A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification

By Özal Yıldırım. Comput. Biol. Medicine, 2018

742 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Network on Imbalanced ECG Datasets (2021, methodology): “Yildirim [52] fed the same database into two bidirectional LSTM and two fully-connected layers to detect five types of ECG beats, while Kim and Pyun [53] tested the performance of six different LSTM network architectures in order to achieve ECG classification for the MIT-BIH”
ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network (2019, methodology): “After the step of feature extraction of ECG signals, classification is traditionally conducted using state-of-the-art classifiers such as support vector machines (SVM), neural networks (NN), cluster analysis (CA), random forests (RF), optimum-path forest (OPF) and some other classification tools [14”
A review on deep learning methods for ECG arrhythmia classification (2020, methodology): “9, the QRS complex is noticeably changed for cases with RBBB or LBBB, and a dynamic classiﬁer like LSTM can learn and classify such the variations of QRS complex.”