Ewha Womans University · 医学
Professor Junbeom Park's research lab specializes in cardiac electrophysiology and arrhythmia mechanisms, with a focus on atrial fibrillation (AF) pathophysiology, non-invasive biomarkers, and advanced ECG analysis. The lab investigates hemodynamic and electrical remodeling in the left atrium, particularly in relation to prehypertension, insulin resistance, and left atrial compliance. Utilizing deep learning and surface ECG signal analysis, the lab develops predictive models for AF onset and recurrence, emphasizing early detection and personalized rhythm management strategies.
Figures are computed from collected data and may differ slightly.
Even in a healthy Asian populations without comorbidities, prehypertension and IFG were important risk factors of AF. Specifically, when prehypertension, including systolic and diastolic BPs, was finally combined with the IFG, the risk of new onset AF was increased especially in the BMI <25 kg/m2 group.
The PR interval was closely associated with advanced LA remodeling due to AF, and had a noninvasive significant predictive value of clinical recurrence of AF after RFCA.
Abstract We report the fabrication of individually addressable, high-density, vertical zinc oxide (ZnO) nanotube pressure sensor arrays. High-sensitivity and flexible piezoelectric sensors were fabricated using dimension- and position-controlled, vertical, and free-standing ZnO nanotubes on a graphene substrate. Significant pressure/force responses were achieved from small devices composed of only single, 3 × 3, 5 × 5, and 250 × 250 ZnO nanotube arrays on graphene. An individually addressable pi
Background The purpose of the RAFAS (Risk and Benefits of Urgent Rhythm Control of Atrial Fibrillation in Patients With Acute Stroke) trial was to explore the risks and benefits of early rhythm control in patients with newly documented atrial fibrillation (AF) during an acute ischemic stroke (IS). Method and Results An open-label, randomized, multicenter trial design was used. If AF was diagnosed, the patients in the early rhythm control group started rhythm control within 2 months after the occ
Stiff left atrial (LA) syndrome was initially reported in post-cardiac surgery patients and known to be associated with low LA compliance. We investigated the physiological and clinical implications of LA compliance by estimating LA pulse pressure (LApp) among patients with atrial fibrillation (AF) and structurally and functionally normal heart. Among 1038 consecutive patients with LA pressure measurements before AF ablation, we included 334 patients with structurally and functionally normal hea
Since carbon nanotube (CNT) fibers have a hierarchical structure, the specific strength of CNT fibers can be estimated to be much higher than its real value when the linear density of the fiber is measured using the vibroscopic method.
Polyacrylonitrile-based carbon nanofibers (PAN-based CNFs) have great potential to be used for carbon dioxide (CO<sub>2</sub>) capture due to their excellent CO<sub>2</sub> adsorption properties. The porous structure of PAN-based CNFs originates from their turbostratic structure, which is composed of numerous disordered stacks of graphitic layers. During the carbonization process, the internal structure is arranged toward the ordered graphitic structure, which significantly influences the gas ad
We characterized the f-waves in atrial fibrillation (AF) in the surface ECG by quantifying the amplitude, irregularity, and dominant rate of the f-waves in leads II, aVL, and V<sub>1</sub>, and investigated whether those parameters of the f-waves could discriminate long-standing persistent AF (LPeAF) from non-LPeAF. A total of 224 AF patients were enrolled: 112 with PAF (87 males), 48 with PeAF (38 males), and 64 with LPeAF (47 males). The f-waves in surface ECG leads V<sub>1</sub>, aVL, and II,
This study aims to compare the effectiveness of using discrete heartbeats versus an entire 12-lead electrocardiogram (ECG) as the input for predicting future occurrences of arrhythmia and atrial fibrillation using deep learning models. Experiments were conducted using two types of inputs: a combination of discrete heartbeats extracted from 12-lead ECG and an entire 12-lead ECG signal of 10 s. This study utilized 326,904 ECG signals from 134,447 patients and categorized them into three groups: tr
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