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

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

Apple acquired Beddit, a Finnish technology company, back in 2017. Since that time, Apple has filed patents to improve the Beddit system (01, 02, 03 and more). Besides bed device, Apple has launched a sleep app for Apple Watch and iPhone. On Thursday, the US Patent and Trademark Office published a patent application from Apple titled “Sleep Staging using Machine Learning.” This is a patent that medical professionals will appreciate.

One of the inventors who worked on this patent was Matt Bianchi MD PhD, Double-Board in Neurology and Sleep Medicine currently at Apple Health Technologies. Another listed on the patent is Alexander Chan, Ph.D. in Medical Engineering with a focus in neuroscience who led the Health Technology Algorithms/Data Science team developing 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 constantly active even when the patient is asleep. Because sleeping with scalp electrodes can be cumbersome, other sensors have been developed to monitor sleep patterns, such as wearable devices and in-bed sensors.

Wearable devices are typically worn on the wrist, legs, or chest and include motion sensors (eg, accelerometers) to detect 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 small body movements that occur when a user breathes, or their heart rate. The sensor data can be entered into a sleep adjustment application installed on a smartphone or other device.

The sleep phase app calculates various sleep statistics, 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 app let the users know how to get more sleep.

While Apple’s patent application supports the “Beddit” sleep tracker, they are 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 movement signals and respiratory signals from a user; extracting, with the at least one processor, characteristics of 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 estimate the amount of sleep they are getting. get to improve, and to allow the sleep/wake tracking app to guide users on how to get more sleep.

Apple’s patent FIG. 2 is a conceptual block diagram of a sleep/wake classification system that includes a machine learning classifier; FIG. 4 is a flow diagram of a feature extraction process using 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 working in related medical fields will appreciate checking the details of Apple’s patent application US 20220386944 A1.

10.51FX - Patent Application Bar

Leave a Reply

Your email address will not be published. Required fields are marked *