Author: Dave Muoio In an effort to tackle in-home cardiac arrest, University of Washington researchers have devised a novel contactless system that uses smartphones or voice-based personal assistants to identify telltale breathing patterns that accompany an attack. The proof-of-concept strategy, described in an NPJ Digital Medicine paper published this morning, involved a supervised machine learning model called a support-vector machine that was trained for use in the bedroom, a controlled environment in which the majority of in-home cardiac arrests occur. “Sometimes reported as ‘gasping’ breaths, agonal respirations may hold potential as an audible diagnostic biomarker, particularly in unwitnessed cardiac arrests that occur in a private residence, the location of [two-thirds] of all [out-of-hospital cardiac arrests],” the researchers wrote. “The widespread adoption of smartphones and smart speakers (projected to be in 75% of US households by 2020) presents a unique opportunity to identify this audible biomarker and connect unwitnessed ...
your submission has already been received.
Please enter a valid Email address！
The most relevant industry news & insight will be sent to you every two weeks.
WordPress database error: [Table 'wp_posts' is marked as crashed and should be repaired]
SELECT SQL_CALC_FOUND_ROWS wp_posts.ID FROM wp_posts LEFT JOIN wp_term_relationships ON (wp_posts.ID = wp_term_relationships.object_id) WHERE 1=1 AND (
wp_term_relationships.term_taxonomy_id IN (1,54)
) AND wp_posts.post_type = 'post' AND (wp_posts.post_status = 'publish') GROUP BY wp_posts.ID ORDER BY wp_posts.post_date DESC LIMIT 0, 10