{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:13:45Z","timestamp":1756253625810,"version":"3.44.0"},"reference-count":35,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG065636","R01AG069895","RF1 AG067924","U01AG073079"],"award-info":[{"award-number":["R01AG065636","R01AG069895","RF1 AG067924","U01AG073079"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016204","name":"Minnesota Supercomputing Institute, University of Minnesota","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100016204","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Stat Anal Data Min: An ASA Data Sci Journal"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Atrial fibrillation (AF), a cardiac arrhythmia characterized by an abnormal and rapid heartbeat, has the potential to develop into stroke, heart failure, and, ultimately, mortality. The electrocardiogram (ECG) is a pivotal tool in the diagnosis of AF, offering a quick, cost\u2010effective, and non\u2010invasive mean to record the heart's electrical activity. Recent studies are increasingly engaged in the implementation of deep learning techniques for ECG feature extraction for AF prediction. In addition, the application of Mendelian randomization (MR) methodologies has been investigated to identify causal associations between genetically imputed pre\u2010defined ECG characteristics and cardiovascular diseases, such as AF. DeepFEIVR, a non\u2010linear extension of the classical instrumental variable (IV) regression model, was designed with the objective of extracting disease\u2010associated causal features from high\u2010dimensional data, such as neuroimaging data. In this article, we applied DeepFEIVR as well as its variant (with residual inclusion), DeepFEIVR\u2010RI, to the large UK Biobank dataset. The application of DeepFEIVR and DeepFEIVR\u2010RI showed that the genetic components in ECGs could contribute to the development of AF statistically significantly (<jats:italic>p<\/jats:italic> values &lt; ). Another contribution of this article is an extension to both DeepFEIVR and DeepFEIVR\u2010RI to accommodate a large number of IVs. A comparison of results from DeepFEIVR and DeepFEIVR\u2010RI, based on various choices of IVs, was conducted. Furthermore, we applied a recent algorithm called dnn\u2010loc, enabling a visual examination on specific ECG components as extracted causal features for AF, thus advancing the understanding of the etiology of AF.<\/jats:p>","DOI":"10.1002\/sam.70026","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T07:45:21Z","timestamp":1751442321000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Extracting Genetically\u2010Imputed Causal Features From <scp>ECG<\/scp> Data"],"prefix":"10.1002","volume":"18","author":[{"given":"Yuchen","family":"Yao","sequence":"first","affiliation":[{"name":"School of Statistics University of Minnesota  Minneapolis Minnesota USA"},{"name":"Division of Biostatistics and Health Data Science, School of Public Health University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8723-4392","authenticated-orcid":false,"given":"Zhaotong","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Statistics Florida State University  Tallahassee Florida USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotong","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Statistics University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin Yee","family":"Chen","sequence":"additional","affiliation":[{"name":"Cardiovascular Division, Department of Medicine University of Minnesota Medical School  Minneapolis Minnesota USA"},{"name":"Lillehei Heart Institute University of Minnesota Medical School  Minneapolis Minnesota USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1159-0582","authenticated-orcid":false,"given":"Wei","family":"Pan","sequence":"additional","affiliation":[{"name":"Division of Biostatistics and Health Data Science, School of Public Health University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering7010016"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.285.18.2370"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S47385"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.113.005119"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurjpc\/zwaf138"},{"issue":"2","key":"e_1_2_9_7_1","first-page":"130","article-title":"Is Pulse Palpation Helpful in Detecting Atrial Fibrillation? A Systematic Review: Particular High\u2010Risk Patients May Benefit From Repeated Testing","volume":"55","author":"Cooke G.","year":"2006","journal-title":"Journal of Family Practice"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/clc.22667"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/aaf35b"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0386-x"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2005.1615314"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNNB.2005.1614807"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-021-01736-y"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.120.047829"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15432-4"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/ehjdh\/ztae081"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01418-9"},{"key":"e_1_2_9_18_1","doi-asserted-by":"crossref","unstructured":"V. V.Kuznetsov V. A.Moskalenko andN. Y.Zolotykh \u201cElectrocardiogram Generation and Feature Extraction Using a Variational Autoencoder \u201d2020.","DOI":"10.3389\/fgene.2021.638191"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1009862"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1003572"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-36997-w"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.9824"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001779"},{"issue":"3","key":"e_1_2_9_24_1","first-page":"2019","article-title":"Data\u2010Adaptive Discriminative Feature Localization With Statistically Guaranteed Interpretation","volume":"17","author":"Dai B.","year":"2022","journal-title":"Annals of Applied Statistics"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhealeco.2007.09.009"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.117347"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-09718-5"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-05473-8"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611970104"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv546"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature15393"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.15171\/jcvtr.2015.33"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyt093"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/anec.12751"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3367"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3506"}],"container-title":["Statistical Analysis and Data Mining: An ASA Data Science Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/sam.70026","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T11:47:11Z","timestamp":1756208831000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/sam.70026"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["10.1002\/sam.70026"],"URL":"https:\/\/doi.org\/10.1002\/sam.70026","archive":["Portico"],"relation":{},"ISSN":["1932-1864","1932-1872"],"issn-type":[{"type":"print","value":"1932-1864"},{"type":"electronic","value":"1932-1872"}],"subject":[],"published":{"date-parts":[[2025,7,2]]},"assertion":[{"value":"2025-01-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-26","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-02","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70026"}}