Smart speakers like Alexa and Google Home can act as remotely operating systems that monitor a person's breathing and accurately pick up agonal breathing of cardiac arrest and immediately alert family or calls emergency services for help. The tool was 97% accurate in detecting agonal breathing from a distance of up to 20 feet.

The proof of concept tool using artificial intelligence was developed by scientists at the University of Washington, in a study led by first author Justin Chan, a doctoral student and co-corresponding author Shyam Gollakota, an associate professor at Paul G. Allen School of Computer Science & Engineering, University of Washington.

The findings of the study appear in npj Digital Medicine.

What is Agonal Breathing?

Agonal breathing occurs when a patient experiences really low oxygen levels and sounds like a sort of a guttural gasping noise, which is characteristic making it a good audio biomarker for someone having a cardiac arrest.

Training AI Tool to Identify Agonal Breathing of Cardiac Arrest

• The team collected sounds of agonal breathing from actual 911 calls to Seattle's Emergency Medical Services
• Since patients suffering from cardiac arrest are often unconscious, bystanders recorded the agonal breathing sounds by placing their phones near the patient's mouth while calling emergency services
• The team obtained recordings from 162 calls between 2009 and 2017 and extracted 2.5 seconds of audio at the beginning of each agonal breath and came up with 236 audio clips
• They then captured the audio clips on various smart devices including an Amazon Alexa, a Samsung Galaxy S4 and iPhone 5s and employed machine learning technology to increase the dataset to 7316 positive audio clips
• The team played the audio clips from different locations in the bedroom to simulate how the sound would be from various distances and also included interfering sounds such as cars honking, the sound of the air conditioner or a dog's bark that would normally occur in a home
• This was done for comparison so that the AI system could differentiate positive and negative agonal breathing accurately, the team fed a negative dataset comprising 83 hours of audio data collected during sleep studies, and obtained a total of 7,305 sound samples. These clips contained usual sounds people make during sleep, such as snoring or obstructive sleep apnea
• Using the positive and negative data sets, the research team employed machine learning to produce a tool that could identify agonal breathing 97% of the time when the smart speaker was located up to 6 meters away from the person sleeping
• Additionally, the team tested the algorithm to make sure that the tool would not identify normal heavy breathing or snoring mistakenly as agonal breathing

"We don't want to alert either emergency services or loved ones unnecessarily, so it's important that we reduce our false positive rate," said Chan.

• During testing the false positive rate of a breathing sound was 0.14% and false positivity for separate audio clips recorded by normal volunteers was 0.22%
• When the team programmed the tool classify agonal breathing only when it heard two distinct events at minimum 10 seconds interval, the false positivity fell to zero.

The findings of the study suggest that AI working through smart speakers or smartphone apps could be potentially lifesaving in cardiac arrest patients. The team feel this tool could be used as a smartphone app or a skill for Alexa that runs in the background while the person is asleep.

Future Plans

• Improving the accuracy of the algorithm further and make it work across a larger population by obtaining more 911 call data
• Commercialize this tool through a University of Washington spinout, Sound Life Sciences, Inc.

In summary, the findings of the study show how AI tools can be used in our daily lives to detect possibly life-threatening symptoms and alert emergency services for prompt help.

‘Newly developed artificial intelligence (AI) tool can work through a smart speaker such as Alexa and identifies irregular breathing of cardiac arrest in asleep patients and immediately alerts family or emergency care services for immediate help and be potentially lifesaving.’
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