… Here we hypothesized that a deep neural network (DNN) … Machine learning methods,
including neural networks, have … Deep learning in particular has recently shown promise for …

… used for arrhythmic beats classification, including ECG data acquisition, beat segmentation,
… -based deep learning technique to classify five types of arrhythmias using ECG signals. The …

… in the data, these automation methods rely almost entirely on data-driven pattern discovery
1 . Though data-driven inferencing techniques … Similarly, ECG interpretation is essential for …

… other features derived from the ECG, including phase-rectified … data in the classification of
Wake, REM, NREM light and … on ECG—it would be unfair to compare any technique which …

… Yet, there is an enormous body of historical ECG data worldwide that exists only in paper …
ECG dataset, we developed our own digitization method based upon established techniques. …

… The deep learning method used in this paper realizes the identification of ECG signals in the
scale of 60 samples whose ECG data were … This method had a strong learning ability and …

… of ECG in screening for LVH. Recently, a few studies utilized machine learning techniques for
ECG and … Moreover, our DL model did not need additional preprocessing for ECG data like …

… The goal of this paper is to apply deep learning techniques in the diagnosis of cardiac
arrhythmia using ECG signals with minimal possible data pre-processing. We employ …

… overview of our proposed method of classifying multiple heart diseases from ECG signal and
in … Therefore, a sequence of 1000 data points is sampled from the modified ECG signal for …

… Since the beginning of the 21 st century, deep learning has … a method of combining (Convolutional
neural network) CNN … It contains 48 ECG data, each of which is about 30 min long. …