Onera SleepMap (SLEEPMAP)

K253668

Onera B.V. · cleared 2026-03-08 · product code OLZ · Neurology

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
SleepMap is a software- only medical device to be used under the supervision of a clinician to analyze physiological signals and automatically score sleep study recordings, including the staging of sleep, detection of arousals, leg movements, desaturations, obstructive apneas and obstructive hypopneas.
AlgorithmThe Onera SleepMap contains algorithms including a deep learning model (AI-model) for sleep staging and arousals, and rule-based algorithms for desaturation, obstructive apnea, obstructive hypopneas, leg movement, and artifact detection.
source quote (p.6)
The Onera SleepMap contains the following algorithms: - Sleep staging algorithm, a deep learning model (Al-model) which classifies sleep stages, based on EEG, EOG and EMG inputs. - Arousals algorithm, a Convolutional Neural Network (Al-model) which predicts arousals, based on sleep stages, EEG, EOG, EMG, ECG and nasal pressure inputs. - Desaturation algorithm, a rule-based algorithm which detects events of minimum 3% or 4% oxygenation drop based on sleep stages and SpO2 signal inputs. - Apnea (obstructive) detection, a rule-based algorithm which detects ≥90% nasal pressure drops, based on sleep stages and nasal pressure signal inputs. - Hypopneas (obstructive) detection, a rule-based algorithm which detects ≥30% nasal pressure drops based on sleep stages, nasal pressure signal, arousal events, and desaturation event inputs. - Leg Movement algorithm, a rule-based algorithm which detects (repetitive) EMG amplitude increasements, based on sleep stages, EMG signal and respiratory event inputs. - Artifact algorithms, rule-based algorithms which detect artifacts, based on SpO2, EEG, EOG, EMG and heart rate inputs.
Adaptive (vs locked)FDA source did not state this
PCCPFDA source did not state this
Cybersecurity addressedYes
source quote (p.13)
Authentication controls, authorization controls, cryptographic controls, access controls, integrity controls, intrusion & availability monitoring controls, and database controls.

Validation studies (2)

Retrospective clinical

n=98 patients · 2 site(s)

endpoints: sleep staging; arousal event detection; desaturation event detection; obstructive apnea event detection; obstructive hypopnea event detection; leg movement; PLM event detection

standards: EN IEC 62304:2006/A1:2015, EN 82304-1:2017, EN ISO 20417:2021, EN ISO 15223-1:2021

Retrospective clinical

n=215 patients · 2 site(s)

endpoints: HR output accuracy

Reported performance (0 observations)

FDA source did not state a quantitative performance metric — non-reporting is itself the signal.

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

0
recalls in product code, 24mo
2
MAUDE reports in code, 12mo
+20%
vs code's own 3-yr baseline
0
drift signals on this device

Recall and MAUDE counts are product-code-level (reports aren't reliably attributable to one device). Signals are descriptive observables with sources — never a judgment that the device is unsafe or drifting. Snapshot 2026-07-08.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

RIGOR™ Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: radar.healthai.com/precedent/device/K253668