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Describes the nature of a clinical study. Types include:
The purpose of this study is to evaluate the initial safety and effectiveness of Microburst VNS stimulation in subjects with refractory epilepsy.
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The purpose of this study is to compare ECG Belt Research System managed cardiac resynchronization therapy (CRT) patients and a control CRT group with respect to left ventricular (LV) remodeling.
The purpose of this study is to determine if it is possible to record a high quality fetal ECG using signals acquired inside of the birth canal (vagina).
The purpose of this study is to assess the time savings of VIVO device to guide ventricular ablation procedures compared to standard-of-care procedures utilizing electroanatomical mapping systems.
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Scottsdale/Phoenix, AZ, Rochester, MN
The purpose of this study is to develop a new technique based on the change in wave pattern as it passes through muscle tissue, to estimate the location of the self-sustaining regions of chaotic rhythms like human Atrial Fibrillation (AF).
The purpose of this study is to describe and assess the performance of a surface ECG AI algorithm to detect patients with asymptomatic left ventricular dysfunction.
The postnatal diagnosis of Long QT Syndrome (LQTS) is suggested by a prolonged QT interval on 12 lead electrocardiogram (ECG),a positive family history and/or characteristic arrhythmias and confirmed by genetic testing. LQTS testing cannot be performed successfully before birth as fetal ECG is not possible and direct measure of the fetal QT interval by magnetocardiography is limited. Genetic testing can be performed in utero, but there is risk to the pregnancy and the fetus. Although some fetuses present with arrhythmias easily recognized as LQTS (torsade des pointes (TdP) and/or 2° atrioventricular (AV) block, this is uncommon, occurring in <25% of fetal LQTS cases. Rather, the most common presentation of fetal LQTS is sinus bradycardia, a subtle rhythm disturbance that often is unappreciated to be abnormal. Consequently, the majority of LQTS cases are unsuspected and undiagnosed during fetal life, with dire consequences. For example, maternal medications commonly used during pregnancy can prolong the fetal QT interval and may provoke lethal fetal ventricular arrhythmias. But the most significant consequence is the missed opportunity for primary prevention of life threatening ventricular arrhythmias after birth because the infant is not suspected to have LQTS before birth. The over-arching goal of the study is to overcome the barriers to prenatal detection of LQTS. The investigators plan to do so by developing an algorithm using fetal heart rate (FHR) which will discriminate fetuses with or without LQTS. Immediate Goal: The investigators propose a multicenter pre-birth observational cohort study to develop a Fetal Heart Rate (FHR)/Gestational Age (GA) algorithm from a cohort of fetuses recruited from 13 national and international centers where one parent is known by prior genetic testing to have a mutation in one of the common LQTS genes: potassium voltage-gated channel subfamily Q member 1 (KCNQ1), potassium voltage-gated channel subfamily H member 2 (KCNH2), or sodium voltage-gated channel alpha subunit 5 (SCN5A). The investigators have chosen this population because 1) These mutations are the most common genetic causes of LQTS, and 2) Offspring will have high risk of LQTS as inheritance of these LQTS gene mutations is autosomal dominant. Thus, progeny of parents with a known mutation are at high (50%) risk of having the same parental LQTS mutation. The algorithm will be developed using FHR measured serially throughout pregnancy. All offspring will undergo postnatal genetic testing for the parental mutation as the gold standard for diagnosing the presence or absence of LQTS.
The purpose of this study is to assess the feasibility of using the current FDA-approved AliveCor Kardia device and their AliveCor Tripod device (FDA Approval pending) to measure the QT/QTc in patients presenting to the Genetic Heart Rhythm Clinic.
The purpose of this study is to describe and assess the performance of a surface ECG AI algorithm to detect patients with asymptomatic left ventricular dysfunction in a large community setting, to validate our adapted LVEF algorithm in outpatients in the community, to use a combination of digital heart sounds and EKG data to identify structural heart diseases.
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