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Research Article | Open Access
Volume 14 2022 | .
BIDIRECTIONAL LSTM BASED HYBRID DEEP LEARNING FRAMEWORKS FORCARDIACAR RHYTHMIACLASSIFICATION
G.Selvakumari,G.Selvapriya
Pages: 2283-2290
Abstract
Electrocardiogram (ECG) analyzes the electrical activity of the heart which diagnoses Cardiac Arrhythmia(CA) in the biomedical field. Cardiovascular disease classification is necessary for efficient and fast remedial treatment of the patient. In this paper, we propose a hybrid technique for ECG classification using a Deep Neural Network (DNN)with Bi-directional Long Short Term Memory (Bi-LSTM) layer by giving the modified ECG signal as an input. The modified ECG signal is obtained through the combination of Empirical Mode Decomposition(EMD) and Discrete Wavelet Transform(DWT)which gives a better denoising performance.
Keywords
Electrocardiogram,EmpiricalModeDecomposition,DiscreteWaveletTransform,Deep Neural Networks
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