FBDM based time-frequency representation for sleep stages classification using EEG signals

计算机科学 时频表示法 希尔伯特变换 模式识别(心理学) 人工智能 傅里叶变换 时频分析 脑电图 瞬时相位 解析信号 信号(编程语言) 希尔伯特-黄变换 短时傅里叶变换 卷积神经网络 语音识别 分类器(UML) 信号处理 算法 滤波器(信号处理) 数学 傅里叶分析 数字信号处理 计算机视觉 精神科 数学分析 计算机硬件 程序设计语言 心理学
作者
Vipin Gupta,Ram Bilas Pachori
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:64: 102265-102265 被引量:20
标识
DOI:10.1016/j.bspc.2020.102265
摘要

In this paper, we have proposed a new method of time-frequency representation (TFR) which is based on the Fourier-Bessel decomposition method (FBDM). This proposed method is an advanced version of the existing Fourier decomposition method (FDM). The proposed method decomposes the non-stationary signal into a finite number of Fourier-Bessel intrinsic band functions (FBIBFs). The FBIBFs are the real parts of analytic FBIBFs (AFBIBFs) which are obtained from an analytic signal during frequency scanning (FS) operations. The Hilbert transform (HT) is used to generate an analytic signal from the Fourier-Bessel series (FBS) expansion of an arbitrary signal. In addition to FBDM, we have also proposed zero-phase filter-bank based FBDM in order to get fix number of FBIBFs in this work. The performance of the proposed FBDM has been evaluated with the help of Poverall measure and TFR analysis of synthesized signals. The experimental results and performance measures show that the proposed FBDM is more capable for analysis of non-stationary multi-component signals such as linear frequency modulated and nonlinear frequency modulated signals as compared to the existing methods. The developed FBDM has also been used for the classification of six different sleep stages using electroencephalogram (EEG) signals. The convolutional neural network (CNN) classifier has been utilized for the classification of TFR images, which were obtained with the application of FBDM on a publicly available sleep EEG signals database. The developed classification system has achieved 91.90% classification accuracy for the classification of six different sleep stages using EEG signals.
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