… However, feature selection is a process of removing irrelevant and redundant features (dimensions)
from the data set in the training process of machine learning algorithms. There are …

… In addition to all of these results, in this study, the effect of the number distribution in the
input data on the success of the deep learning model was observed. As far as the arrhythmia …

… three methods for synthesizing artificial ECG signals by using deep learning techniques. To
… This R-wave peaks data set represents a simple form of ECG, as a result, we can use this …

… We found 147 papers using deep-learning methods in EMG signal analysis, ECG signals
analysis, EEG signals analysis, EOG signals analysis, and combinations of signal analysis. …

… the capture of ECG data, and DL algorithm-based interpretation software automatically
interprets ECG data. Wearable technology, wireless sensors, and deep learning techniques can …

… ECG interpretation. In this paper, the challenges and differences between machine learning
techniques for ECG … in the identification of hidden patterns in data) when applied to ECG …

… All experiments were repeated 10 times using a bootstrapping method. The CAE … also
subjected ECG waveform data to deep learning. Attia et al. [1] showed that deep learning of only …

… ), deep learning techniques are favored over conventional machine learning techniques, due
… [26], which employed other deep learning algorithms and small data sizes. Notably, while …

… deep learning methodology is proposed for the classification of single-lead electrocardiogram
(ECG) … from the raw two -lead ECG data. Zubair et al. [43] used CNN with 44 recordings of …

… The massive amount of ECG data collected every day, … Machine learning based methods
for ECG monitoring and interpretation range from traditional machine learning to deep learning, …