A Wavelet Packet-Based Algorithm for the Extraction of Neural Rhythms |
| |
Authors: | Osbert C Zalay Eunji E Kang Marija Cotic Peter L Carlen and Berj L Bardakjian |
| |
Institution: | (1) Institute of Biomaterials and Biomedical Engineering, University of Toronto, 164 College Street, Rosebrugh Building, Room 407, Toronto, ON, M5S 3G9, Canada;(2) Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON, M5S 3G4, Canada;(3) Division of Fundamental Neurobiology, Toronto Western Research Institute, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada;(4) Department of Physiology, University of Toronto, 1 King’s College Circle, Toronto, ON, M5S 1A8, Canada |
| |
Abstract: | Neural rhythms are associated with different brain functions and pathological conditions. These rhythms are often clinically
relevant for purposes of diagnosis or treatment, though their complex, time-varying features make them difficult to isolate.
The wavelet packet transform has proven itself to be versatile and effective with respect to resolving signal features in
both time and frequency. We propose a signal analysis technique, called neural rhythm extraction (NRE) that incorporates wavelet
packet analysis along with a threshold-based scheme for separating rhythmic neural features from non-rhythmic ones. We applied
NRE to rat in vitro intracellular recordings and human scalp electroencephalogram (EEG) signals, and were able to isolate and classify individual
neural rhythms in signals containing large amplitude spikes and other artifacts. NRE is capable of discriminating signal features
sharing similar time or frequency localization, as well as extracting low-amplitude, low-power rhythms otherwise masked by
spectrally dominant signal components. The algorithm allows for independent retention and reconstruction of rhythmic features,
which may serve to enhance other analysis techniques such as independent component analysis (ICA), and aid in application-specific
tasks such as detection, classification or tracking. |
| |
Keywords: | Wavelet packet transform Feature extraction Brain rhythms EEG |
本文献已被 SpringerLink 等数据库收录! |
|