{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T06:52:50Z","timestamp":1791355970078,"version":"4.3.1"},"reference-count":37,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"19","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2026,10,6]]},"abstract":"<jats:sec xml:lang=\"en\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p xml:lang=\"en\">Seizures are common after out\u2010of\u2010hospital cardiac arrest and may exacerbate secondary brain injury, yet early prediction remains challenging. Although heart rate variability has been used to predict seizures in epilepsy, its role and optimal prediction window after cardiac arrest remain unclear. We aimed to develop a heart rate variability\u2013based model for predicting post\u2013cardiac arrest seizures and identify the optimal prediction window.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p xml:lang=\"en\">This study prospectively collected and retrospectively analyzed data from adults resuscitated from nontraumatic out\u2010of\u2010hospital cardiac arrest and admitted to an academic post\u2013cardiac arrest care center. Electrocardiography and electroencephalography were continuously recorded, and seizures were independently confirmed by a neurologist. Five\u2010minute time\u2010, frequency\u2010, and nonlinear\u2010domain heart rate variability features from 0 to 30\u2009minutes before seizure onset were normalized to baseline and entered into a support vector machine classifier using training, validation, and testing sets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p xml:lang=\"en\">Among 36 patients, 20 (56%) experienced 93 seizures. Model performance was highest 15 to 20\u2009minutes before seizure onset. At 18\u2009minutes, the model achieved an area under the receiver operating characteristic curve of 0.812 (95% CI, 0.792\u20130.832), 76% accuracy, 77% sensitivity, and 68% specificity. Compared with baseline, low frequency increased 3.28\u2010fold, sample entropy 2.58\u2010fold, high frequency 2.43\u2010fold, and the low\/high frequency ratio 1.47\u2010fold in seizure segments; corresponding ratios in nonseizure segments remained near baseline. These findings suggest that autonomic function fluctuates substantially before post\u2013cardiac arrest seizures and may provide an early warning window for clinically important neurologic events during intensive care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec xml:lang=\"en\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p xml:lang=\"en\">Heart rate variability changed before post\u2013cardiac arrest seizures, with 18\u2009minutes before onset appearing optimal for prediction.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1161\/jaha.126.050349","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:29:21Z","timestamp":1789738161000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Post\u2013Cardiac Arrest Seizures Using Heart Rate 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