Improving time–frequency domain sleep EEG classification via singular spectrum analysis
Mahvash Mohammadi, S, Kouchaki, S, Ghavami, M and Sanei, S (2016). Improving time–frequency domain sleep EEG classification via singular spectrum analysis. Journal of Neuroscience Methods. 273, pp. 96-106.
|Authors||Mahvash Mohammadi, S, Kouchaki, S, Ghavami, M and Sanei, S|
© 2016 Elsevier B.V.Background Manual sleep scoring is deemed to be tedious and time consuming. Even among automatic methods such as time–frequency (T–F) representations, there is still room for more improvement. New method To optimise the efficiency of T–F domain analysis of sleep electroencephalography (EEG) a novel approach for automatically identifying the brain waves, sleep spindles, and K-complexes from the sleep EEG signals is proposed. The proposed method is based on singular spectrum analysis (SSA). The single-channel EEG signal (C3-A2) is initially decomposed and then the desired components are automatically separated. In addition, the noise is removed to enhance the discrimination ability of features. The obtained T–F features after preprocessing stage are classified using a multi-class support vector machines (SVMs) and used for the identification of four sleep stages over three sleep types. Furthermore, to emphasise on the usefulness of the proposed method the automatically-determined spindles are parameterised to discriminate three sleep types. Result The four sleep stages are classified through SVM twice: with and without preprocessing stage. The mean accuracy, sensitivity, and specificity for before the preprocessing stage are: 71.5 ± 0.11%, 56.1 ± 0.09% and 86.8 ± 0.04% respectively. However, these values increase significantly to 83.6 ± 0.07%, 70.6 ± 0.14% and 90.8 ± 0.03% after applying SSA. Comparison with existing method The new T–F representation has been compared with the existing benchmarks. Our results prove that, the proposed method well outperforms the previous methods in terms of identification and representation of sleep stages. Conclusion Experimental results confirm the performance improvement in terms of classification rate and also representative T–F domain.
|Keywords||1109 Neurosciences; 1702 Cognitive Science; Neurology & Neurosurgery|
|Journal||Journal of Neuroscience Methods|
|Journal citation||273, pp. 96-106|
|Digital Object Identifier (DOI)||doi:10.1016/j.jneumeth.2016.08.008|
|01 Nov 2016|
|Publication process dates|
|Deposited||07 Mar 2017|
|Accepted||11 Aug 2016|
|Accepted author manuscript|
CC BY-NC-ND 4.0
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