Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …

Background The electrocardiogram (ECG) is one of the most commonly used diagnostic
tools in medicine and healthcare. Deep learning methods have achieved promising results …

Objective: We aim to provide an algorithm for the detection of myocardial infarction that
operates directly on ECG data without any preprocessing and to investigate its decision …

This paper proposes a novel hybrid network named multiple-feature-branch convolutional
bidirectional recurrent neural network (MFB-CBRNN) for myocardial infarction (MI) detection …

Artificial intelligence and machine learning techniques have progressed dramatically and
become powerful tools required to solve complicated tasks, such as computer vision, speech …

Uncovering flow dynamic behavior of different flow patterns is an important foundation of
multiphase flow research. But the traditional classifier is still adopted in the flow pattern …

In this paper, a denoising temporal convolutional recurrent autoencoder (DTCRAE) is
proposed to improve the performance of the temporal convolutional network (TCN) on time …

Time series modeling has attracted great research interests in the last decades. Among the
literature, shapelet-based models aim to extract representative subsequences, and could …

A novel convolutional neural network is proposed for local prior feature embedding and
imbalanced dataset modeling for multi-channel time-varying signal classification. This model …

Beat-by-beat arrhythmia detection in ambulatory electrocardiogram (ECG) monitoring is
critical for the evaluation and prognosis of cardiac arrhythmias, however, it is a highly …