@Article{2019.100000104,
AUTHOR = {Xin Liu, Yujuan Si, Di Wang},
TITLE = {LSTM Neural Network for Beat Classification in ECG Identity Recognition},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {2},
PAGES = {341--351},
URL = {http://www.techscience.com/iasc/v26n2/39941},
ISSN = {2326-005X},
ABSTRACT = {As a biological signal existing in the human living body, the electrocardiogram 
(ECG) contains abundantly personal information and fulfils the basic 
characteristics of identity recognition. It has been widely used in the field of 
individual identification research in recent years. The common process of 
identity recognition includes three steps: ECG signals preprocessing, feature 
extraction and processing, beat classification recognition. However, the existing 
ECG classification models are sensitive to limitations of database type and 
extracted features dimension, which makes classification accuracy difficult to 
improve and cannot meet the needs of practical applications. To tackle the 
problem, this paper proposes to build an ECG individual recognition model 
based on a deep Long Short-Term Memory (LSTM) neural network. The LSTM 
network model has a memory cell and, therefore, it is an expert in handling 
long time ECG signals. With deeper learning, the nonlinear expression ability of 
the ECG beat classification model is gradually enhancing. The paper adopts two 
stacked LSTM models as hidden layers in the neural network; the Softmax layer 
is used as a classification layer to identify an individual. Then, low -level 
morphological features and deep-level chaotic features (Lyapunov exponent) 
are extracted to verify the feasibility of the deep LSTM network for 
classification. The model is respectively applied to a healthy human database 
and a human with a heart disease database. Experimental results show that 
extracting simple low-level features and chaotic features both achieve better 
classification performance. So, the robustness of the LSTM classification model 
is verified.},
DOI = {10.31209/2019.100000104}
}