Method for automatically identifying and extracting K complex waves in sleep brain waves

Abstract

A method for automatically identifying and extracting K complex waves in sleep brain waves comprises the following steps of performing wavelet decomposition and reconstruction on brain wave signals; performing Teager energy operator calculation on reconstructed data and obtaining absolute values; smoothing and performing 0 / 1 coarse graining on an obtained Teager energy curve; performing threshold detection on the data which is performed on coarse graining; performing morphology detection on reconstructed signals satisfying the threshold values, enabling the signals at positions which satisfying a morphology condition to be the K complex waves and recording starting and final positions and wave crest and wave trough values and positions. The method for automatically identifying and extracting the K complex waves in the sleep brain waves has the advantages of analyzing the signals which is performed on the wavelet decomposition and the reconstruction through the Teager energy operator, extracting an absolute value sequence of the Teager energy operator and performing smoothness and coarse graining processes, being easy to achieve and high in anti-noise capacity, accurately confirming K complex wave positions and the wave crest and trough values and positions and establishing foundation for identification of a non-rem second period in sleep stage and research of the K complex waves.

Description

technical field [0001] The invention relates to the technical field of EEG signals, in particular to a method for automatically identifying and extracting K-complex waves in sleep EEG. Background technique [0002] Sufficient sleep is the three health standards recognized by the international community, but with the advancement of science and technology, the acceleration of the pace of life and the increasing pressure from all aspects, more and more human beings are suffering from various sleep-related diseases , such as: insomnia, lethargy, etc. The decline in sleep quality not only affects people's physical health but also endangers people's mental health, because the decline in sleep quality is often accompanied by the occurrence of some mental diseases, such as: anxiety, depression, chronic pain, fear of insomnia and other symptoms. In addition to mental diseases, the relationship between other diseases and sleep has also been extensively studied. Through the detection ...

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Problems solved by technology

[0005] However, because the Teager energy operator is a method suitable for single-component signal analysis, it is easily affected by noise and interference; and digital filtering technology cannot completely remove low-frequency noise and interference, resulting in the combination of filtering and energy operator The method has a large error in the identification of the K complex wave in the clinical actual signal, which eventually leads to poor overall detection effect, and the generality and robustness of the algorithm are poor.

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