Single-sweep analysis of event-related potentials by wavelet networks--methodological basis and clinical application.
OBJECTIVE
Trial-to-trial variabilities in event-related potentials (ERP's), which are neglected by investigating averaged ERP's, can be important to establish group-specific effects in clinical studies. Single ERP responses have to be analyzed to quantify these variations. In order to overcome the disadvantages of existing single-sweep estimators, we have developed a new procedure based on wavelet networks (WN's) and applied this novel approach in a study concerning attention deficit hyperactivity disorder (ADHD) in children.


METHODS
WN's represent signals as a linear combination of wavelet nodes, i.e., components characterized by time-frequency features related to the wavelet transformation. In single-sweep analysis, each wavelet node is restricted to a specific region of the time-frequency plane during the recursive WN training process. This is achieved by means of tapering and bandpass filtering with Gaussian functions which are automatically adapted and closely related to the Morlet basis wavelet. The time course of a single event-related response can be reliably estimated. Furthermore, the WN method automatically provides well-defined parameters for single event-related responses, respectively ERP trial-to-trial variabilities.


RESULTS
In a psychophysiological study on ADHD using auditory evoked potentials (AEP's), latency and amplitude parameters extracted from averaged ERP's did not reveal any significant differences between 25 control and 25 ADHD boys. In contrast, interesting group-specific differences could be established by WN single-sweep analysis.


CONCLUSIONS
WN single-sweep analysis can be recommended as a sensitive tool for clinical ERP studies which should be applied in addition to the investigation of averaged responses. INDEX TERMS: Attention deficit hyperactivity disorder (ADHD), event-related potentials, single-sweep estimation, single-sweep parameterization, time-frequency method, wavelet networks.
Heinrich
H
H
University of Goettingen, Germany. hheinri@gwdg.de
Dickhaus
H
H
Rothenberger
A
A
Heinrich
V
V
Moll
G H
GH
D000293:Adolescent; D000465:Algorithms; D000704:Analysis of Variance; D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis*; D002648:Child; D005071:Evoked Potentials; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D011930:Reaction Time; D012815:Signal Processing, Computer-Assisted