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An Apple Patent published this week describes the use of Machine Learning within a new Sleep Stage Tracking System

1 cover Apple sleep stage tracking sysetm with ML - Patently Apple IP report


Apple acquired Beddit, a Finnish technology company, back in 2017. Since that time, Apple has been filing patents to improve the Beddit system (01, 02, 03 & more). Beyond Bed apparatus, Apple has launched a sleep app for Apple Watch and iPhone. On Thursday, the US Patent & Trademark Office published a patent application from Apple titled "Sleep Staging using Machine Learning." It's a patent that medical professionals will appreciate.


One of the inventors that worked on this patent was Matt Bianchi MD PhD, Double- Boarded in Neurology and Sleep Medicine currently with Apple Health Technologies. Another listed on the patent is Alexander Chan, Ph.D. in Medical Engineering with a focus in neuroscience that led the Health Technologies Algorithms/Data Science team that develops machine learning and signal processing algorithms to extract health information from sensors for new health/wellness features in new and existing Apple products.


To understand a patient's sleep patterns, doctors will typically perform objective sleep staging by monitoring Electroencephalographic (EEG) activity during sleep. An EEG is a test that detects electrical activity in the brain using electrodes attached to the scalp. A patient's brain cells communicate using electrical impulses and are active all the time even when the patient is asleep. Because sleeping with electrodes attached to the scalp can be cumbersome, other sensors for monitoring sleep patterns have been developed, such as wearable devices and in bed sensors.


Wearable devices are typically worn on the wrist, legs or chest and include motion sensors (e.g., accelerometers) for tracking movements at those locations. In-bed sensors are typically placed under a bed sheet and include sensors that can track breathing and heart rate by measuring tiny body movements that occur when a user breathes, or their heart beats. The sensor data can be input into a sleep staging application installed on a smartphone or other device.


The sleep staging application computes various sleep metrics, such as total sleep/wake time and sleep efficiency, which can be used to quantify sleep to help users improve the amount of sleep they get, and to allow the sleep/wake tracking application to coach the users on how to get more sleep.


While Apple's patent application supports the "Beddit" sleep tracker, they're adding a new approach to that system that involves machine learning.  


In one embodiment, a method comprises: receiving, with at least one processor, sensor signals from a sensor, the sensor signals including at least motion signals and respiratory signals of a user; extracting, with the at least one processor, features from the sensor signals; predicting, with a machine learning classifier, that the user is asleep or awake based on the features; and computing, with the at least one processor, a sleep or wake metric based on whether the user is predicted to be asleep or awake.


Machine learning is used to improve prediction of sleep/wake states that can be used by a sleep/wake tracking application to generate a variety of sleep metrics that can be used to quantify sleep to help users improve the amount of sleep they get, and to allow the sleep/wake tracking application to coach users on how to get more sleep.


Apple's patent FIG. 2 is a conceptual block diagram of a sleep/wake classification system which includes a machine learning classifier; FIG. 4 is a flow diagram of a feature extraction process that uses a sequencer/machine learning classifier; FIG. 5 is a flow diagram of a classification process for predicting sleep/wake probabilities; FIG. 6 is a flow diagram of a sleep/wake process.


2 Apple Sleep technology patent figs 2  4  5 & 6 - Patently Apple Patent


Those who work in related Medical Fields will appreciate reviewing the details of Apple's patent application US 20220386944 A1.


10.51FX - Patent Application Bar


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