… In this work, the power of deep learning techniques was used to predict the four major … using
the public ECG images dataset of cardiac patients. First, the transfer learning approach was …

… This paper proposes a deep learning based new method for detection of five different ECG
… Despite traditional methods, ECG signals were examined with 2-D image formation in this …

… This paper introduces a new approach to predict VTA and classify various arrhythmias
using a novel technique. In this technique, we transform ECG signals into binary images. Our …

… To this end, we first show a big picture of most common learning models used in the studied
papers (see Section 2). Then, we present an overview of the arrhythmias from the medical …

… is 1-D signal, it is input directly into 1-D CNN or transformed into image and processed by
2-… In this section, deep learning methods of classifying ECGs are introduced according to their …

… data into two-dimensional representations, many profound learning techniques applied on
images can be used. The short-time Fourier Transform (STFT) approach is frequently used to …

… This special case of ECG differs from usual one where CNN is used – image recognition task.
… and use some machine learning algorithm without any feature engineered procedure and …

… Particularly for localization tasks, such as QRS detection, a fully convolutional network
with a large kernel size can be applied to an ECG image to reduce that image's height to one …

… certain machine learning tasks [7,8]. Hence, for ECG signals, a 2-D transformation has to
be applied to make the time series suitable for deep learning methods that require 2-D images …

… arrhythmia classification result indicates that detection of arrhythmia with ECG images
and CNN model can be an effective approach to help the experts to diagnose cardiovascular …