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

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.

[1]  H. He,et al.  A self-organizing learning array system for power quality classification based on wavelet transform , 2006, IEEE Transactions on Power Delivery.

[2]  Reza Tafreshi,et al.  A novel wavelet-based index to detect epileptic seizures using scalp EEG signals , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Kenneth Revett,et al.  EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks , 2006, IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA'06).

[4]  Carlos E. D'Attellis,et al.  Detection of epileptic events in electroencephalograms using wavelet analysis , 2007, Annals of Biomedical Engineering.

[5]  Reza Tafreshi,et al.  Automated Real-Time Epileptic Seizure Detection in Scalp EEG Recordings Using an Algorithm Based on Wavelet Packet Transform , 2010, IEEE Transactions on Biomedical Engineering.

[6]  Lalit M. Patnaik,et al.  Epileptic EEG detection using neural networks and post-classification , 2008, Comput. Methods Programs Biomed..

[7]  Yann LeCun,et al.  Classification of patterns of EEG synchronization for seizure prediction , 2009, Clinical Neurophysiology.

[8]  B. Kotchoubey,et al.  Recognition of affective prosody: continuous wavelet measures of event-related brain potentials to emotional exclamations. , 2004, Psychophysiology.

[9]  Michael G. Strintzis,et al.  ECG pattern recognition and classification using non-linear transformations and neural networks: A review , 1998, Int. J. Medical Informatics.

[10]  Tong Zhang,et al.  A multistage, multimethod approach for automatic detection and classification of epileptiform EEG , 2002, IEEE Transactions on Biomedical Engineering.

[11]  Ali Ghaffari,et al.  Heart arrhythmia detection using continuous wavelet transform and principal component analysis with neural network classifier , 2010, 2010 Computing in Cardiology.

[12]  A. Prochazka,et al.  Wavelet transform use for feature extraction and EEG signal segments classification , 2008, 2008 3rd International Symposium on Communications, Control and Signal Processing.

[13]  Abdulhamit Subasi,et al.  Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing , 2005, Neural Networks.

[14]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[15]  Sazali Yaacob,et al.  EEG feature extraction for classifying emotions using FCM and FKM , 2008 .