Autosoft Journal

Online Manuscript Access


LSTM Neural Network for Beat Classification in ECG Identity Recognition


Authors



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.


Keywords


Pages

Total Pages: 11

DOI
10.31209/2019.100000104


Manuscript ViewPdf Subscription required to access this document

Obtain access this manuscript in one of the following ways


Already subscribed?

Need information on obtaining a subscription? Personal and institutional subscriptions are available.

Already an author? Have access via email address?


Published

Online Article

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




CONTACT INFORMATION


TSI Press
18015 Bullis Hill
San Antonio, TX 78258 USA
PH: 210 479 1022
FAX: 210 479 1048
EMAIL: tsiepress@gmail.com
WEB: http://www.wacong.org/tsi/