Search Results

Aurora is a Software as a Medical Device (SaMD) that establishes sleep quality. Aurora automatically analyzes, displays, and summarizes Photoplethysmogram (PPG) data collected during sleep using compatible devices. Aurora is intended for use by and by order of a healthcare professional to aid in the diagnosis of sleep disorders including sleep apnea in adults.

The Aurora output, including automatically detected respiratory events and parameters, may be displayed and edited by a qualified healthcare professional. The Aurora output is not intended to be interpreted or clinical action taken without consultation of a qualified healthcare professional.

Aurora is not intended for use with polysomnography devices.

Aurora is a Class II Software as a Medical Device (SaMD), intended to aid in the evaluation of sleep disorders, where it may inform or drive clinical management. Aurora is a software application that is indicated for use on a general-purpose computing platform. It is a cloud-based software-as-a-medicaldevice (SaMD) with a user interface that runs in a web browser.

Aurora automatically analyzes and displays photoplethsmography (PPG) signal data including SPO2 and pulse/heart rate only from compatible FDA-cleared medical purpose pulse oximeters that meet Aurora's data acquisition requirements for sampling rate, digital resolution, measurement range, and accuracy range.

Following upload of a compatible PPG study to the cloud software, the algorithm functions by verifying minimum signal quality, study length, and technical adequacy requirements, preprocessing the data including normalization, digital filtration, and artifact detection/rejection procedures, applying machine learning algorithms including multiple deep neural network machine learning models, statistical signal processing analyses including time-domain and time-frequency domain analyses over multiple time and resolution scales, and other analyses output a detected set of events and derived signals for the PPG study that are post-processed and logically filtered according to algorithm rules based on the American Academy of Sleep Medicine (AASM) recommended scoring event, desaturation, and association rules. Aurora algorithm outputs, including scored respiratory events, sleep stages, Aurora Apnea-Hypopnea Index (eAHI), Total Sleep Time (TST), Sleep Efficiency (SE), Sleep Latency (SL), Wake After Sleep Onset (WASO), and Oxygen Desaturation Events Index (ODI) measures, are stored and made available for display, editing, and review in Aurora by qualified healthcare professionals.

Aurora reports results of the automated data analysis based on AASM guidelines, including the Aurora output Apnea-Hypopnea Index (eAHI) and total sleep time (TST). The algorithm outputs are graphical and numerical displays and reports of sleep latency, sleep quality, and sleep pathologies including sleep disordered breathing. The Aurora displays and reports are for the order of physicians, trained technicians, or other healthcare professionals to evaluate sleep disorders where it may inform or drive clinical management taking into consideration other factors that normally are considered for clinical management of sleep disorders for adults.

The clinician can view raw data for interpretation, edit events, write clinical notes, and customize sleep reports for the patient.

Aurora output is not intended to be interpreted or clinical action taken without consultation of a qualified healthcare professional.

The document provides detailed information about the performance evaluation of the Aurora device, a Software as a Medical Device (SaMD) intended to aid in the diagnosis of sleep disorders.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them:

1. Acceptance Criteria and Reported Device Performance

The acceptance criteria for Aurora are implied by the performance metrics reported, demonstrating its accuracy in detecting Apnea Hypopnea Index (eAHI) and performing sleep staging against polyomnography (PSG) ground truth. While explicit numerical "acceptance criteria" tables are not provided, the reported sensitivity, specificity, and regression/Bland-Altman statistics serve as the evidence of meeting performance expectations for substantial equivalence.

Table of Performance Data (Implied Acceptance Criteria)

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

• Test Set Sample Size:
  • For eAHI performance (sensitivity/specificity): 158 adult patients.
  • For Sleep Staging:
    • Wake: 52,622 epochs
    • Light NREM: 69,438 epochs
    • Deep NREM: 10,195 epochs
    • REM: 14,459 epochs
• Data Provenance: The document does not explicitly state the country of origin but implies clinical settings where PSG (Polysomnography) and HSAT (Home Sleep Apnea Test) recordings are collected. The study involved simultaneous PSG and HSAT recordings, suggesting a prospective collection of data for testing purposes to facilitate direct comparison.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Three registered polysomnographic technologists were used for manual scoring, and one board-certified sleep physician reviewed each PSG.
• Qualifications of Experts:
  • Scorers: Registered polysomnographic technologists.
  • Reviewer/Confirmer: Board-certified sleep physician.

4. Adjudication Method for the Test Set

• Adjudication Method: A 2+1 consensus method. For an event to be officially scored or reported, a consensus of at least two-thirds among the three scorers was required. Additionally, each PSG was reviewed by a board-certified sleep physician to provide clinical confirmation of scoring and technical adequacy, serving as a final adjudication layer.

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

• The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to assess how much human readers improve with AI vs. without AI assistance. The study focuses on the standalone performance of the Aurora algorithm against expert-scored ground truth. The device output may be displayed and edited by a qualified healthcare professional, suggesting a human-in-the-loop workflow, but the study described does not quantify the effect of AI assistance on human reader performance.

6. Standalone Performance Study

• Yes, a standalone performance study was done. The reported sensitivity, specificity, Deming regression, and Bland-Altman analyses directly evaluate the algorithm's performance (Aurora) against the expert-scored PSG as ground truth, without a human in the loop for the performance metrics themselves.

7. Type of Ground Truth Used

• The type of ground truth used was expert consensus from manual scoring of Polysomnography (PSG) data. Specifically, PSG recordings were manually scored by three registered polysomnographic technologists using guidelines following the 3% desaturation guidance. This was further reviewed and confirmed by a board-certified sleep physician.

8. Sample Size for the Training Set

• The document does not specify the sample size for the training set. The provided details pertain exclusively to the test set used for performance validation.

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

• The document does not specify how the ground truth for the training set was established. Information regarding the training data, its collection, or annotation methods is not included in this summary.

Ask a specific question about this device

EnsoSleep is intended for use in the diagnostic evaluation by a physician to assess sleep quality and as an aid for physicians in the diagnosis of sleep disorders and respiratory related sleep disorders in pediatric as follows:

• · Pediatric patients 13 years and older with polysomnography (PSG) tests obtained in a Hospital or Sleep Clinic
• · Adult patients with PSGs obtained in a Hospital or Sleep Clinic
• · Adult patients with Home Sleep Tests
  EnsoSleep is a software-only medical device to be used under the supervision of a clinician to analyze physiological signals and automatically score sleep study results, including the staging of arousals, leg movements, and sleep disordered breathing events including obstructive apneas (OSA), central sleep apneas (CSA), and hypopneas.
  All automatically scored events and physiological signals which are retrieved, analyzed, displayed, and summarized are subject to verification by a qualified clinician. Central sleep apneas (CSA) should be manually reviewed and modified as appropriate by a clinician.
  All events can be manually marked or edited within records during review.
  Photoplethysmography (PPG) total sleep time is not intended for use when electroencephalograph (EEG) data is recorded. PPG total sleep time is not intended to be used as the sole or primary basis for diagnosing any sleep related breathing disorder, prescribing treatment, or determining whether additional diagnostic assessment is warranted.

EnsoSleep is a software-only medical device that analyzes previously recorded physiological signals obtained during sleep. Users of EnsoSleep are consistent with the roles required to run a sleep clinic: sleep physicians, sleep technicians, clinic operations managers, and IT administrators. EnsoSleep can analyze at-home and in-lab sleep studies for both adult and pediatric patients who are at least 13 years old. 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 the following: respiratory events, sleep staging events, arousal events, movement events, cardiac events, derived signals, and calculated indices. EnsoSleep does not interpret the results, nor does it suggest a diagnosis. The device only marks events of interest for review by a physician who is responsible for diagnoses. The device does not analyze data that are different from those analyzed by human scorers.
The signals and automated analyses can be visually inspected and edited prior to the results being integrated into a sleep study report.
The software consists of 4 major components:

• The Application Platform runs on local clinic workstations and manages the detection, upload, and download of study records and scoring to and from the Storage Platform
• The Processing Platform accepts raw physiological signals as inputs in order to recognize events, derive signals, and calculate indices
• . The Storage Platform facilitates file and database storage in the EnsoSleep cloud through an API
• The Dashboard is a web-based user interface to support configuration, clinic management, and sleep study scoring

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The text describes that "the acceptance criteria were selected based on PA, NA, OA, MAE, Deming Regression coefficient, Bland-Altman mean difference and limits of agreement performance criteria that validate performance substantially equivalent to, or greater than, but not lesser than by more than 10% or similarly defined criteria in any category of the predicate 510(k) reported device performance across all endpoints respectively."

For detailed performance metrics, the document presents tables for specific endpoints. Below is a structured representation focusing on the key performance indicators mentioned and a comparison to the predicate device where available.

Endpoint 1: Sleep Staging Event Detection (EnsoSleep K210034 vs. Predicate K162627)

Endpoint 2: Sleep Apnea Diagnostic Agreement (Per-Patient AHI)

Endpoint 3: Sleep Scoring Event Detection (EnsoSleep K210034 vs. Predicate K162627 and Reference K112102)

Endpoint 4: Total Sleep Time (TST) and Respiratory Rate (RR)

Overall Conclusion on Study Meeting Acceptance Criteria:
"The subject EnsoSleep device event detection and diagnostic agreement performance were observed to meet or exceed the PA, NA, and OA performance acceptance in the 26 total experiments (26/26) across all 4 experimental endpoints evaluated..." indicating that the device successfully met all predefined acceptance criteria.

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

• Test Set Sample Sizes:
  • Adult Sample: N=100 adult subjects (for Sleep Staging, Sleep Apnea Diagnostic, Event Detection, and TST/RR)
  • Pediatric Sample: N=47 pediatric subjects (for Sleep Staging, Sleep Apnea Diagnostic, and Event Detection, and TST/RR)
  • Respiratory Rate (RR) Sample: N=100 adult subjects (specifically for Respiratory Rate and TST analysis)
• Data Provenance:
  • "Archived collection of retrospective diagnostic clinical PSG subject data."
  • The data was collected from "five (5) clinical testing laboratories" where "two (2) AASM Accredited Sleep Testing Facilities were selected each with two (2) regional sleep testing centers."
  • No specific country of origin is mentioned, but the regulatory submission is to the FDA (U.S. Food & Drug Administration). Given the use of AASM (American Academy of Sleep Medicine) accreditation, it is highly likely the data originated from the United States.
  • The data is explicitly stated as retrospective.

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

• Number of Experts: A panel of 9 total registered scoring technologists (RPSGTs) was used to establish the ground truth. From this panel, any given subject was assigned to 3 additional, prospective scorers.
• Qualifications of Experts: The RPSGTs had "5 to 20+ years clinical experience". They were verified to meet "all defined study scoring-acquisition, scoring-blind, and rater-quality certification controls."

4. Adjudication Method for the Test Set

• The ground truth was established using a "2/3 Majority Scoring consensus reference". This means that for any given event or stage, at least two out of the three independent scorers had to agree for it to be considered part of the ground truth.

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

• The study described is not a Multi-Reader Multi-Case (MRMC) comparative effectiveness study evaluating human readers' improvement with AI assistance.
• Instead, it's a standalone performance study comparing the EnsoSleep algorithm's performance against expert consensus (human-scored ground truth) and demonstrating substantial equivalence to a predicate device.
• The study design focused on the agreement of the device with human scorers, not on how the device assists human readers or changes their performance.

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

• Yes, a standalone performance study was done. The described tests for "event detection and diagnostic agreement performance" of the EnsoSleep device relate to its direct output compared to the ground truth established by human experts. The system "automatically score sleep study results" and "does not interpret the results, nor does it suggest a diagnosis." It marks events for a clinician's review and verification, implying its standalone detection capability is what was assessed.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

• The type of ground truth used was expert consensus, specifically a "2/3 Majority Scoring consensus reference" established by independent, registered sleep technologists (RPSGTs).

8. The Sample Size for the Training Set

• The document does not explicitly state the sample size for the training set. The sample sizes provided (N=100 Adult, N=47 Pediatric, N=100 RR) refer specifically to the test sets used for clinical validation, described as "study sample[s]... to construct the final study sample."

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

• Since the training set size and characteristics are not detailed, the method for establishing its ground truth is also not described in this document. It is implied that the algorithm was trained on prior data, but the specifics of that process are outside the scope of this 510(k) summary, which focuses on the validation of the final product.

Ask a specific question about this device

EnsoSleep is intended for use for the diagnostic evaluation by a physician to assess sleep quality and as an aid for the diagnosis of sleep and respiratory related sleep disorders in adults only. EnsoSleep is a software-only medical 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, leg movements, and sleep disordered breathing events including obstructive apneas. All automatically scored events are subject to verification by a qualified clinician. Central apneas, mixed apneas, and hypopneas must be manually marked within records.

EnsoSleep is a software application that analyzes previously recorded physiological signals obtained during sleep. The EnsoSleep software can analyze any EDF or EDF+ files.

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 Stage Events

• Wake
• Stage N1
• Stage N2
• Stage N3
• Stage REM

Respiratory Events

• Sleep disordered breathing (apneas and hypopneas)
• Apneas detected with airflow signal are classified as obstructive apnea (OSA), and can be edited to be central or mixed appeas
• Sleep disordered breathing events not detected to be apneas are marked as hypopnea
• Central apneas, mixed apneas, and hypopneas must be manually marked within records

Arousal Events

• Arousals

Movement Events

• Periodic Leg Movements during Sleep (PLMS)

The EnsoSleep software can be used as a stand-alone application for use on Microsoft Windows 7 & 8 operating system platforms. All processing, scoring, and analysis of signal data occurs on the EnsoSleep cloud servers.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria were established competitively based on the performance reported in the predicate device (Advanced Brain Monitoring, Inc. Sleep Profiler, K153412) and the reference device (Younes Sleep Technologies MICHELE Sleep Scoring System, K112102) 510(k) documentation. The study aimed to demonstrate that EnsoSleep's performance was statistically similar to or greater than these benchmarks.

Endpoint 1: Sleep Staging Performance

Conclusion on Endpoint 1: EnsoSleep showed no statistically significant differences or was significantly greater than the predicate device in all comparisons for sleep staging performance.

Endpoint 2: Sleep Apnea Diagnostic Agreement Performance

Conclusion on Endpoint 2: EnsoSleep showed no statistically significant differences in PA and NA compared to the predicate device, with one exception for overall-mild PA (a 2% difference in EnsoSleep's CI upper bound vs. predicate point estimate). EnsoSleep's point estimates for PA, NA, and OA exceeded, were equivalent to, or were within 10% of the predicate device's.

Endpoint 3: Event Detection Performance

Conclusion on Endpoint 3: EnsoSleep's point-estimates for PA and NA event detection performance exceeded, were equivalent to, or were within 10% of the reference device, with statistically significant differences observed in a minority of cases.

2. Sample Size for Test Set and Data Provenance

• Sample Size (Test Set): N=72 subjects (59719 epochs for epoch-based analyses, 72 for per-patient analyses).
• Data Provenance: Retrospective clinical PSG data. The data constituted an "archived collection of retrospective diagnostic clinical PSG data collected from an AASM Accredited" facility. The country of origin is not explicitly stated but implied to be the US given the FDA submission.

3. Number of Experts for Ground Truth and Qualifications

• Number of Experts: Three (3) independent registered sleep technologists (RPSGT).
• Qualifications: Registered sleep technologists (RPSGT) who met "all acquisition, scoring-bind, and rater controls." Specific years of experience are not mentioned.

4. Adjudication Method for Test Set

• Adjudication Method: 2/3 Majority Scoring was used to establish the designated comparative reference (ground truth).

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

• No MRMC comparative effectiveness study was done to evaluate human readers with and without AI assistance. The study described is a standalone performance evaluation of the AI algorithm against expert consensus.

6. Standalone (Algorithm Only) Performance Study

• Yes, a standalone study was performed. The entire clinical performance testing described evaluates the EnsoSleep algorithm's performance (without human-in-the-loop) against a human expert consensus ground truth.

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus (2/3 Majority Scoring by three independent RPSGTs).

8. Sample Size for Training Set

• The document does not specify the sample size for the training set. It only describes the validation/test set.

9. How Ground Truth for Training Set Was Established

• The document does not provide information on how the ground truth for the training set was established. It focuses solely on the clinical performance testing (validation/test set).

Ask a specific question about this device