{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T03:43:56Z","timestamp":1790567036970,"version":"4.1.0"},"reference-count":23,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T00:00:00Z","timestamp":1638403200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Physiol."],"abstract":"<jats:p>\n                    In this study, we present a non-invasive solution to identify patients with coronary artery disease (CAD) defined as \u2a7e50% stenosis in at least one coronary artery. The solution is based on the analysis of linear acceleration (seismocardiogram, SCG) and angular velocity (gyrocardiogram, GCG) of the heart recorded in the x, y, and z directional axes from an accelerometer\/gyroscope sensor mounted on the sternum. The database was collected from 310 individuals through a multicenter study. The time-frequency features extracted from each SCG and GCG data channel were fed to a one-dimensional Convolutional Neural Network (1D CNN) to train six separate classifiers. The results from different classifiers were later fused to estimate the CAD risk for each participant. The predicted CAD risk was validated against related results from angiography. The SCG z and SCG y classifiers showed better performance relative to the other models (\n                    <jats:italic>p<\/jats:italic>\n                    &amp;lt; 0.05) with the area under the curve (AUC) of 91%. The sensitivity range for CAD detection was 92\u201394% for the SCG models and 73\u201387% for the GCG models. Based on our findings, the SCG models achieved better performance in predicting the CAD risk compared to the GCG models; the model based on the combination of all SCG and GCG classifiers did not achieve higher performance relative to the other models. Moreover, these findings showed that the performance of the proposed 3-axial SCG\/GCG solution based on recordings obtained during rest was comparable, or better than stress ECG. These data may indicate that 3-axial SCG\/GCG could be used as a portable at-home CAD screening tool.\n                  <\/jats:p>","DOI":"10.3389\/fphys.2021.758727","type":"journal-article","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T12:30:33Z","timestamp":1638448233000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Detecting Coronary Artery Disease Using Rest Seismocardiography and Gyrocardiography"],"prefix":"10.3389","volume":"12","author":[{"given":"Parastoo","family":"Dehkordi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erwin P.","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kouhyar","family":"Tavakolian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen G.","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew P.","family":"Blaber","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farzad","family":"Khosrow-Khavar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,12,2]]},"reference":[{"key":"B1","unstructured":"Cardiac CT, MR, SPECT, ECHO, and PET: what test, when?1322\n            Al-ShehriH.\n            SmallG.\n            BenjaminJ. 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