Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review

Abstract

Deep learning models have become a popular mode to classify electrocardiogram (ECG) data.Investigators have used a variety of deep learning techniques for this application. Herein, a detailed examination of deep learning methods for ECG arrhythmia detection is provided. Approaches used by investigators are examined, and their contributions to the field are detailed. For this purpose, journal papers have been surveyed according to the methods used. In addition, various deep learning models and experimental studies are described and discussed. A five-class ECG dataset containing 100,022 beats was then utilized for further analysis of deep learning techniques. The constructed models were examined with this dataset, and results are presented. This study therefore provides information concerning deep learning approaches used for arrhythmia classification, and suggestions for further research in this area.

Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records

“…In the model, a LSTM block was used for sequence learning. Some studies on 1D signals such as EEG and ECG [52, 67, 68, 72] show that the combination of representation and sequence learning can yield a higher performance than by using representation learning alone. According to this information, we used a 128 unit LSTM block at the end of the representation learning layers.…”

“…Deep learning has been the preferred mode of ECG classification over the last few years [4, 31, [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] ]. One-dimensional convolutional neural networks (1D-CNN) have become popular to classify ECG records because of their one-dimension structure.…”

“…We developed a DNN model to classify rhythms from each of 12-lead inputs. We preferred to use the representation and sequence learning structure together because of their salutary performance on both ECG [52, 67, 68, 72] and EEG signals [45] . The novelty of this paper can be summarized as follows: a useful single DNN model has been constructed to detect multiple rhythm classes on 12-lead signals.…”

Automated detection of COVID-19 cases using deep neural networks with X-ray images

“…Application of machine learning methods for automatic diagnosis in the medical field have recently gained popularity by becoming an adjunct tool for clinicians [21][22][23][24][25]. Deep learning, which is a popular research area of artificial intelligence (AI), enables the creation of end-to-end models to achieve promised results using input data, without the need for manual feature extraction [26,27].…”

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices

“…As said, use of ECG features instead of raw data (as sometimes done when using long short-term memory, 1D convolutional neural network and others [ 11 , 15 , 16 , 33 , 34 ]) at the input of RSL_ANN implies adding an ECG processing step for feature extraction before classification; however, it also allows the construction of a faster and simpler artificial neural network, since based on a reduced number of hidden layers, through a smaller training dataset. In addition, since each feature, if well selected, reflects a specific physiologic phenomenon, classification logic of a network is physiologically more understandable than when it is based on raw data, and this is very much appreciated in context in which interpretability of the model is desirable.…”