Automatic recognition of chewing noises in epileptic EEG based on period segmentation

Wei, Zuochen; Zou, Junzhong; Zhang, Jian

doi:10.1016/j.neucom.2016.01.029

Cited by 6 publications

(1 citation statement)

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“…The time-domain method to detect sleep spindles depends on the algorithm of the merger of increasing or decreasing sequence based on the visual organization principle which was proposed by Zhang J [9][10]. Visual organization principle is the method to make the basic image elements form an overall subject according to the quantitative model of Gestalt principle in a narrow sense [11].…”

Section: Methods Of Detecting Sleep Spindlesmentioning

confidence: 99%

Correction of Sleep Staging Based on Sleep Spindle Detection

Jin¹,

Zou²,

Zhang³

2017

Proceedings of the 2nd International Conference on Biomedical and Biological Engineering 2017 (BBE 2017)

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Abstract. It has been demonstrated that sleep has a great relationship with humans' health and activities. Sleep staging is an important method to analysis sleep. Lots of methods have been adopted for automatic sleep staging. Some of the results are good and some of them are poor. For the poor, the sleep spindle detection can help to correct the results, because in the sleep stage 2(S2), the characteristic wave is sleep spindle and in the later of sleep stage 1(S1), the waves less than the duration time of sleep spindles but have the same period with sleep spindles (immature sleep spindles) appear on the electroenphalography (EEG). Therefore, a time-domain method for detecting sleep spindles is mentioned in this paper. With this method, the majority of sleep spindles and immature sleep spindles can be detected and the duration time of these detected waves can be used as the feature for sleep staging correction, especially the results of S1 and S2. The experiment results show that the accuracy of sleep staging is increased from 65.68% to 66.22%, 47.2% to 48% of S1 and 72.4% to 74.3% of S2.

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Section: Methods Of Detecting Sleep Spindlesmentioning

confidence: 99%