K-complexes Detection in EEG Signals using Fractal and Frequency Features Coupled with an Ensemble Classification Model
Article
Article Title | K-complexes Detection in EEG Signals using Fractal and Frequency Features Coupled with an Ensemble Classification Model |
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ERA Journal ID | 14535 |
Article Category | Article |
Authors | Al-Salman, Wessam (Author), Li, Yan (Author) and Wen, Peng (Author) |
Journal Title | Neuroscience |
Journal Citation | 422, pp. 119-133 |
Number of Pages | 15 |
Year | 2019 |
Place of Publication | United Kingdom |
ISSN | 0306-4522 |
1873-7544 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.neuroscience.2019.10.034 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0306452219307262?via%3Dihub |
Abstract | K-complexes are important transient bio-signal waveforms in sleep stage 2. Detecting k-complexes visually requires a highly qualified expert. In this study, an efficient method for detecting k-complexes from electroencephalogram (EEG) signals based on fractal and frequency features coupled with an ensemble model of three classifiers is presented. EEG signals are first partitioned into segments, using a sliding window technique. Then, each EEG segment is decomposed using a dual-tree complex wavelet transform (DT-CWT) to a set of real and imaginary parts. A total of 10 sub-bands are used based on four levels of decomposition, and the high sub-bands are considered in this research for feature extraction. Fractal and frequency features based on DT-CWT and Higuchi’s algorithm are pulled out from each sub-band and then forwarded to an ensemble classifier to detect k-complexes. A twelve-feature set is finally used to detect the sleep EEG characteristics using the ensemble model. The ensemble model is designed using a combination of three classification techniques including a least square |
Keywords | K-complexes, dual-tree complex wavelet transform, fractal dimensions, ensemble model, EEG signals |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
460207. Modelling and simulation | |
461399. Theory of computation not elsewhere classified | |
460306. Image processing | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Agricultural, Computational and Environmental Sciences |
School of Mechanical and Electrical Engineering | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5919/k-complexes-detection-in-eeg-signals-using-fractal-and-frequency-features-coupled-with-an-ensemble-classification-model
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