Improving generalization performance of electrocardiogram classification models.

Recently, many electrocardiogram (ECG) classification algorithms using deep learning have been proposed. Because the ECG characteristics vary across datasets owing to variations in factors such as recorded hospitals and the race of participants, the model needs to have a consistently high generalization performance across datasets. In this study, as part of the PhysioNet/Computing in Cardiology Challenge (PhysioNet Challenge) 2021, we present a model to classify cardiac abnormalities from the 12- and the reduced-lead ECGs.

Chronic cavitary pulmonary aspergillosis: Serial clinical and CT findings correlated with antifungal treatment and patient response.

Background: Chronic cavitary pulmonary aspergillosis (CCPA) is the most common form of chronic pulmonary aspergillosis.

Objective: We hypothesise that by observing serial clinical and CT findings of CCPA patients with antifungal therapy, factors helping predict responses to antifungal therapy could be withdrawn.

Methods: A total of 31 patients with CCPA who received antifungal therapy for greater than six months and who had serial CT studies were included.

CNN-based Two Step R Peak Detection Method: Combining Segmentation and Regression.

For semantic segmentation, U-Net provides an end-to-end trainable framework to detect multiple class objects from background. Due to its great achievements in computer vision tasks, U-Net has broadened its application to biomedical signal processing, especially, segmentation of waveforms in ECG signal. Despite its superior performance for QRS complex detection to other traditional signal processing methods, direct application of the U-Net to R peak detection has limitation since the U-Net structures tend to predict high probability around true peak.

Acute Pulmonary Embolism and Chronic Thromboembolic Pulmonary Hypertension: Clinical and Serial CT Pulmonary Angiographic Features.

In acute pulmonary embolism (PE), circulatory failure and systemic hypotension are important clinically for predicting poor prognosis. While pulmonary artery (PA) clot loads can be an indicator of the severity of current episode of PE or treatment effectiveness, they may not be used directly as an indicator of right ventricular (RV) failure or patient death. In other words, pulmonary vascular resistance or patient prognosis may not be determined only with mechanical obstruction of PAs and their branches by intravascular clot loads on computed tomography pulmonary angiography (CTPA), but determined also with vasoactive amines, reflex PA vasoconstriction, and systemic arterial hypoxemia occurring during acute PE.

Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems.

Background And Objectives: Most deep-learning-related methodologies for electrocardiogram (ECG) classification are focused on finding an optimal deep-learning architecture to improve classification performance. However, in this study, we proposed a methodology for fusion of various single-lead ECG data as training data in the single-lead ECG classification problem.

Methods: We used a squeeze-and-excitation residual network (SE-ResNet) with 152 layers as the baseline model.