Eigenvector methods for analysis of human PPG, ECG and EEG signals.
This paper presents eigenvector methods for analysis of the photoplethysmogram (PPG), electrocardiogram (ECG), electroencephalogram (EEG) signals recorded in order to examine the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) upon the human electrophysiological signal behavior. The features representing the PPG, ECG, EEG signals were obtained by using the eigenvector methods. In addition to this, the problem of selecting relevant features among the features available for the purpose of discrimination of the signals was dealt with. Some conclusions were drawn concerning the efficiency of the eigenvector methods as a feature extraction method used for representing the signals under study.
Ubeyli
Elif Derya
ED
TOBB Economics and Technology University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Ankara, Turkey. edubeyli@etu.edu.tr
Cvetkovic
Dean
D
Cosic
Irena
I
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D017156:Photoplethysmography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted