RT journal article
T1 Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals
A1 Sajid Butt, Fatima
A1 Wagner, Matthias F.
A1 Schaefer, Joerg
A1 Gómez-Ullate Oteiza, David
A2 Ingeniería Informática
K1 Electrocardiograph
K1 benchmark testing
K1 fall detection
K1 time series analysis
K1 machine learning
K1 deep learning
K1 LSTM
K1 CNN
K1 attention
K1 transformer
K1 PTB XL
AB Many recent studies have focused on the automatic classification of electrocardiogram (ECG) signals using deep learning (DL) methods. Most rely on existing complex DL methods, such as transfer learning or providing the models with carefully designed extracted features based on domain knowledge. A common assumption is that the deeper and more complex the DL model is, the better it learns. In this study, we propose two different DL models for automatic feature extraction from ECG signals for classification tasks: A CNN-LSTM hybrid model and an attention/transformer-based model with wavelet transform for the dimensional embedding. Both of the models extract the features from time series at the initial layers of the neural networks and can obtain performance at least equal to, if not greater than, many contemporary deep neural networks. To validate our hypothesis, we used three publicly available data-sets to evaluate the proposed models. Our model achieved a benchmark accuracy of 99.92% for fall detection and 99.93% for the PTB database for myocardial infarction versus normal heartbeat classification.
PB IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
SN 2169-3536
YR 2022
FD 2022
LK http://hdl.handle.net/10498/28744
UL http://hdl.handle.net/10498/28744
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-may-2026