NDLI: Deep neural network based approach for ECG classification using hybrid differential features and active learning

Content Provider	IET Digital Library
Author	Hanbay, Kazım
Abstract	A novel active learning-based electrocardiogram (ECG) signal classification method using eigenvalues and deep learning is proposed. Six statistical features relating to ECG beat intervals are calculated separately for each heartbeat. Both statistical features and eigenvalues of ECG beats are combined into a single feature vector. The eigenvalues of ECG beats are used as an input to denoising autoencoder (DAE). Weighted ECG beat intervals are calculated by using ten-fold cross-validation approach. To learn an efficient feature representation from the hybrid feature vector, DAE is used in an unsupervised way. After completing the feature learning procedure, a softmax regression layer is added on the top of the resulting hidden layer of DAE, and thus a suitable deep neural network (DNN) architecture is built. The learned features obtained from the autoencoder layers are fed to the softmax regression layer for classification. To update weights of the proposed eigenvalues-based DNN model, ECG beats are labelled by the medical expert are used. In order to determine the most informative beats, entropy and Breaking-Ties are also used as selection criteria. The proposed method is evaluated in terms of ECG beats classes. The classification performance of the authors’ proposed model is also compared with the several conventional machine learning classifiers.
Starting Page	165
Ending Page	175
Page Count	11
ISSN	17519675
Volume Number	13
e-ISSN	17519683
Issue Number	Issue 2, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/13/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2018.5103
Journal	IET Signal Processing
Publisher Date	2018-09-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Active Learning-based Electrocardiogram Signal Classification Method Bioelectric Signal Biology And Medical Computing Cross-validation Approach DAE Deep Learning Deep Neural Network Architecture Digital Signal Processing ECG Beats ECG Classification Eigen Values And Eigen Function Eigenvalues-based DNN Model Electrical Activity in Neurophysiological Processes Electrocardiography Electrodiagnostics And Other Electrical Measurement Technique Feature Extraction Feature Learning Procedure Feature Representation Hybrid Differential Feature Hybrid Feature Vector Informative Beats Knowledge Engineering Technique Learning in AI Medical Signal Processing Neural Computing Technique Neural Nets Neural Network Based Approach Regression Analysis Signal Classification Signal Processing And Detection Single Feature Vector Softmax Regression Layer Statistical Feature Weighted ECG Beat Intervals
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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