Big data reveals insights for lead importance in ECG interpretation.
Background: Not every lead contributes equally in the interpretation of an ECG. There are some abnormalities in which the lead importance is not clear either from cardiac electrophysiology or experience. Therefore, it is beneficial to develop an algorithm to quantify the lead importance in the readi...
| Publicado en: | Journal of Electrocardiology Vol. 69; pp. 12 - 23 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
W B Saunders
2021 Supplement
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153954901&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153954901 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00220736 1276 jtl: Journal of Electrocardiology issn: 00220736 maglogo: N pubinfo: dt: 2021 Supplement vid: 69 pid: 1351 pub: W B Saunders place: Philadelphia, Pennsylvania artinfo: ui: 153954901 153954901 NLM34579960 10.1016/j.jelectrocard.2021.01.002 NLM34579960 153954901 ppf: 12 ppct: 11 formats: tig: atl: Big data reveals insights for lead importance in ECG interpretation. aug: au: Yang, Ting Gregg, Richard E. Babaeizadeh, Saeed affil: Advanced Algorithm Research Center, Philips Healthcare, 222 Jacobs St, Cambridge, MA 02141, USA sug: subj: Electrocardiography Predictive Value of Tests Algorithms Resource Databases ab: Background: Not every lead contributes equally in the interpretation of an ECG. There are some abnormalities in which the lead importance is not clear either from cardiac electrophysiology or experience. Therefore, it is beneficial to develop an algorithm to quantify the lead importance in the reading of ECGs, namely to determine how much to weigh the evidence from each individual lead when interpreting ECG.Methods: One representative beat per ECG lead was constructed for each ECG in a database. An algorithm was developed to find the top K (K = 1, 5, 10, 20, 50, 100) ECGs in the database that had the most similar morphology to the query ECG, independently for each lead. For each lead, the query ECG was interpreted based on the weighted average voting on the most similar ECGs by applying a variety of thresholds. For each category of abnormality, we found the threshold that maximized the median F1 score of sensitivity and positive predictive value among all ECG leads. Finally, the F1 score of each lead at this chosen threshold was defined as the importance value for that lead.Results: Eighteen morphology-based categories of abnormality were investigated for two databases. For most, the lead importance confirmed what expert ECG readers already know. However, it also revealed new insights. For example, lead aVR appeared in the top 6 most important leads in 11 and 12 categories of abnormality in two databases respectively, and ranked first among 12 leads if summarizing all categories.Conclusions: Lead importance information may be useful in selecting only the most important leads to screen for a specific abnormality, for example using wearable patches. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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