implying the predicate device's output served as the reference standard. For self-application (part B), signal quality was assessed, presumably against accepted norms for such physiological signals, but no multi-expert adjudication process is described.

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

No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not conducted or described in this document. The X8 System "acquires and displays physiological signals; no claims are being made for analysis of the acquired signals with respect to the accuracy, precision, and reliability." Therefore, this device does not involve AI analysis or human reader interpretation for diagnostic claims, but rather accurate signal acquisition.

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

The device's intended use is to "acquire, record, transmit, and display physiological signals." It does not include automated analysis or diagnostic algorithms. Therefore, a standalone algorithm performance study was not applicable and not done. The focus of the performance study was on the quality and equivalence of the acquired physiological signals.

7. The Type of Ground Truth Used

• For comparison of airflow and respiratory effort signals (Part A): The ground truth was based on the signals obtained from an FDA-cleared predicate device (Compumedics Somte (K072201)).
• For self-application and signal quality (Part B): The ground truth for "valid" recording time and "high EEG quality" would have been established by assessing the acquired physiological signals against accepted physiological standards and criteria for signal integrity, likely by trained professionals. The specific method of establishing this ground truth is not detailed beyond the term "good data."

8. The Sample Size for the Training Set

The document describes "prospective studies" for verification and validation. Given that the device's function is signal acquisition and display, and no claims for analysis accuracy or reliability are being made, there isn't a "training set" in the context of machine learning. The studies described are performance evaluations of the hardware and software's ability to accurately capture and transmit signals.

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

As noted above, no "training set" in the machine learning sense is described for this device. The ground truth for performance evaluation was established through comparison with a predicate FDA-cleared device and assessment against physiological signal quality standards.

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The Night Shift is indicated for prescription use for the treatment of adult patients with positional obstructive sleep apnea with a non-supine apnea-hypopnea index < 20, and to reduce or alleviate snoring. It records position, movement, and sound so that positional changes in sleep quality and snoring can be assessed.

The Night Shift is worn around the neck to reduce the amount of time the user sleeps in the supine position as a treatment for positional obstructive sleep apnea. Night Shift combines hardware and firmware to detect when the user attempts to sleep in the supine position and can initiate vibro-tactile feedback with increasing intensity until the user shifts to a non-supine position. The initiation of positional feedback from the Night Shift is turned on is programmable to allow the user to fall asleep (if they must) on their back. Each night the Night Shift is worn, it monitors sleep position (% time supine), behavioral sleep efficiency, and snoring levels (% time snoring > 40 and 50 dB) as well as the frequency, duration and intensity of the feedback (when applied). These data can be optionally transferred via the USB port to the Night Shift Web Portal where the user can generate a report to assess how well the positional feedback is working. A "trial" protocol can include one night with no feedback to establish a baseline and two nights with feedback to assess compliance/efficacy. Utilization information is saved on the device that allows reports to be generated that compares daily use by month and monthly averages for one year. The portal also allows the device to be reformatted (to eliminate all previously recorded data) for a new user, adjust the feedback settings to a new user's personal preferences, and/or upgrade the firmware. For large healthcare organizations that limit internet access, desktop software is provided as an alternative to the portal.

Here's a breakdown of the acceptance criteria and the study details for the Advanced Brain Monitoring, Inc. Night Shift device, based on the provided text:

Acceptance Criteria and Reported Device Performance

Study Details for Clinical Tests:

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

   • Test Set Sample Size for Primary Effectiveness Endpoint: 27 patients with pre-treatment positional obstructive sleep apnea with a non-supine AHI < 20 were included in the analysis for the primary effectiveness endpoint.
   • Test Set Sample Size for other Endpoints: 30 subjects (the 27 patients plus an additional 3 subjects who had a pre-study non-supine AHI >20).
   • Data Provenance: Not explicitly stated, but the study was a clinical study conducted to evaluate safety and efficacy, implying prospective data collection. The location (country of origin) is not mentioned.

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

   • This information is not provided in the given text. Ground truth for sleep studies typically involves highly trained sleep technologists and physicians interpreting polysomnography (PSG) data. However, the document does not specify the number or qualifications for this particular study.

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

   • This information is not provided in the given text.

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:

   • No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The study evaluated the device's performance in treating sleep apnea and recording sleep parameters, not how human readers improve with or without AI assistance in interpreting diagnostic data from the device. The Night Shift is a therapeutic and monitoring device, not an AI diagnostic interpretation tool.

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

   • Yes, the study primarily assessed the standalone performance of the Night Shift device. While it is a prescription device, its effectiveness was measured by its ability to reduce supine sleep and associated AHI, as well as its accuracy in measuring sleep parameters (position, TST, SE) independently. Human interaction is primarily for setup, compliance, and physician review of the generated reports, but the core therapeutic and monitoring function is standalone.

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

   • The ground truth for sleep parameters (AHI, supine time, TST, SE) appears to be Polysomnography (PSG), a gold standard for sleep disorder diagnosis. For the "Accuracy of Supine Position Measurement" endpoint, the ground truth was "video recordings plus chest sensor." For subjective measures (ESS, FOSQ), the ground truth was the participant's self-reported scores.

7. The sample size for the training set:

   • This information is not provided in the given text. The document describes a clinical validation study, not the development or training phase of an algorithm.

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

   • This information is not provided as the training set details are not mentioned.

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Sleep Profiler is intended for the diagnostic evaluation by a physician to assess sleep quality in adults only. The Sleep Profiler is a software-only device to be used under the supervision of a clinician to analyze physiological signals and automatically score sleep study results, including the staging of sleep, detection of arousals and snoring.

The Sleep Profiler is a software application that analyzes previously recorded physiological signals obtained during sleep. The Sleep Profiler software can analyze any EDF files meeting defined specifications, including signals acquired with the Advanced Brain Monitoring X4 System. Automated algorithms are applied to the raw signals in order to derive additional signals and interpret the raw and derived signal information. The software automates recognition of: a) sleep stage, b) snoring frequency and severity, c) pulse rate, d) cortical (EEG), sympathetic (pulse) and behavioral (actigraphy and snoring) arousals. A single channel of electrocardiography, electrooculargraphy, electromyography, or electroencephalography can be optionally presented for visual inspection and interpretation. The software identifies and rejects periods with poor electroencephalography signal quality. The full disclosure recording of derived signals and automated analyses can be visually inspected and edited prior to the results being integrated into a sleep study report. Medical and history information can be input from a questionnaire. Responses are analyzed to provide a pre-test probability of Obstructive Sleep Apnea (OSA) (a condition that cannot be diagnosed with Sleep Profiler) so an appropriate referral to a sleep physician is made. The automated analyses of physiological data are integrated with the questionnaire data, medical and history information to provide a comprehensive report. Several report formats are available depending on whether the user has acquired more than one night of data, wishes to obtain a narrative summary report or provide patient reports. The capability to enter or edit patient information, call the application to generate a study report, and/or download a report is provided using either the desktop PC application or in a web-based module which emulates the desktop functionality. The same analysis and report generation software is used for both the desktop and web-portal applications.

The provided text describes a 510(k) submission for the Advanced Brain Monitoring, Inc. Sleep Profiler (K130007). This submission is for modifications to an already cleared device (K120450) to introduce a web-based module for patient information entry and report generation/download. The core analysis and report generation software remains unchanged from the predicate device.

Therefore, the acceptance criteria and study information provided in this document focus exclusively on the non-clinical testing performed to demonstrate that the new web-based module functions equivalently to the desktop application. There is no clinical study described here that would involve human readers, ground truth establishment through expert consensus or pathology, or outcome data for performance metrics like sensitivity, specificity, etc.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not explicitly stated. The verification and validation activities tested the functionality of the web-based module against the desktop application, indicating a comparative test of functionalities. It does not refer to a "test set" in the context of clinical data.
• Data Provenance: Not applicable in the context of clinical data, as this was a non-clinical software verification study. The tests would have involved functional verification of the software.

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

• Not applicable. This submission focuses on software functionality verification, not clinical performance requiring expert ground truth.

4. Adjudication Method for the Test Set

• Not applicable. This was a non-clinical software verification, not a clinical study requiring adjudication of expert interpretations.

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. An MRMC study was not conducted or described in this document. The submission explicitly states that "The modifications ... did not require clinical studies to support substantial equivalence."

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

• Yes, indirectly. The Sleep Profiler is described as a "software-only device" that "automates recognition of: a) sleep stage, b) snoring frequency and severity, c) pulse rate, d) cortical (EEG), sympathetic (pulse) and behavioral (actigraphy and snoring) arousals." The verification discussed here specifically confirms that the web-based module performs these functions identically to the pre-existing desktop version, which does perform these analyses automatically without continuous human intervention during the analysis phase. However, it's intended to be "used under the supervision of a clinician" and the reporting can be "visually inspected and edited prior to the results being integrated into a sleep study report," implying a human-in-the-loop for final interpretation. The performance of the automated algorithm itself was established in the predicate device (K120450) and is not being re-evaluated for K130007.

7. The Type of Ground Truth Used

• Not applicable for clinical performance. For the software verification described, the "ground truth" was the expected functional output and behavior of the established desktop application (K120450). The web-based module was verified to produce identical results.

8. The Sample Size for the Training Set

• Not provided/not applicable. This submission does not discuss algorithmic development or training sets. It is a modification to an already existing software application. The predicate device (K120450) would have had this information.

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

• Not provided/not applicable. As above, this information relates to the original algorithmic development, not the current submission for a web-based module.

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Sleep Profiler is intended for the diagnostic evaluation by a physician to assess sleep quality in adults only. The Sleep Profiler is a software-only device to be used under the supervision of a clinician to analyze physiological signals and automatically score sleep study results, including the staging of sleep, detection of arousals and snoring.

The Sleep Profiler is a software application that analyzes previously recorded physiological signals obtained during sleep. The Sleep Profiler software can analyze any EDF files meeting defined specifications, including signals acquired with the Advanced Brain Monitoring X4 System which is the subject of a separate 510(k). Automated algorithms are applied to the raw signals in order to derive additional signals and interpret the raw and derived signal information. The software automates recognition of: a) sleep stage, b) snoring frequency and severity, c) pulse rate, d) cortical (EEG), sympathetic (pulse) and behavioral (actigraphy and snoring) arousals. A single channel of electrocardiography, electrooculargraphy, electromyography, or electroencephalography can be optionally presented for visual inspection and interpretation. The software identifies and rejects periods with poor electroencephalography signal quality. The full disclosure recording of derived signals and automated analyses can be visually inspected and edited prior to the results being integrated into a sleep study report. Medical and history information can be input from a questionnaire. Responses are analyzed to provide a pre-test probability of Obstructive Sleep Apnea (OSA) (a condition that cannot be diagnosed with Sleep Profiler) so an appropriate referral to a sleep physician is made. The automated analyses of physiological data are integrated with the questionnaire data, medical and history information to provide a comprehensive report. Several report formats are available depending on whether the user has acquired more than one night of data, wishes to obtain a narrative summary report or provide patient reports.

Here's a breakdown of the acceptance criteria and the study details for the Sleep Profiler device, based on the provided 510(k) summary:

Acceptance Criteria and Device Performance

The acceptance criteria are implied by the comparison to a predicate device, MICHELE (K112102). The goal is to demonstrate "similar" performance. The specific metrics are overall percent agreement and agreement for each sleep stage.

Table 1: Sleep Profiler Performance vs. Predicate Device Performance (Sleep Staging)

The summary states, "The positive and negative percent agreement obtained during clinical validation of the Sleep Profiler are similar to that obtained by the predicate device, MICHELE (K112102), which was validated using a different data set."

Study Details

1. Sample Size used for the test set and the data provenance:

   • Test Set Sample Size: 44 subjects.
   • Data Provenance: Not explicitly stated, but it's a "clinical validation" comparing to manual observation. It doesn't state whether it's retrospective or prospective, or the country of origin.

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

   • Number of Experts: Three raters.
   • Qualifications: "either sleep technicians or physicians."

3. Adjudication method for the test set:

   • The table indicates a "No-Consensus" row for the experts, implying adjudication by consensus was used. Specifically, the "Epochs assigned by Expert Scoring" includes a "No-Consensus" category (653 epochs out of 39191 total), suggesting that if the three raters did not agree, those epochs were excluded from the primary agreement calculations for individual stages. The main performance metrics are likely based on epochs where there was full consensus (3 out of 3, or potentially 2 out of 3 if that's what "consensus" meant here, although the "No Consensus" row suggests agreement was required).

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:

   • No, an MRMC comparative effectiveness study was not done. The study's purpose was to validate the "sleep staging algorithms by comparison to sleep staging made by manual observation by three raters." This is a standalone algorithm performance study compared to human experts as ground truth, not a study evaluating human performance with or without AI assistance.

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

   • Yes, a standalone algorithm performance study was done. The results presented in the table ("Epochs assigned by Sleep Profiler" vs. "Epochs assigned by Expert Scoring") directly report the algorithm's performance without human interaction or modification. The description also states the software "automates recognition" and that the "full disclosure recording of derived signals and automated analyses can be visually inspected and edited prior to the results being integrated into a sleep study report," but the presented validation data is for the automated algorithm's output.

6. The type of ground truth used:

   • Expert Consensus. The ground truth for the test set was established by the "manual observation by three raters who were either sleep technicians or physicians."

7. The sample size for the training set:

   • Not specified. The document does not provide details about the training set size or how it was established. It only discusses the clinical validation (test set) of the software.

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

   • Not specified. Since details about the training set are not provided, how its ground truth was established is also not mentioned.

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The X4 System is intended for prescription use in the home, healthcare facility, or clinical research environment to acquire, record, transmit and display physiological signals from adult patients. The X4 System acquires, records, transmits and displays electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and/or electromyogram (EMG), accelerometer, acoustical and photoplethesmographic signals. The X4 system only acquires and displays physiological signals, no claims are being made for analysis of the acquired signals with respect to the accuracy, precision and reliability.

The X4 system is used for configurable acquisition of physiological signals. Model X4-E provides for acquisition of three channels of electroencephalography (EEG) and one photoplethesmographic (PPG) signal from a head strip, with an optional channel connected to two sensors via a dual-lead connector with twice the gain. Model X4-M provides four channels of EEG with the dual-lead connector providing the input for reference sensors. Both models measure sound via an acoustic microphone, and movement and position measured via a 3-D accelerometer. The device is designed so it can be affixed by the patient and to record data. Alternatively, a technician can affix the device and display the signals via a wireless connection during acquisition. The X4 system firmware monitors signal quality to ensure that the sensors are properly applied and that high quality signals are being acquired.

The X4 software provides a means to: a) initiate a study and track patient information, b) acquire and save signals to the memory of the device, c) acquire and wirelessly transmit signals from the device, d) upload data saved in the memory of the device to a PC, and e) visually inspect the signal quality.

The acquired signals are saved in a universal data format (European Data Format – EDF) that is intended to be analyzed with third party software, including Advanced Brain Monitoring's Sleep Profile Software (K120450).

The provided text describes the X4 System, a device for acquiring physiological signals. The 510(k) summary focuses on demonstrating substantial equivalence to predicate devices through non-clinical testing and a limited clinical study.

Here's an analysis of the acceptance criteria and study information provided:

Acceptance Criteria and Reported Device Performance

The core acceptance criterion for the X4 System, as articulated in the clinical tests, is based on the interpretability of the acquired signals and the ease of device application.

Study Details for Acceptance Criteria

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test set sample size: While a specific number for the sample size for the clinical test is not explicitly stated, it refers to "all subjects" and "over 90% of studies recorded overnight." This suggests a prospective clinical study involving multiple subjects.
• Data provenance: Not explicitly stated, but the context of a 510(k) submission to the FDA for a US company suggests it would likely involve data from the US, potentially a prospective clinical trial.

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)

• The summary states that "signals acquired with the X4 System provide similar information as compared to the predicate device that would allow a physician to interpret the signals." and that "over 90% of studies recorded overnight with the X4 are interpretable."
• This implies that physicians are the experts who would interpret the signals to determine interpretability. However, the number of experts and their specific qualifications (e.g., board-certified sleep physicians, neurologists, years of experience) are not specified in the provided text.

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

• The text does not specify an adjudication method. It mentions that "physicians" would interpret the signals, but it does not detail how potential disagreements among interpreters would be resolved or if multiple interpreters were used for each case.

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, an MRMC comparative effectiveness study was not done.
• The X4 System is described as a device for acquiring, recording, transmitting, and displaying physiological signals, with "no claims being made for analysis of the acquired signals with respect to the accuracy, precision and reliability." This indicates that the device itself does not perform AI-assisted analysis and therefore, a study on human reader improvement with AI assistance would not be applicable to this submission.

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

• No, a standalone (algorithm only) performance study was not done.
• The device's intended use is to acquire, record, transmit, and display physiological signals. It explicitly states "no claims are being made for analysis of the acquired signals." Thus, there is no standalone algorithm being evaluated for diagnostic or analytical performance.

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

• The ground truth for the clinical study appears to be expert interpretation (physician interpretability) of the acquired signals. The study aimed to demonstrate that the signals are "interpretable" and "behave as expected," which would rely on a physician's assessment.

8. The sample size for the training set

• Not applicable / Not provided. The X4 System is a signal acquisition and display device, not an analytical or AI-driven system. Therefore, it does not involve a "training set" in the context of machine learning algorithms.

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

• Not applicable. As a signal acquisition device without analytical claims, there is no training set or associated ground truth establishment process for algorithm training.

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The Apnea Risk Evaluation System (ARES™), Model 610 is indicated for use in the diagnostic evaluation by a physician of adult patients with possible sleep apnea. The ARES™ can record and score respiratory events during sleep (e.g., apneas, hypopneas, mixed apneas and flow limiting events). The device is designed for prescription use in home diagnosis of adults with possible sleep-related breathing disorders.

The Apnea Risk Evaluation System (ARES™) includes a device called a Unicorder which records oxygen saturation, pulse rate, snoring level, head movement and head position, and airflow. Additionally, a physiological signal from the forehead used to stage sleep or respiratory effort signal obtained from an optional piezo respiratory effort belt can be acquired. The battery powered Unicorder provides sufficient capacity to record two nights of data. The device monitors signal quality during acquisition and notifies the user via voice messages when adjustments are required. A standard USB cable connects the Unicorder to a USB port on a host computer when patient data is to be uploaded or downloaded. The USB cable provides power to the Unicorder during recharging from the host computer or from a USB wall charger. The Unicorder cannot record nor can it be worn by the patient when connected to the host computer or the wall charger. Software, residing on a local PC or a physical or virtual server controls the uploading and downloading of data to the Unicorder, processes the sleep study data and generates a sleep study report. The ARES™ can auto-detect positional and non-positional obstructive and mixed apneas and hypopneas similarly to polysomnography. It can detect sleep/wake and REM and non-REM. After the sleep study has been completed, data is transferred off the Unicorder is prepared for the next study. The downloaded sleep study record is then processed with the ARES™ Insight software to transform the raw signals and derive and assess changes in oxygen saturation (SpO2), pulse rate, head movement, head position, snoring sounds, airflow, and EEG or respiratory effort. The red and IR signals are used to calculate the SpO2 and pulse rate. The actigraphy signals are transformed to obtain head movement and head position. A clinician can convert an auto-detected obstructive apnea to a central apnea based on visual inspection of the waveforms. ARES™ Screener can predict pre-test probability of obstructive sleep apnea (OSA). The ARES™ can also assist the physician to identify patients who will likely have a successful OSA treatment outcome, including CPAP and oral appliance therapies. ARES™ can help identify patients who would benefit from a laboratory PAP titration.

The provided FDA 510(k) summary for the Apnea Risk Evaluation System (ARES™), Model 610 (K112514) primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study proving the device meets specific acceptance criteria in a standalone performance evaluation. The changes in the modified device are related to software platform, improved filtering of the SpO2 signal, and new claims in the report messages.

Here's an attempt to extract and organize the information based on your request, with significant caveats that much of the requested detail is not explicitly provided in the document for the performance of the modified device in the way you've outlined:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are primarily for the SpO2 accuracy after the filtering changes. The document explicitly states that the accuracy in all ranges is less than 3.5% as recommended by draft FDA guidance.

Note: The document states "The Arms has changed due to the filtering changes but labeling will reflect specification of < 3.0%. Accuracy in all ranges is less than 3.5% as recommended in Draft Guidance..." This implies < 3.0% is the target specification for labeling, and the performance meets the broader FDA guidance of < 3.5%.

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

The document mentions validation of the SpO2 filtering changes using "clinical data previously acquired in two clinical studies" but does not specify the sample size for these studies. The data provenance is described as "clinical data previously acquired," suggesting it was retrospective analysis of existing data. The country of origin is not specified.

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

This information is not provided in the document. For the new claims regarding treatment messages and PAP titration identification, it states these are "supported by published literature," but it doesn't detail the ground truth establishment process for this specific device's testing.

4. Adjudication Method 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

A Multi Reader Multi Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not mentioned in the document. The study described focuses on the device's SpO2 accuracy and the functionality of the new software platform and report messages.

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

A standalone performance evaluation of the algorithm's core diagnostic capabilities (e.g., apnea/hypopnea detection accuracy compared to PSG) is not detailed as a new clinical study for this 510(k) submission. The clinical tests mentioned specifically relate to the filtering changes to the SpO2 signal. The document states: "The ARES™ can auto-detect positional and non-positional obstructive and mixed apneas and hypopneas similarly to polysomnography," which implies standalone performance, but the K112514 document primarily refers back to the predicate device (K111194) for such claims and only describes validation of the SpO2 filtering for the current submission.

7. The Type of Ground Truth Used

For the SpO2 accuracy validation:

• Original breathe down data: This typically involves controlled desaturation events where arterial blood gas measurements or a highly accurate reference pulse oximeter serve as ground truth for SpO2.
• Breath hold data: This involves evaluating performance during dynamic SpO2 changes, again implying a reference standard for SpO2 measurements.
• For the new claims (treatment planning messages, PAP titration): The ground truth is cited as published literature.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions "previously acquired clinical data" being used for validation, but not for training.

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

The document does not provide information on how ground truth was established for any training set for the modified device, as it primarily focuses on validating changes to an already cleared device.

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The Apnea Guard is a mandibular repositioning device intended to reduce or alleviate snoring and mild to moderate obstructive sleep apnea (OSA) in patients 18 years and older. The Apnea Guard is intended to be fitted with assistance from a healthcare professional, and used during sleep for less than 30-nights.

The Apnea Guard is a Mandibular Repositioning Device (MRD) which consists of interlocking upper and lower trays filled with silicone retention material fitted to the patient. The useful life of the retention material will limit the Apnea Guard's used to a temporary appliance, less than 30 days. The trays are locked into a position that advances the mandible for the treatment of snoring and / or obstructive sleep apnea. The one-size-fits-all appliance is able to accommodate the full range of dental etiologies, including wide and narrow arches, missing or compromised teeth and gums, etc.

This document describes the acceptance criteria and the study that demonstrates the Apnea Guard device meets these criteria.

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly present a table of "acceptance criteria" for the Apnea Guard in a quantitative manner that can be directly mapped to the study's performance results. Instead, it frames the performance in terms of "substantial equivalence" to a predicate device (Airway Management TAP III). The key performance indicator highlighted is the reduction in Apnea-Hypopnea Index (AHI).

Therefore, the table below reflects this comparative performance for the critical metric mentioned:

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

• Sample Size: Eighteen patients.
• Data Provenance: The study was "prospective." The document does not explicitly state the country of origin, but given the FDA 510(k) submission, it is highly likely to be a study conducted within the United States or under international clinical trial standards acceptable to the FDA.

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

The document does not specify the number or qualifications of experts (e.g., sleep physicians, polysomnography technologists) involved in establishing the ground truth for the test set. It mentions that patients underwent a "two-night Qualification Home Sleep Test (HST)" and that "Measurement and recording of Apnea Index, Apnea-Hypopnea Index, Hypoxemia, Snoring, and Sympathetic Arousals was done." This implies that standard diagnostic procedures for sleep disorders, typically interpreted by trained professionals, were followed.

4. Adjudication Method for the Test Set

The document does not describe a specific adjudication method (e.g., 2+1, 3+1) for the test set data. The collection of AHI, hypoxemia, snoring, and sympathetic arousal data likely followed standard polysomnography scoring rules, which inherently involve a defined process for data interpretation, but not necessarily a multi-reader adjudication process as seen in some AI studies.

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, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. The study was a clinical study comparing the Apnea Guard (the proposed device) against a predicate device (Airway Management TAP III) in terms of its effectiveness in reducing sleep-disordered breathing. It does not involve human readers interpreting data with or without AI assistance.

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

The Apnea Guard is a physical mandibular repositioning device, not an algorithm or software. Therefore, the concept of a "standalone" algorithmic performance study is not applicable. The device itself is the "standalone" intervention being tested.

7. The Type of Ground Truth Used

The ground truth for the clinical performance was established using physiological measurements and clinical assessments derived from sleep studies. Specifically, "Apnea Index, Apnea-Hypopnea Index, Hypoxemia, Snoring, and Sympathetic Arousals" were measured and recorded. These metrics are objective physiological indicators of sleep-disordered breathing.

8. The Sample Size for the Training Set

No training set is mentioned as the Apnea Guard is a physical medical device, not an AI/ML algorithm that requires a training phase.

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

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

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The Apnea Risk Evaluation System (ARES™), Model 610 is indicated for use in the diagnostic evaluation by a physician of adult patients with possible sleep apnea. The ARES™ can record and score respiratory events during sleep (e.g., apneas, hypopneas, mixed apneas and flow limiting events). The device is designed for prescription use in home diagnosis of adults with possible sleep-related breathing disorders.

The Apnea Risk Evaluation System (ARES™) includes a device called a Unicorder which records oxygen saturation, pulse rate, snoring level, head movement and head position, and airflow. Additionally, a physiological signal from the forehead used to stage sleep or respiratory effort signal obtained from an optional piezo respiratory effort belt can be acquired. The battery powered Unicorder provides sufficient capacity to record two nights of data. The device monitors signal quality during acquisition and notifies the user via voice messages when adjustments are required. A standard USB cable connects the Unicorder to a USB port on a host computer when patient data is to be uploaded or downloaded. The USB cable provides power to the Unicorder during recharging from the host computer or from a USB wall charger. The Unicorder cannot record nor can it be worn by the patient when connected to the host computer or the wall charger. Software controls the uploading and downloading of data to the Unicorder, processes the sleep study data and generates a sleep study report. The ARES™ can auto-detect positional and non-positional obstructive and mixed apneas and hypopneas similarly to polysomnography. It can detect sleep/wake and REM and non-REM. After the sleep study has been completed, data is transferred off the Unicorder is prepared for the next study. The downloaded sleep study record is then processed with the ARES™ Insight software to transform the raw signals and derive and assess changes in oxygen saturation (SpQ2), pulse rate, head movement, head position, snoring sounds, airflow, and EEG or respiratory effort. The red and IR signals are used to calculate the SpO- and pulse rate: The actigraphy signals are transformed to obtain head movement and head position. A clinician can convert an auto-detected obstructive apnea to a central apnea based on visual inspection of the waveforms. ARES™ Screener can predict pre-test probability of obstructive sleep apnea (OSA). The ARES™ can assist the physician to identify patients who will likely have a successful OSA treatment outcome, including CPAP and oral appliance therapies. ARES™ can also help identify patients who would benefit from a laboratory PAP titration.

Here's an analysis of the provided text regarding the ARES device's acceptance criteria and studies:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document mentions "Two clinical studies were conducted," but does not explicitly state the sample size for either clinical study.

The data provenance is not explicitly stated (e.g., country of origin, retrospective or prospective). However, the description of clinical studies implies prospective data collection for the purpose of validating the device.

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

The document does not specify the number of experts used to establish ground truth for the clinical studies.

It notes that the "signals acquired... would allow a physician, trained in sleep medicine to interpret the signals." This implies that the interpretation of signals for the "ground truth" or comparison in the equivalence study would be done by physicians trained in sleep medicine, but their specific qualifications (e.g., years of experience) are not provided.

4. Adjudication Method for the Test Set:

The document does not describe any specific adjudication method (e.g., 2+1, 3+1). For the first clinical study, it states the ARES chest signal and Somté chest signal were "equivalent in their response," implying a direct comparison rather than a consensus among multiple human readers.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size:

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done as described in this 510(k) summary. The clinical studies focused on signal equivalence and interpretability, not on the improvement of human readers with or without AI assistance.

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

The document mentions that the ARES™ can "auto-detect positional and non-positional obstructive and mixed apneas and hypopneas similarly to polysomnography" and that "A clinician can convert an auto-detected obstructive apnea to a central apnea based on visual inspection of the waveforms."

However, beyond these functional descriptions, the reported clinical studies primarily focus on signal acquisition, equivalence, and interpretability by a physician, rather than a standalone performance evaluation of the scoring algorithm's accuracy against a gold standard without human intervention. The statement "at least 95% of signals from the ARES effort belt recorded during overnight studies are interpretable and behave consistently based on the airflow signal" suggests a focus on the quality of the raw data for human interpretation, not the autonomous performance of an algorithm.

7. The Type of Ground Truth Used:

For the first clinical study (signal equivalence), the "ground truth" appears to be the signals simultaneously acquired using the predicate device, Compumedics Somté System. This functions as a form of comparative ground truth against an established device.

For the second clinical study (usability/interpretability), the ground truth for "interpretable and behave consistently" is implied to be expert visual assessment based on consistency with airflow signals by a physician trained in sleep medicine. Additionally, subject feedback from a Usability survey served as a form of ground truth for ease of use.

8. The Sample Size for the Training Set:

The document does not provide any information regarding a training set or its sample size. This 510(k) summary focuses on the validation of modifications to an existing device (adding a respiratory effort belt) and demonstrating substantial equivalence, rather than the initial development and training of the core algorithmic components for apnea detection.

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

Since no training set information is provided, the method for establishing its ground truth is also not described.

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The Apnea Risk Evaluation System (ARES™) is indicated for use in the diagnostic evaluation by a physician of adult patients with possible sleep apnea. The ARES can record and score respiratory events during sleep (e.g., apneas, hypopneas, mixed apneas and flow limiting events). The device is designed for prescription use in home diagnosis of adults with possible sleep-related breathing disorders.

The Apnea Risk Evaluation System (ARES) includes a device called a Unicorder which records oxygen saturation, pulse rate, snoring level, head movement and head position, airflow, respiratory effort, and a physiological signal from the forehead used to stage sleep. The battery-powered Unicorder provides sufficient capacity to record for 18-hours of continuous use. The device monitors signal quality during acquisition and notifies the user via voice messages when adjustments are required. A standard USB cable connects the Unicorder to a USB port on a host computer when patient data is to be uploaded or downloaded. The USB cable provides power to the Unicorder during recharging from the host computer or from a USB wall charger. The Unicorder cannot record nor can it be worn by the patient when connected to the host computer or the wall charger. Software controls the uploading and downloading of data to the Unicorder, processes the sleep study data and generates a sleep study report. Algorithms are applied to the physiological data to automatically detect apneas and hypopneas, distinguish sleep from awake and rapid eye movement sleep from non-rapid eye movement sleep. A full disclosure recording is provided, allowing a clinician to edit any of the events detection algorithms. The software includes the capability to assign a pre-test probability of a patient having OSA based on questionnaire responses. Six disposable components must be replaced and the forehead sensor must be cleaned before reuse.

The provided text describes the Apnea Risk Evaluation System (ARES) Model 600. It details various performance tests conducted to establish substantial equivalence to predicate devices, but it does not provide specific acceptance criteria or an explicit study that quantifies device performance against those criteria in a table format with numerical results.

However, based on the text, we can infer some aspects related to acceptance criteria and the nature of the validation.

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not present a table of acceptance criteria with corresponding performance metrics. Instead, it indicates that "Design verification and validation tests were performed on the ARES Unicorder Model 600 to ensure it meets the specified product requirements," and states that the accuracy of automated detection was "assessed and compared to the predicate device." This phrasing suggests a comparative approach to acceptance rather than pre-defined numerical thresholds.

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

The document does not explicitly state the sample size used for the test set for performance evaluation of automated detection. It mentions "laboratory PSG" as the gold standard, suggesting the data originated from sleep lab studies. The provenance (country of origin) is not specified, but the submission is to the US FDA, so it's likely US data. The data appears to be retrospective, as it's used for comparison against technician scoring.

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

The document states that "The technician scoring was considered the gold-standard for purposes of assessing accuracy."

• Number of experts: Not specified (implied to be one or more technicians per PSG).
• Qualifications of experts: "Technician scoring" implies trained polysomnography technicians. No specific experience level (e.g., "10 years of experience") is mentioned.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method like 2+1 or 3+1. The ground truth for automated detection was based on "technician scoring," which implies a single-reader assessment per PSG record, rather than a consensus or adjudicated reading.

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

No, an MRMC comparative effectiveness study is not described. The documentation focuses on the device's standalone performance compared to technician scoring and other predicate devices, not on human readers' improvement with AI assistance.

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

Yes, a standalone performance assessment was done. The document states: "The accuracy of the automated detection of awake and sleep and REM vs. non-REM compared to technician scoring of laboratory PSG was assessed and compared to the predicate device." This directly refers to the algorithm's performance without human intervention.

7. The Type of Ground Truth Used

The ground truth used for assessing the automated detection of sleep stages (awake, sleep, REM vs. non-REM) was expert consensus / technician scoring (specifically stated as "technician scoring of laboratory PSG was considered the gold-standard").

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size of a training set for the algorithm. It focuses on the validation of the ARES Model 600, not its development.

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

Since no information regarding a training set is provided, there is also no information on how its ground truth was established.

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