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Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

BMC Med Inform Decis Mak

January 2025

Department of Biomedical Engineering, National Defense Medical Center, Taiwan, No.161, Sec.6, Minchiuan E. Rd., Neihu Dist, Taipei, 11490, Taiwan.

Background: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the condition has become the epicenter of a plethora of ECG diagnostic research. In recent diagnostic methodologies, Morse Continuous Wavelet Transform (MsCWT) is a feature extraction technique utilized to draw out distinctive attributes of ECG signals. In our study, we explore the employment of MsCWT in the classification of AF with ECG signals in a continuum.

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Background: Historically, percutaneous transcatheter left atrial appendage closure (LAAC) has been performed under general anesthesia (GA) with transesophageal echocardiographic images obtained by a noninvasive cardiologist and usually requires an overnight hospital stay. Alternatively, we present our single-center experience performing LAACs under deep sedation (DS), employing an echocardiographic technician instead of a noninvasive cardiologist, and expediting same-day discharge. Mid- to long-term outcomes were also evaluated with follow-up imaging at a 45-day visit.

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Venous thromboembolic disease remains a leading cause of maternal morbidity and mortality. We report a case of a 30-year-old woman at 37 gestation with a history of thalassaemia intermedia and splenectomy. During pregnancy, she had been managed with frequent blood transfusions and enoxaparin.

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