Wavelet transform based neural network model to detect and characterise ECG and EEG signals simultaneously

Author

Vedavathi, B.S. ; Biradar, Shilpa ; Hiremath, S.G. ; Thippeswamy, G.

Author_Institution

Dept. of ECE, East West Inst. of Technol., Bangalore, India

fYear

2015

fDate

12-13 June 2015

Firstpage

743

Lastpage

748

Abstract

This research work focuses on to the development of neural network based detection and characterization of electrocardiogram (ECG) and electroencephalogram (EEG) signal. ECG and EEG signals have prime importance for patients under critical care. These signals have to be continuously monitored and processed as they are inter dependent. In this research Dyadic wavelet transform (DyWT) is used to process ECG data and Daubechies wavelet transform (DWT) is used to process EEG data. Emerging back propagation NN algorithm and Hopfield algorithm is used to detect and characterize both ECG and EEG signals. The different ECG and EEG data´s have been collected and simultaneously processed and recognized.

Keywords

Hopfield neural nets; backpropagation; electrocardiography; electroencephalography; medical signal detection; patient care; wavelet transforms; DWT; Daubechies wavelet transform; DyWT; ECG; EEG; Hopfield algorithm; back propagation NN algorithm; dyadic wavelet transform; electrocardiogram; electroencephalogram; neural network model; patient care; signal detection; Artificial neural networks; Biological neural networks; Electrocardiography; Electroencephalography; Multiresolution analysis; Wavelet transforms; Back Propagation Neural Network; Daubechies Wavelet transforms(DWT); Dyadic wavelet transforms (DyWT); Electrocardiogram(ECG); Electroencephalogram (EEG); Neural Network;

fLanguage

English

Publisher

ieee

Conference_Titel

Advance Computing Conference (IACC), 2015 IEEE International

Conference_Location

Banglore

Print_ISBN

978-1-4799-8046-8

Type

conf

DOI

10.1109/IADCC.2015.7154806

Filename

7154806