Abstract
Sleep disorders have a significant effect on psychological depression and many other human diseases. Nowadays, technology, as well as innovation, has become an essential and analytical part of the world. Detection of sleep disorders by brain waves has become a dynamic study. From this point of view, this paper proposed a model for detecting sleep disorders using time-frequency analysis based feature extraction model based on EEG (Electroencephalogram) signal. In this proposed method, Empirical mode decomposition (EMD) and wavelet packet transform (WPT) time-frequency analysis techniques are used for extracting effective features, because those techniques are very effective for analyzing the non-stationary signal like EEG. In this research, the EMD and WPT are used to decompose the input signal. In EMD decomposition, up to 9th IMFs (Intrinsic Mode Functions) are decomposed. In WPT decomposition, the EEG signal is decomposed up to third level wavelet coefficient. After the decomposition process, different statistical features are extracted, i.e., Shannon entropy, energy, standard deviation, skewness, and kurtosis. However, identifying the optimal sub-band of time-frequency analysis is very challenging. Thus, the genetic algorithm (GA) is used to select the effective subset of the feature. In the detection process, SVM classifier is used and sleep disorders are classified based on trained knowledge. As a result, the performance of the proposed method is evaluated for various statistical features and to find the optimal features for detecting sleep disorder. According to the experimental results, the proposed model shows improved performance by 4.88% improved classification accuracy.
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Islam, M.R., Rahim, M.A., Islam, M.R., Shin, J. (2020). Genetic Algorithm Based Optimal Feature Selection Extracted by Time-Frequency Analysis for Enhanced Sleep Disorder Diagnosis Using EEG Signal. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_65
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DOI: https://doi.org/10.1007/978-3-030-29513-4_65
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