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Sleep staging method based on single-channel electroencephalogram signal ocular artifact removal

A technology of EEG signal and EEG artifacts, applied in instruments, character and pattern recognition, computer components, etc., can solve problems such as difficulties in knowledge extraction and expression, inability to learn knowledge, and inability to fully mine EEG signals. The effect of high accuracy, fast calculation speed, and improved accuracy of sleep staging

Active Publication Date: 2015-07-29
HARBIN INST OF TECH
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  • Summary
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  • Claims
  • Application Information

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Problems solved by technology

[0006] The purpose of the present invention is to solve the lack of reference electrooculogram signals in traditional methods, the removal method is relatively difficult, and the extraction and expression of knowledge are relatively difficult, and its convergence sometimes cannot be guaranteed, and knowledge cannot be directly learned and the EEG signals cannot be fully tapped. In view of the problem of sleep information contained in it, a sleep staging method based on the removal of oculoelectric artifacts from single-channel EEG signals is proposed.

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  • Sleep staging method based on single-channel electroencephalogram signal ocular artifact removal
  • Sleep staging method based on single-channel electroencephalogram signal ocular artifact removal
  • Sleep staging method based on single-channel electroencephalogram signal ocular artifact removal

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specific Embodiment approach 1

[0061] Specific Embodiment 1: A sleep staging method based on single-channel EEG signal oculoelectric artifact removal in this embodiment is specifically prepared according to the following steps:

[0062] Step 1. Perform wavelet transformation on the collected single-channel EEG signal X(n) to obtain M wavelet coefficients; wherein, the M wavelet coefficients are divided into two categories: P wavelet coefficients that do not contain electrooculogram artifacts and The number of wavelet coefficients containing oculoelectric artifacts is M-P;

[0063] Step 2. For the P wavelet coefficients that do not contain electrooculogram artifacts, directly use them as pure EEG signals;

[0064] Step 3. After empirical mode decomposition, the M-P wavelet coefficients W(i) containing electrooculograph artifacts are expressed as the sum of the number of N intrinsic mode functions, that is, the number of IMF (Intrinsic Mode Function) components and the residual:

[0065] W ...

specific Embodiment approach 2

[0075] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that the frequency domain parameters ratio(δ), ratio(θ), ratio(α) and ratio(β) extracted in step 7 are specifically:

[0076] According to the formula (2), (3), (4) and (5), the frequency band energy ratio of each frequency band signal in the pure EEG signal X(n) is calculated, that is, the characteristic parameters of the 4 frequency domain parameters are respectively:

[0077]ratio(δ)=E(δ) / E all (2)

[0078] Wherein, ratio (δ) is the frequency band energy ratio characteristic parameter of the δ frequency band in the pure EEG signal X(n);

[0079] ratio(θ)=E(θ) / E all (3)

[0080] Wherein, ratio (θ) is the frequency band energy ratio characteristic parameter of the θ frequency band in the pure EEG signal X (n);

[0081] ratio(α)=E(α) / E all (4)

[0082] Wherein, ratio (α) is the frequency band energy ratio characteristic parameter of the α frequency band in the pure EEG...

specific Embodiment approach 3

[0085] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is: E(δ), E(θ), E(α) and E(β) are specifically:

[0086] Carry out frequency domain analysis on the pure EEG signal with oculoelectric artifacts removed, and extract the characteristic parameters of "frequency band energy ratio" of four rhythmic EEG signals;

[0087] (1), perform discrete Fourier transform on the pure EEG signal X(n) according to formula (6), and obtain the power spectrum P(k) of the signal X(n):

[0088] P ( k ) = Σ n = 0 N - 1 X ( n ) e - I 2 π N ...

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Abstract

The invention provides a sleep staging method based on single-channel electroencephalogram signal ocular artifact removal, and relates to a sleep staging method of ocular artifact removal. The sleep staging method solves the problems that in a traditional method, reference electro-oculogram signals are lacked, a removal method is difficult, knowledge extraction and expression are difficult, astringency of the method can not be ensured sometimes, knowledge can not be directly learned, and sleeping information contained in electroencephalogram signals can not be fully mined. The method is achieved through the steps of firstly, obtaining M wavelet coefficients; secondly, using P wavelet coefficients containing no ocular artifact as pure electroencephalogram signals; thirdly, obtaining the number and the residual error sum of IMF components; fourthly, obtaining electroencephalogram components and electro-oculogram components; fifthly, reconstructing the pure electroencephalogram signals; sixthly, obtaining X(n); seventhly, extracting seven characteristic parameters; eighthly, obtaining the sleeping state staging index. The sleep staging method is applied to the field of sleep staging.

Description

technical field [0001] The invention relates to a sleep staging method for removing oculoelectric artifacts, in particular to a sleep staging method for removing oculoelectric artifacts based on single-channel electroencephalogram signals. Background technique [0002] As the natural state of human rest, sleep accounts for about one-third of human life. Therefore, sleep staging is of great significance for studying the influence of sleep on neural function, sleep disorders, and the relationship between sleep and other medical state disorders. Electroencephalogram (EEG) is the reflection of the voltage changes between the axons and dendrites of brain cells and between cells on the scalp of the brain, and contains a wealth of brain function status information. Therefore, the analysis of EEG has very important practical significance. In the process of sleep-related EEG signal analysis, removal of oculograph artifacts, feature parameter extraction and classification are the mos...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06F2218/06G06F2218/08G06F18/241
Inventor 刘志勇孙金玮朱政刘丹黄博妍
Owner HARBIN INST OF TECH
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