title: Eigenvector methods for analysis of human PPG, ECG and EEG signals.
abstract: 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.
authors.affiliation: TOBB Economics and Technology University, Faculty of Engineering, Department of Electrical and Electronics Engineering, Ankara, Turkey. edubeyli@etu.edu.tr
authors.forename: Elif Derya
authors.initials: ED
authors.lastname: Ubeyli
authors.forename: Dean
authors.initials: D
authors.lastname: Cvetkovic
authors.forename: Irena
authors.initials: I
authors.lastname: Cosic
mesh_terms: 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