Lullaby: A Novel Algorithm to Extract Fetal QRS in Real Time Using Periodic Trend Feature
A fetal heart rate (fHR) is an important indicator for the monitoring of fetal cardiac health and development. The widely used method based on ultrasound; however, is not continuous and often requires an expert to perform; thus, it is mostly used in clinics during checkups. The advances in wearable...
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| Veröffentlicht in: | IEEE sensors letters 2022-09, Vol.6 (9), p.1-4 |
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| creator | Jilani, Daniel Le, Tai Etchells, Tim Lau, Michael P. H. Cao, Hung |
| description | A fetal heart rate (fHR) is an important indicator for the monitoring of fetal cardiac health and development. The widely used method based on ultrasound; however, is not continuous and often requires an expert to perform; thus, it is mostly used in clinics during checkups. The advances in wearable technology have paved the way for the home assessment of the fHR via the extraction of the mother's abdominal electrocardiogram (aECG) acquired by novel patches. Several methods have been developed for such; however, the computation is either too slow for real-time monitoring or too heavy to be performed in a wearable. In this letter, we develop and validate the Lullaby algorithm-a novel method for fetal QRS extraction from the aECG. The results showed that Lullaby is almost seven times faster than the existing methods with a better F1-score of 0.815, holding promise to transform perinatal monitoring. |
| doi_str_mv | 10.1109/LSENS.2022.3200072 |
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The results showed that Lullaby is almost seven times faster than the existing methods with a better F1-score of 0.815, holding promise to transform perinatal monitoring.</description><subject>abdominal electrocardiogram (aECG)</subject><subject>Algorithms</subject><subject>biosignals</subject><subject>Calibration</subject><subject>Electrocardiography</subject><subject>Feature extraction</subject><subject>Fetal heart rate</subject><subject>fetal heart rate (fHR)</subject><subject>fetal QRS (fQRS)</subject><subject>Heart rate</subject><subject>periodic trend feature (PTF)</subject><subject>Real time</subject><subject>Real-time systems</subject><subject>sensor applications</subject><subject>Sensor signal processing</subject><subject>Sensors</subject><subject>signal processing</subject><subject>Ultrasonic testing</subject><subject>Wearable computers</subject><subject>Wearable technology</subject><issn>2475-1472</issn><issn>2475-1472</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkU9v1DAQxS0EolXpF2gvlrhw2WX8J7HdC1pVW0BaFehuDz1ZjjPZukri4iQV_fZ42VUFnDySf-9p5j1CzhjMGQPzcbVeXq_nHDifCw4Air8ix1yqYsak4q__mo_I6TA8ZIRprkDAW3IkVCmUVOaY3K2mtnXV8wVd0Ov4hC1dtNuYwnjf0THS5a8xOT_SKxxdS3_crGno6Q3meRM6pLdD6Lf0O6YQ6-DpJmFfZ9aNU8J35E3j2gFPD-8Jub1abi6_zFbfPn-9XKxmXnI9znjBsVLO1QZAOKZZw5XRJW_qyqGsTF0q8FiCl7qpPEoJRaWNrwUX3LiaixPyae_7OFUd1h77vHJrH1PoXHq20QX7708f7u02PlkGsiiNgezw4eCQ4s8Jh9F2YfCYc-kxToPlutAStBQso-__Qx_ilPp8n83Z6jKfBDtDvqd8isOQsHnZhoHdtWf_tGd37dlDe1l0vhcFRHwR5ChEKbT4DZX0k1M</recordid><startdate>20220901</startdate><enddate>20220901</enddate><creator>Jilani, Daniel</creator><creator>Le, Tai</creator><creator>Etchells, Tim</creator><creator>Lau, Michael P. 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H. ; Cao, Hung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c428t-252eb7aad9003a181f279862fdbae4b9d670ce60c48fbce4405b89cd32329ad23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>abdominal electrocardiogram (aECG)</topic><topic>Algorithms</topic><topic>biosignals</topic><topic>Calibration</topic><topic>Electrocardiography</topic><topic>Feature extraction</topic><topic>Fetal heart rate</topic><topic>fetal heart rate (fHR)</topic><topic>fetal QRS (fQRS)</topic><topic>Heart rate</topic><topic>periodic trend feature (PTF)</topic><topic>Real time</topic><topic>Real-time systems</topic><topic>sensor applications</topic><topic>Sensor signal processing</topic><topic>Sensors</topic><topic>signal processing</topic><topic>Ultrasonic testing</topic><topic>Wearable computers</topic><topic>Wearable technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jilani, Daniel</creatorcontrib><creatorcontrib>Le, Tai</creatorcontrib><creatorcontrib>Etchells, Tim</creatorcontrib><creatorcontrib>Lau, Michael P. 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H.</au><au>Cao, Hung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lullaby: A Novel Algorithm to Extract Fetal QRS in Real Time Using Periodic Trend Feature</atitle><jtitle>IEEE sensors letters</jtitle><stitle>LSENS</stitle><date>2022-09-01</date><risdate>2022</risdate><volume>6</volume><issue>9</issue><spage>1</spage><epage>4</epage><pages>1-4</pages><issn>2475-1472</issn><eissn>2475-1472</eissn><coden>ISLECD</coden><abstract>A fetal heart rate (fHR) is an important indicator for the monitoring of fetal cardiac health and development. The widely used method based on ultrasound; however, is not continuous and often requires an expert to perform; thus, it is mostly used in clinics during checkups. The advances in wearable technology have paved the way for the home assessment of the fHR via the extraction of the mother's abdominal electrocardiogram (aECG) acquired by novel patches. Several methods have been developed for such; however, the computation is either too slow for real-time monitoring or too heavy to be performed in a wearable. In this letter, we develop and validate the Lullaby algorithm-a novel method for fetal QRS extraction from the aECG. The results showed that Lullaby is almost seven times faster than the existing methods with a better F1-score of 0.815, holding promise to transform perinatal monitoring.</abstract><cop>Piscataway</cop><pub>IEEE</pub><pmid>37637479</pmid><doi>10.1109/LSENS.2022.3200072</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0002-1845-0064</orcidid><orcidid>https://orcid.org/0000-0003-4197-7208</orcidid><orcidid>https://orcid.org/0000-0002-9097-3978</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | abdominal electrocardiogram (aECG) Algorithms biosignals Calibration Electrocardiography Feature extraction Fetal heart rate fetal heart rate (fHR) fetal QRS (fQRS) Heart rate periodic trend feature (PTF) Real time Real-time systems sensor applications Sensor signal processing Sensors signal processing Ultrasonic testing Wearable computers Wearable technology |
| title | Lullaby: A Novel Algorithm to Extract Fetal QRS in Real Time Using Periodic Trend Feature |
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