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Sleepiz One+ is a contactless medical device intended to measure heart rate and respiration rate in adult patients, at rest or during sleep (in non-motion condition) and to detect patient presence and body movements.

The Sleepiz One+ hardware unit is intended to be used by a healthcare professional when the recordings are performed in a clinical setting, or by patients or their caregivers when the recordings are performed in a home environment. The Sleepiz One+ cloud software is intended for use by healthcare professionals. This device is not indicated for active patient monitoring, as it does not provide alarms for timely response in life-threatening situations. It is not indicated for use on pregnant women.

Sleepiz One+ is a contactless medical device that uses radar technology to measure respiration rate and heart rate from a resting or sleeping patient.

The Sleepiz One+ consists of a hardware unit and cloud-based software. The hardware unit can be positioned on a bedside table, mounted on a stand, or attached to the wall behind the patient's bed. It is designed to monitor physiological signals by detecting small body movements, such as those caused by breathing and heartbeat, using Doppler radar. The recorded signals are then transmitted via Wi-Fi to cloud-based software, where they are analyzed to obtain respiration rate, heart rate, and body movement. These outputs can be exposed via the Application Programming Interfaces (APIs) to allow healthcare professionals the review and annotation of the data and compilation of results into reports.

Outputs:

• Breathing pattern
• Instantaneous breathing rate [breaths per minute]
• Breathing rate statistics (10th, 50th, and 90th quantiles) [breaths per minute]
• Body movement
• Time in bed [hours]
• Presence detection
• Heart rate [beats per minute]
• Heart rate statistics (10th, 50th, and 90th quantiles) [beats per minute]

The overall system can be grouped into 4 major components, which are classified on the basis of the logical component interfaces where data exchange is occurring:

• Sleepiz Hardware - This is a hardware component serving as primary data acquisition device.

• Embedded Software - This encompasses the firmware running of the Sleepiz Hardware. This, together with Sleepiz hardware, is responsible for data acquisition. The embedded software forms the crux of the Sleepiz hardware such that it defines and controls the data acquisition process. The security aspects related to the operation of the device are incorporated in the design and implementation of embedded software.

• Sleep Analytics Software - The sleep analytics software is responsible for processing data from the Sleepiz Hardware and returning its analytics (e.g., breathing rate, heart rate), as well as its statistics (e.g., mean breathing rate, total recording time, etc.). This refers to the ML model deployed within the cloud software. By itself, the Sleep Analytics Software does not have an external interface. It is wholly encapsulated by the cloud software component Data Processing Layer.

• Cloud Software - The cloud software can be divided into the backend service and the analytics service. The backend service includes modules for data ingestion, a public API, a private API, and a module for sending analysis process requests. The analytics service is responsible for receiving analysis requests and interacting with the sleep analytics software.

The provided FDA 510(k) clearance letter for the Sleepiz One+ (Model 2.5) indicates that no new non-clinical or clinical testing was required for this specific submission (K253388). The device is stated to be identical in design, materials, intended use, and technological characteristics to its predicate device (Sleepiz One+, K251364). Therefore, the information regarding acceptance criteria and a study to prove the device meets these criteria would pertain to the predicate device's clearance.

Based on the provided text, we can infer the following about acceptance criteria and the study that would have supported the predicate device (Sleepiz One+, K251364):

Since the current clearance states "The previous testing and clinical evidence supporting the predicate remain applicable," the information requested relates to what would have been submitted for the predicate.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list the acceptance criteria or reported device performance for the predicate device. However, based on the stated intended use and functionality, the performance would likely be evaluated based on the accuracy of:

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

This information is not provided in the current 510(k) document. For the predicate device, a clinical study would have been conducted to validate the accuracy of heart rate and respiration rate measurements against a gold standard. The sample size would depend on statistical power requirements to demonstrate equivalence or non-inferiority to the reference device. The data provenance (e.g., country of origin, retrospective/prospective) would also be detailed in the predicate's submission.

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

This information is not provided in the current 510(k) document. For the predicate device, if ground truth involved human interpretation (e.g., for certain types of sleep events, though unlikely for automated HR/RR), the number and qualifications of experts would be specified (e.g., board-certified pulmonologists or sleep specialists with X years of experience). For HR/RR, the "ground truth" would more likely come from concurrent measurements from established medical devices.

4. Adjudication Method for the Test Set

This information is not provided in the current 510(k) document. For the predicate device, if multiple experts were involved in establishing ground truth (which is less likely for direct physiological measurements like HR/RR), the adjudication method (e.g., 2+1, 3+1 consensus, majority vote) would be detailed. For physiological measurements, adjudication typically involves comparing automated readings against a validated reference device.

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

This information is not provided in the current 510(k) document. An MRMC study is typically performed for AI devices that assist human readers in interpreting complex images or signals. Since the Sleepiz One+ is a direct measurement device (heart rate, respiration rate, presence, movement), an MRMC study comparing human readers with and without AI assistance is unlikely to be relevant for its primary functions. The device directly outputs physiological parameters, not interpretations for human review.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Based on the device description, where "recorded signals are then transmitted via Wi-Fi to cloud-based software, where they are analyzed to obtain respiration rate, heart rate, and body movement," a standalone performance study would have been conducted for the predicate device. The study would evaluate the algorithm's accuracy in autonomously determining heart rate, respiration rate, presence, and movement against a ground truth.

7. Type of Ground Truth Used

For the predicate device, the ground truth for heart rate and respiration rate measurements would most likely be established using validated medical-grade reference devices (e.g., electrocardiogram (ECG) for heart rate, capnography or pneumotachograph for respiration rate) concurrently with the Sleepiz One+ device. For presence and body movement, the ground truth could be established by observation, video recording, or other motion sensors.

8. Sample Size for the Training Set

This information is not provided in the current 510(k) document. For the predicate device, data from human subjects would have been collected to train the machine learning models (specifically the "Sleep Analytics Software / ML model deployed within the cloud software"). The size and diversity of this training set are critical for the model's generalizability and accuracy.

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

This information is not provided in the current 510(k) document. Similar to the test set, the ground truth for the training set for the predicate device would have been established by concurrent recording with validated medical-grade reference devices for heart rate and respiration rate. This would involve recording signals from patients simultaneously with both the Sleepiz device and the reference device, allowing for the precise labeling of heartbeats, breaths, and movements in the training data.

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