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

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

doi:10.1109/iadcc.2015.7154806

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

Vedavathi B S Vedavathi B S, S G Hiremath + Show 2 more

https://doi.org/10.1109/iadcc.2015.7154806

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Publication Date: Jun 1, 2015

Citations: 7

Affiliation: Mathrusri Ramabai Ambedkar Dental College & Hospital, International Institute of Information Technology Bangalore

#Electrocardiogram ) And Electroencephalogram #Daubechies Wavelet Transform + Show 8 more

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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.

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