[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Seerlinq s. r. o.\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":46},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":11,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":27,"overallStatus":34,"whyStopped":4,"lastUpdateSubmitDate":35,"lastUpdatePostDateStruct":36,"startDateStruct":39,"completionDateStruct":41,"leadSponsor":43,"locationsCount":5},"100650554","a-photoplethysmography-based-machine-learning-algorithm-for-early-atrial-fibrillation-detection-a-prospective-validation-study-100650554",false,"NCT07749183","A Photoplethysmography-Based Machine Learning Algorithm for Early Atrial Fibrillation Detection: A Prospective Validation Study","Prospective Validation of a Machine-Learning Algorithm Using Photoplethysmography Signals for Early Detection of Atrial Fibrillation During Remote Telemonitoring","HeartCore AF","Inclusion Criteria:\n\n* Adults ≥18 years with a diagnosis of heart failure (HFrEF, HFmrEF, or HFpEF)\n* 12-lead ECG performed to confirm cardiac rhythm classification (AF vs. non-AF)\n\nExclusion Criteria:\n\n* Missing a valid PPG recording","ALL","18 Years",{"count":20,"type":21},200,"ESTIMATED","OBSERVATIONAL","This is a prospective study validating a new machine-learning algorithm that detects atrial fibrillation (AF) from photoplethysmography (PPG) signals, developed for integration into the Seerlinq remote monitoring platform. This algorithm builds on the same core PPG signal-processing technology as Seerlinq's HeartCore device, a CE-certified (Class IIb, MDR) device that monitors left ventricular filling pressures in heart failure patients. The algorithm will be validated through internal cross-validation, external validation against an independent cohort with paired PPG-ECG recordings, and validation in a cohort of patients with paroxysmal atrial fibrillation and frequent sinus-AF transitions.",[25,26],"Atrial Fibrillation (AF)","Heart Failure",[28,29,30,31,32,33],"Photoplethysmography","Machine learning","Remote telemonitoring","Wearable device","Digital biomarker","Arrhythmia detection","RECRUITING","2026-07-31",{"date":37,"type":38},"2026-08-06","ACTUAL",{"date":40,"type":38},"2025-10-01",{"date":42,"type":21},"2026-11",{"name":44,"class":45},"Seerlinq s. r. o.","OTHER",""]