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Meas."],"published-print":{"date-parts":[[2025,11,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Electrocardiography and blood pressure (BP)\n\t\t\t\t\tmeasurement are two widely used tools for diagnosis and monitoring\n\t\t\t\t\tcardiovascular diseases. While the electrocardiogram (ECG) and BP have been\n\t\t\t\t\tconsidered complementary modalities, there are also systematic relationships\n\t\t\t\t\tbetween them. Therefore, advancements in portable and wearable ECG devices,\n\t\t\t\t\talong with promising results in cuff-less BP measurement using a combination of\n\t\t\t\t\tECG and other bio-signals have led researchers to hypothesize the possibility of\n\t\t\t\t\testimating BP and classifying BP categories (e.g. normal vs. hypertensive) using\n\t\t\t\t\tonly ECG. However, the literature is divided on this topic: some studies support\n\t\t\t\t\tthis hypothesis, while others reject it.\n                    <jats:italic>Approach.<\/jats:italic>\n                    In this\n\t\t\t\t\tstudy, regression and classification machine learning (ML) models were developed\n\t\t\t\t\tto explore the feasibility of estimating BP and predicting BP categories (normal\n\t\t\t\t\tvs. hypertensive) from 30 s ECGs using an extensive dataset from AliveCor Inc.\n\t\t\t\t\twhich includes 124 427 records from 7412 subjects. The ECG and BP recordings\n\t\t\t\t\twere asynchronous with variable counts and time lags. Therefore, a 3.5 min time\n\t\t\t\t\twindow before and after each ECG recording was used to calculate the mean BP\n\t\t\t\t\tmeasurement. Sex-aware ML models were trained using a comprehensive feature\n\t\t\t\t\tvector comprising 280 features: 128 explainable ECG features developed by the\n\t\t\t\t\tresearch team and 150 ECG features extracted by the Black Swan team, one of the\n\t\t\t\t\ttop-performing teams in the PhysioNet Challenge 2017. Additionally, the average\n\t\t\t\t\ttime gap between each ECG and the corresponding BP measurement, along with the\n\t\t\t\t\tsubject\u2019s age, were included as two supplementary features.\n                    <jats:italic>Main\n\t\t\t\t\t\tresults.<\/jats:italic>\n                    Our best regression ML models achieved a mean absolute\n\t\t\t\t\terror of 12.59 mmHg for estimating systolic BP and 7.43 mmHg for diastolic BP,\n\t\t\t\t\twith correlation coefficients of 0.35 and 0.38 between the predicted and actual\n\t\t\t\t\tvalues, respectively. The best BP normal-hypertensive classification model\n\t\t\t\t\tachieved an area under the receiver operating characteristic curve of 0.655.\n                    <jats:italic>Significance.<\/jats:italic>\n                    Using a large dataset of ECG and BP\n\t\t\t\t\trecordings, this study found that ML models did not achieve acceptable\n\t\t\t\t\tperformance in predicting BP values or classifying BP categories, indicating\n\t\t\t\t\tthat BP cannot be reliably estimated from the ECG.\n                  <\/jats:p>","DOI":"10.1088\/1361-6579\/ae1926","type":"journal-article","created":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T22:53:36Z","timestamp":1761778416000},"page":"115005","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Estimating blood pressure from the electrocardiogram: findings of a\n\t\t\t\t\tlarge-scale negative results study"],"prefix":"10.1088","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6703-0226","authenticated-orcid":true,"given":"Seyedeh","family":"Somayyeh Mousavi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4537-4001","authenticated-orcid":true,"given":"Sajjad","family":"Karimi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9694-6673","authenticated-orcid":true,"given":"Mohammadsina","family":"Hassannia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuzana","family":"Koscova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Bahrami Rad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5099-6438","authenticated-orcid":true,"given":"David","family":"Albert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5709-201X","authenticated-orcid":false,"given":"Gari D","family":"Clifford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4913-6825","authenticated-orcid":false,"given":"Reza","family":"Sameni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"pmeaae1926bib1","first-page":"pp 914","type":"conference-proceedings","article-title":"ECG-based blood pressure estimation using a two-stage inception-regression model","author":"Aldein","year":"2025"},{"key":"pmeaae1926bib2","first-page":"pp 107","type":"conference-proceedings","article-title":"An ECG-based blood pressure estimation using U-Net auto-encoder and random forest regressor","author":"Aldein","year":"2023"},{"key":"pmeaae1926bib3","doi-asserted-by":"publisher","first-page":"2494","DOI":"10.1097\/HJH.0000000000003286","type":"journal-article","article-title":"Artificial intelligence-based opportunistic screening for the detection of arterial hypertension through ECG signals","volume":"40","author":"Angelaki","year":"2022","journal-title":"J. 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