title: Phasic Electromyographic Metric detection based on wavelet analysis.
abstract: The Phasic Electromyographic Metric (PEM) has been recently introduced as a sensitive indicator to differentiate Parkinson's Disease (PD) patients from controls, non-PD patients with a history of Rapid Eye Movement Disorder (RBD) from controls, and PD patients with early and late stage disease. However, PEM assessment through visual inspection is a cumbersome and time consuming process. Therefore, a reliable automated approach is required so as to increase the utilization of PEM as a reliable and efficient clinical tool to track PD progression. In this study an automated method for the detection of PEM is presented, based on the use of signal analysis and pattern recognition techniques. The results are promising indicating that an automatic PEM identification procedure is feasible.
authors.affiliation: NIH NINDS postdoctoral fellow at Emory University School of Medicine Department of Neurology, Atlanta, GA 30322 USA (phone: 404-712-9752;).
authors.forename: Jacqueline A
authors.initials: JA
authors.lastname: Fairley
authors.affiliation: Department of Informatics and Communications Technology, Technological Educational Institution of Epirus, Arta, 47100 Greece.
authors.forename: George
authors.initials: G
authors.lastname: Georgoulas
authors.affiliation: Department of Informatics and Communications Technology, Technological Educational Institution of Epirus, Arta, 47100 Greece.
authors.forename: Chrystostomos D
authors.initials: CD
authors.lastname: Stylios
authors.affiliation: Faculty member at the Georgia Institute of Technology, Atlanta, GA 30332 USA.
authors.forename: George
authors.initials: G
authors.lastname: Vachtsevanos
authors.affiliation: Faculty members at Emory University.
authors.forename: David B
authors.initials: DB
authors.lastname: Rye
authors.affiliation: Faculty members at Emory University.
authors.forename: Donald L
authors.initials: DL
authors.lastname: Bliwise