Wavelet transform feature extraction from human PPG, ECG, and EEG signal responses to ELF PEMF exposures: A pilot study

Authors:
Dean Cvetkovic;Elif Derya Übeyli;Irena Cosic
Affiliations:
RMIT University, School of Electrical and Computer Engineering, GPO Box 2476V, Melbourne, Victoria 3001, Australia;TOBB Economics and Technology University, Faculty of Engineering, Department of Electrical and Electronics Engineering, 06530 Söğütözü, Ankara, Turkey;RMIT University, School of Electrical and Computer Engineering, GPO Box 2476V, Melbourne, Victoria 3001, Australia
Venue:
Digital Signal Processing
Year:
2008

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Abstract

This paper presents the experimental pilot study to investigate the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) in response to photoplethysmographic (PPG), electrocardiographic (ECG), electroencephalographic (EEG) activity. The assessment of wavelet transform (WT) as a feature extraction method was used in representing the electrophysiological signals. Considering that classification is often more accurate when the pattern is simplified through representation by important features, the feature extraction and selection play an important role in classifying systems such as neural networks. The PPG, ECG, EEG signals were decomposed into time-frequency representations using discrete wavelet transform (DWT) and the statistical features were calculated to depict their distribution. Our pilot study investigation for any possible electrophysiological activity alterations due to ELF PEMF exposure, was evaluated by the efficiency of DWT as a feature extraction method in representing the signals. As a result, this feature extraction has been justified as a feasible method.