{"id":92439,"date":"2019-06-21T07:23:07","date_gmt":"2019-06-21T14:23:07","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2019\/06\/ai-system-can-identify-cardiac-arrest"},"modified":"2019-06-21T07:23:07","modified_gmt":"2019-06-21T14:23:07","slug":"ai-system-can-identify-cardiac-arrest","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2019\/06\/ai-system-can-identify-cardiac-arrest","title":{"rendered":"AI System Can Identify Cardiac Arrest"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/ai-system-can-identify-cardiac-arrest.jpg\"><\/a><\/p>\n<p>Researchers at the University of Washington have used machine learning to teach an AI system to identify when someone is having a cardiac arrest. The system learned to identify agonal breathing, which occurs when someone gasps for breath during cardiac arrest, with a high degree of accuracy. The technology can be embedded into a variety of listening devices, such as smart speakers or smartphones, to alert authorities and loved ones to someone having a heart attack while they sleep.<\/p>\n<p>Approximately half a million Americans die from cardiac arrest annually. Cardiac arrests often happen while someone is at home in bed. This is particularly dangerous, as there is likely to be no-one around, or no-one awake, to help.<\/p>\n<p>Now, researchers have developed an AI system that can work through smart speakers or a smartphone to monitor for signs of a cardiac arrest while someone sleeps. The system listens for something called agonal breathing, which occurs in about 50% of people who experience a cardiac arrest, and patients who demonstrate this characteristic gasping often have a better chance of surviving.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers at the University of Washington have used machine learning to teach an AI system to identify when someone is having a cardiac arrest. The system learned to identify agonal breathing, which occurs when someone gasps for breath during cardiac arrest, with a high degree of accuracy. The technology can be embedded into a variety [\u2026]<\/p>\n","protected":false},"author":396,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1512,6],"tags":[],"class_list":["post-92439","post","type-post","status-publish","format-standard","hentry","category-mobile-phones","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/92439","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/396"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=92439"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/92439\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=92439"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=92439"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=92439"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}