“Alexa, check my heart”: the algorithm to detect cardiac arrest and alert emergency services

An image to illustrate smart speakers which may be able to detect cardiac arrest with an algorithm

Could a smart speaker help to save a life? Researchers are developing an algorithm to detect cardiac arrest in users of products like Amazon Alexa and Google Home.

The algorithm is the first contactless cardiac arrest AI system for smart speakers. The intention is for the speaker to detect breathing abnormalities associated with cardiac arrest and alert the emergency services.

Why use a smart speaker like Alexa?

The co-corresponding author Shyam Gollakota, an associate professor in the UW’s Paul G. Allen School of Computer Science & Engineering, said:  “A lot of people have smart speakers in their homes, and these devices have amazing capabilities that we can take advantage of. We envision a contactless system that works by continuously and passively monitoring the bedroom for an agonal breathing event, and alerts anyone nearby to come provide CPR. And then if there’s no response, the device can automatically call 911.”

Agonal breathing

The researchers gathered sounds of agonal breathing from real emergency calls to Seattle’s Emergency Medical Services and observed that agonal breathing is common to around half of the people who experience cardiac arrests, according to the 911 data.

The “This kind of breathing happens when a patient experiences really low oxygen levels,” said co-corresponding author Dr. Jacob Sunshine, an assistant professor of anesthesiology and pain medicine at the UW School of Medicine. “It’s sort of a guttural gasping noise, and its uniqueness makes it a good audio biomarker to use to identify if someone is experiencing a cardiac arrest.”

The researchers gathered sounds of agonal breathing from real 911 calls to Seattle’s Emergency Medical Services. Because cardiac arrest patients are often unconscious, bystanders recorded the agonal breathing sounds by putting their phones up to the patient’s mouth so that the dispatcher could determine whether the patient needed immediate CPR

How accurately does the algorithm detect cardiac arrest?

The first author and doctoral student Justin Chan explained: “We played these examples at different distances to simulate what it would sound like if it the patient was at different places in the bedroom. We also added different interfering sounds such as sounds of cats and dogs, cars honking, air conditioning, things that you might normally hear in a home.”

“Right now, this is a good proof of concept using the 911 calls in the Seattle metropolitan area. But we need to get access to more 911 calls related to cardiac arrest so that we can improve the accuracy of the algorithm further and ensure that it generalizes across a larger population.”

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