[{"innerBlocks":[],"name":"core\/freeform","postId":780,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Researchers at the University of Washington have developed a tool that monitors people at home for cardiac arrest while they\u2019re sleeping by capturing audio samples.

The tool, which uses smart speakers such as Amazon Alexa and Google Home, is trained to detect agonal respiration, an abnormal pattern of breathing associated with about half of people who experience cardiac arrests.

\u201cA lot of people have smart speakers in their homes, and these devices have amazing capabilities that we can take advantage of,\u201d says Shyam Gollakota, an associate professor in the UW\u2019s Paul G. Allen School of Computer Science and Engineering. \u201cWe 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\u2019s no response, the device can automatically call 911.\u201d

Gollakota and his colleagues, who plan to commercialize the technology through a UW spinout, published results of a proof-of-concept<\/a> in the journal npj Digital Medicine<\/i>.

\u201cWe demonstrate that a support vector machine (SVM) can classify agonal breathing instances in real-time within a bedroom environment,\u201d state the authors. \u201cUsing real-world labeled 911 audio of cardiac arrests, we train the SVM to accurately classify agonal breathing instances.\u201d

In fact, the tool was able to accurately detect agonal breathing 97 percent of the time. In addition, the algorithm incorrectly categorized a breathing sound as agonal breathing only 0.14 percent of the time.

\u201cThis is a good proof of concept using the 911 calls in the Seattle metropolitan area,\" says Gollakota. \u201cBut 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.\u201d

Ultimately, researchers believe the algorithm could be used as an app or a skill for Alexa that runs passively on a smart speaker.

\u201cThe increasing adoption of commodity smart speakers in private residences and hospital environments may provide a wide-reaching means to realize the potential of a contactless cardiac arrest detection system,\u201d conclude the authors.","saveContent":"Researchers at the University of Washington have developed a tool that monitors people at home for cardiac arrest while they\u2019re sleeping by capturing audio samples.

The tool, which uses smart speakers such as Amazon Alexa and Google Home, is trained to detect agonal respiration, an abnormal pattern of breathing associated with about half of people who experience cardiac arrests.

\u201cA lot of people have smart speakers in their homes, and these devices have amazing capabilities that we can take advantage of,\u201d says Shyam Gollakota, an associate professor in the UW\u2019s Paul G. Allen School of Computer Science and Engineering. \u201cWe 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\u2019s no response, the device can automatically call 911.\u201d

Gollakota and his colleagues, who plan to commercialize the technology through a UW spinout, published results of a proof-of-concept<\/a> in the journal npj Digital Medicine<\/i>.

\u201cWe demonstrate that a support vector machine (SVM) can classify agonal breathing instances in real-time within a bedroom environment,\u201d state the authors. \u201cUsing real-world labeled 911 audio of cardiac arrests, we train the SVM to accurately classify agonal breathing instances.\u201d

In fact, the tool was able to accurately detect agonal breathing 97 percent of the time. In addition, the algorithm incorrectly categorized a breathing sound as agonal breathing only 0.14 percent of the time.

\u201cThis is a good proof of concept using the 911 calls in the Seattle metropolitan area,\" says Gollakota. \u201cBut 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.\u201d

Ultimately, researchers believe the algorithm could be used as an app or a skill for Alexa that runs passively on a smart speaker.

\u201cThe increasing adoption of commodity smart speakers in private residences and hospital environments may provide a wide-reaching means to realize the potential of a contactless cardiac arrest detection system,\u201d conclude the authors.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

Smart speakers serve as cardiac arrest detection system

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