Timely attention to those who are suffering from cardiac arrest often increases their chances of survival, but most people They do not know how to identify the alarm signals.
One of the most obvious signs that a person experiences cardiac arrest is agonizing breathing, An abnormal breathing pattern that is characterized by wheezing and shortness of breath, in addition to strange vocalizations and involuntary agitation of the muscles.
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In this context, a cardiac resuscitation may increase your chances of survival two or three times. This intervention requires that another person present be able to provide assistance as soon as possible. However, statistics show that many times Cardiac arrest occurs when the person is in the privacy of their home. or out of hospital.
For this reason, a team from the University of Washington has developed a digital tool that detect agonist breathing, According to an article published on Wednesday by the journal Digital Medicine, the group Nature.
The tool, which can operate with s"Smart Speaker" Systems such as "Google Home", "Alexa" from Amazon or any other smart phone, allows the device to detect the sound of typical agonist breathingand make a request for help.
During the test stage, in which agonizing breathing sounds picked up in telephone calls to emergency services were used, the tool detected them 97% of the time at a maximum distance of 6 meters.
Since many times patients suffering from cardiac arrest are unconsciousOther people who come to your aid record the sounds of agonizing breathing by placing the phone next to the victim's mouth so emergency personnel can determine if cardiopulmonary resuscitation is necessary.
Researchers at the University of Washington collected 162 leads between 2009 and 2017 and extracted 2.5 seconds of sound at the beginning of each. agonist breathing to complete a total of 236 audio cuts.
To test the tool, the researchers captured the recordings in different Smart devices including an Alexa, an iPhone 5s and a Samsung Galaxy S4and used various computer learning techniques to increase the data set to 7,316 cuts.
Justin Chan, author of the paper and a doctoral student at Allen School, said that these recordings were used at different distances "to simulate how patients would sound in different places in the room."
"We've also added different noise interferences, like cat and dog noises, car horns, air conditioning … things you can usually hear at night," Chan added.
"Many people it has smart speakers in your house and these devices They have incredible capabilities that we can tap into, "said Shyam Gollakota, one of the authors of the project.
"What we have in mind is a non-contact system, which works continuously by passively observing the room to detect a case of agonist breathing, and to alert someone to give cardiopulmonary resuscitation ", he added.
"If there is no immediate response," Gollakota said, "the device can automatically call the emergency service"
With information from EFE
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