National Yang Ming Chiao Tung University

Other

Taipei Veterans General Hospital

National Central University

Shih-Ann Chen

Yenn-Jiang Lin

Men-Tzung Lo

Yi-Chung Chang

Chen Lin

Yu-Feng Hu

Norden E Huang

Chih-Min Liu

Bo-Jau Kuo

Wan-Hsin Hsieh

Hsiang-Chih Chang

Horng-Shing Lu

Hung-Yi Lee

Shih-Lin Chang

Hung-Hsun Chen

Huan-Yu Kuo

Wei-Shiang Chen

Tze-Wen Chung

Fa-Po Chung

Vincent-Ite Hsieh

Ke-Hsin Hsu

Disclosed herein are methods and compositions for generating pacemaker cells from non-pacemaker cardiomyocytes. For example, the method includes the step of culturing the non-pacemaker cardiomyocytes with silk fibroin so that the silk fibroin induces the transformation of at least a portion thereof into pacemaker cells.

Methods and compositions for generating pacemaker cells

The present disclosure relates to methods, apparatuses, and computer programs for processing computed tomography images. Precise segmentation of the left atrium (LA) in computed tomography (CT) images constitutes a crucial preparatory step for catheter ablation in atrial fibrillation (AF). We aim to apply deep convolutional neural networks (DCNNs) to automate the LA detection/segmentation procedure and create a three-dimensional (3D) geometries. The deep learning provides an efficient and accurate way for automatic contouring and LA volume calculation based on the construction of the 3D LA geometry. Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post atrial fibrillation (AF) ablation. Elimination of NPV triggers can reduce the post-ablation AF recurrence. The deep learning was applied in pre-ablation pulmonary vein computed tomography (PVCT) geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation (PAF). The deep learning model using pre-ablation PVCT can be applied to predict the trigger origins in PAF patients receiving catheter ablation. The application of this model may identify patients with a high risk of NPV trigger before ablation.

Methods, apparatuses, and computer programs for processing pulmonary vein computed tomography images

A computer-assisted method for quantitatively characterizing atrial fibrillation in a patient includes recording time series of bipolar atrial fibrillation signals at multiple sites in a patient's atria using two or more electrodes, calculating a similarity index vector by a computer system based on the bipolar atrial fibrillation signal between a first site and its neighboring sites, constructing an similarity-index vector field based on similarity-index vectors at different sites, calculating Curl and Divergence of the similarity-index vector field, calculating Rotor Identification using Curl and Divergence, calculating Focal Identification using Divergence, and determining one or more critical regions in the patient's atria if Rotor Identification is above a first predetermined threshold and Focal Identification is above a second predetermined threshold.

Accurately indetifying critcal regions in atrial fibrillation by identifying rotors in directional similarity vector field

A computer-assisted method for quantitative characterizing atrial fibrillation (AF) in a patient includes recording unipolar atrial fibrillation signals from multiple sites in a patient's atria, calculating bipolar electrograms using unipolar AF signals recorded at adjacent sites by a computer system, applying Empirical Mode Decomposition to remove a background from the bipolar electrogram signal to obtain a filtered bipolar electrogram signal, applying Hilbert transform to an envelope function of the filtered bipolar electrogram signal to obtain a time series of instantaneous phases of the filtered bipolar electrogram signal, and identifying a rotor region in patient's atria using the instantaneous phases in the filtered bipolar electrogram signal.

System and method for indentifying rotors in fractionated signals in persistent atrial fibrillation ablation

The present invention provides a handy remote physiological signal detection system, comprising a sensing unit, a stimulation unit and a control unit. The sensing unit includes a detecting electrode, a first surface detection electrode, a second surface detection electrode and a sensing module. The sensing modules is used to detect the signals between the detection electrode and the first surface detection electrode to get an epicardial detection signal, and is also used to detect the signals between the second surface detection electrode and the first detection electrode to get a surface-ECG signal. The stimulus unit includes a stimulating electrode and a stimulus module used to provide a stimulus signal to the stimulating electrode. The control unit includes a user interface and a processing module used to convert the epicardial detection signal and the surface-ECG signal to digital signals and display the digital signals in the user interface. All systems were controlled through the remote system and are small and handy.

Physiological signal detection system

A computer-assisted method for quantitative characterization and treatment of ventricular fibrillation includes preprocessing a time series of an atrial fibrillation signal obtained from a patient, segmenting the time series of the AF signal into activation segments by the computer system, obtaining local activation waveforms (LAW) from the activation segments, determining degrees of similarity between the LAWs, and identifying one or more critical regions in the patient's atria if the LAWs have degrees of similarity exceeding a first threshold value.

System and method for improved complex fractionated electrogram ablation

Growing community of inventors

Taipei, Taiwan

Shih-Ann Chen