(128 days)
The Dreem 3S is intended for prescription use to measure, record, display, transmit and analyze the electrical activity of the brain to assess sleep and awake in the home or healthcare environment. The Dreem 3S can also output a hypnogram of sleep scoring by 30-second epoch and summary of sleep metrics derived from this hypnogram.
The Dreem 3S is used for the assessment of sleep on adult individuals (22 to 65 years old). The Dreem 3S allows for the generation of user/predefined reports based on the subject's data.
The Dreem 3S headband contains microelectronics, within a flexible case made of plastic, foam, and fabric. It includes 6 EEG electrodes and a 3D accelerometer sensor.
The EEG signal is measured by two electrodes in the frontal position) and two at the back of the head (occipital position), along with one reference electrode and one ground electrode.
The 3D accelerometer is embedded in the top of the headband to ensure accurate measurements of the wearer's head movement during the night. The raw EEG and accelerometer data are transferred to Dreem's servers for further analysis after the night is over.
The device includes a bone-conduction speaker with volume control to provide notifications to the wearer, and a power button circled by a multicolor LED light
The device generates a sleep report that includes a sleep staging for each 30-second epoch during the night. This output is produced using an algorithm that analyzes data from the headband EEG and accelerometer sensors. A raw data file is also available in EDF format.
The provided text is a 510(k) summary for the Dreem 3S device. It does not contain a comprehensive study detailing acceptance criteria and device performance. Instead, it states that no new testing was performed because the current submission is primarily for the inclusion of a Predetermined Change Control Plan (PCCP). It relies on the performance characteristics previously reported for the predicate device (K223539).
Therefore, I cannot provide a table of acceptance criteria with reported performance, or details about the sample sizes and ground truth for a new study, as none was conducted or reported in this document.
However, based on the information for the predicate device, and the intent behind the PCCP, I can infer and summarize what would typically be expected for such a device and what the PCCP aims to maintain:
Inferred Acceptance Criteria based on Predicate Device (K223539) and PCCP:
The document states, "clinical performance validation will also be repeated, and will require that the performance of any modification to Dreem 3S to be non-inferior to the all previously released versions of the Dreem 3S device." This indicates that the primary acceptance criterion for any future algorithmic updates under the PCCP is non-inferiority to the performance established in the original clearance (K223539). While the specific metrics are not detailed in this current summary, for a sleep staging device, these would typically include accuracy metrics like Cohen's Kappa, Sensitivity, Specificity, and overall accuracy for differentiating sleep stages (Wake, NREM1, NREM2, NREM3, REM).
Regarding Study Information (based on the original clearance of K223539, not detailed here):
Since the provided document explicitly states, "No bench testing, animal testing, or clinical testing was performed to support this submission," I cannot fill in the details for a new study. The performance information relates to the predicate device (K223539).
However, based on the Predetermined Change Control Plan (PCCP) section, which outlines how future algorithmic modifications will be validated, I can describe the methodology for future performance validation under that plan:
Inferred Acceptance Criteria and Future Performance Validation Methodology (based on PCCP)
1. Table of Acceptance Criteria and Reported Device Performance:
| Acceptance Criterion (Inferred from PCCP) | Reported Device Performance (From K223539 - Not detailed in this document) |
|---|---|
| Non-inferiority of sleep staging performance to previously cleared versions | Specific performance metrics (e.g., Kappa, Accuracy, Sensitivity, Specificity for sleep stages) measured in K223539. |
| Maintain performance across specific sleep stages (Wake, N1, N2, N3, REM) | Specific performance metrics for each stage from K223539. |
| Robustness to signal preprocessing, ML model, and postprocessing updates | Performance maintained within non-inferiority margins after updates. |
Note: The actual numerical performance metrics for the predicate device (K223539) are not provided in this document. They would have been part of the original K223539 submission. The PCCP ensures that future algorithmic changes meet these same (or non-inferior) performance levels.
2. Sample Size Used for the Test Set and Data Provenance:
- For future updates under PCCP: The PCCP states, "Recordings that are used for any purpose (e.g., training, tuning, failure analysis, etc.) that might lead to direct or indirect insight regarding the performance of a modified sleep staging algorithm on this recording, other than execution of the clinical performance validation per the methods specified in the PCCP, are excluded from the test dataset." This implies that a new, independent test set will be used for each validation under the PCCP.
- Sample Size: Not specified for future PCCP validations, but it is stated that "Quality checks will ensure that the test data are sufficiently high quality and representative of the intended use population."
- Data Provenance: Not explicitly stated, but for sleep studies, typically involves polysomnography (PSG) data. The "human variability estimated from comparison of expert scoring from 284 American Academy of Sleep Medicine (AASM) compliant polysomnography recordings" suggests a U.S. or internationally recognized standard for data interpretation. The fact that the device assesses adult individuals (22 to 65 years old) means the test set would be composed of data from this age demographic. Retrospective or prospective is not specified, but typically retrospective datasets are used for initial clearances.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
- For future updates under PCCP: "Non-inferiority margins were selected based on the level of human variability estimated from comparison of expert scoring from 284 American Academy of Sleep Medicine (AASM) compliant polysomnography recordings." This strongly implies that the ground truth for validation (both for K223539 and subsequent PCCP validations) is expert consensus scoring based on AASM guidelines.
- Number of Experts: Not explicitly stated, but "expert scoring" typically implies one or more certified sleep technologists or sleep physicians. The mention of "human variability" often means comparison between at least two independent expert scorings.
- Qualifications: "American Academy of Sleep Medicine (AASM) compliant polysomnography recordings" strongly suggests that the experts would be board-certified sleep physicians or registered polysomnographic technologists (RPSGTs) with experience in AASM sleep staging. The number of years of experience is not specified.
4. Adjudication Method for the Test Set:
- Not explicitly defined in the provided text. However, for "expert scoring" and estimating "human variability," common adjudication methods include:
- Consensus: Multiple experts independently score, and a final consensus is reached (e.g., by discussion or a third adjudicator if initial scores differ significantly).
- Majority vote: If more than two experts, the majority decision prevails.
- Pairwise agreement: Often used to quantify inter-rater variability for tasks like sleep staging.
5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study:
- The document does not report on an MRMC comparative effectiveness study where human readers improve with AI vs. without AI assistance for this specific submission (K242094). This submission is for a PCCP and relies on the predicate's performance.
6. Standalone (Algorithm Only) Performance Study:
- Yes, the document implies that a standalone performance study was conducted for the predicate device (K223539). The algorithm "analyzes data from the headband EEG and accelerometer sensors" and "uses raw EEG data and accelerometer data to provide automatic sleep staging according to the AASM classification." The PCCP is about maintaining and improving this algorithm's standalone performance.
- The "clinical performance validation will also be repeated, and will require that the performance of any modification to Dreem 3S to be non-inferior" to previous versions. This directly refers to the algorithm's standalone performance.
7. Type of Ground Truth Used:
- Expert Consensus: The phrase "automatic sleep staging according to the AASM classification" and "comparison of expert scoring from 284 American Academy of Sleep Medicine (AASM) compliant polysomnography recordings" strongly indicates that the ground truth is established by expert scoring conforming to AASM guidelines. This is the standard for sleep staging.
8. Sample Size for the Training Set:
- Not specified in this document. This refers to the original training data used for the predicate device (K223539). For future updates, the PCCP mentions "Retraining with an updated training/tuning dataset" but does not specify the size of these datasets.
9. How the Ground Truth for the Training Set Was Established:
- Not explicitly specified for the training set itself, but it is highly probable that the ground truth for the training set was established through expert consensus scoring according to AASM guidelines, similar to how the test set's ground truth is (or will be for PCCP updates) established. This is standard practice for supervised machine learning models in this domain.
§ 882.1400 Electroencephalograph.
(a)
Identification. An electroencephalograph is a device used to measure and record the electrical activity of the patient's brain obtained by placing two or more electrodes on the head.(b)
Classification. Class II (performance standards).