US 2026/0294334 A1Published application

Adaptive sleep stage detection

OwnerEdwin Ehsan Kayinventor

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Abstract

The embodiments disclose an adaptive system and method for determining sleep states using multimodal physiological data are disclosed. Physiological signals, including photoplethysmography and motion data, are processed to generate sleep stage probability distributions across dynamically determined time intervals. In response to detecting physiological transient events, the system re-segments selected intervals into shorter sub-epoch windows to increase temporal resolution and reduce signal distortion. The system modifies sleep stage probability distributions and transition behavior within the window and performs probabilistic sequence optimization to determine a most-likely sequence of sleep states. The method improves classification accuracy by integrating user-validated events with signal-driven models and supports adaptive control of sensor operation.

Claims20

Dependent claims

A computer-implemented method for determining sleep states of a subject, comprising:

receiving, via one or more sensors of a wearable device, multimodal physiological data including at least photoplethysmography (PPG) signals and motion signals;
segmenting the multimodal physiological data into a plurality of primary time intervals;
determining, by one or more processors, a duration of each primary time interval using an artificial intelligence model trained on a historical sleep profile of the subject;
detecting, within a first primary time interval, a physiological transient event based on a correlation or divergence between a PPG-derived physiological metric and a motion-derived metric exceeding a dynamically determined threshold;
re-segmenting the first primary time interval into at least one sub-epoch window centered on the physiological transient event, wherein the sub-epoch window has a duration shorter than the primary time interval;
calculating a signal quality metric for at least the PPG signals within the sub-epoch window;
extracting features from the sub-epoch window and generating a plurality of sleep state probability distributions for the sub-epoch window;
modifying the plurality of sleep state probability distributions by scaling contributions of features derived from the PPG signals based on the signal quality metric;
and
assigning a sleep state to the sub-epoch window based on the modified plurality of sleep state probability distributions.

7 more elements

A system for sleep stage classification, comprising:

a wearable sensor array configured to collect physiological data;
one or more processors;
and
a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to:
generate an initial sleep stage sequence based on the physiological data and a global transition probability model;
receive a timestamped user-generated event signal associated with a user-perceived or system-assisted sleep-related event;
define a temporal window surrounding a timestamp associated with the user-generated event signal;
generate a localized transition probability model specific to the temporal window, wherein the localized transition probability model modifies transition probabilities relative to the global transition probability model only within the temporal window;
and
update the initial sleep stage sequence by executing a probabilistic sequence optimization process over the temporal window using the localized transition probability model to determine a most-likely sequence of hidden sleep states,
wherein the updating of the sleep stage sequence is performed without modifying the global transition probability model outside the temporal window, and
wherein the user-generated event signal is applied as a constraint on a probabilistic inference process.

9 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/633,972Filed 30 Mar 2026
Priority: 30 Mar 2025
Published: 1 Oct 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification7 codes

A61B 5/4812
Sleep evaluation: detecting sleep stages or cycles
A61B 5/11
Measuring devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes: measuring…
A61B 5/4815
Sleep evaluation: sleep quality
A61B 5/7221
Signal processing specially adapted for physiological signals or for diagnostic purposes: determining signal validity, reliability or quality