Abstract
In order to detect the sleep spindles simply and efficiently, a novel time-domain approach to detect sleep spindles based on the principles of visual organization is proposed. The code idea of the visual organization is to organize the primary visual elements according to some rules of organization, and to form a more meaningful object of visual processing, as the input of next process. After the collected EEG is processed with the merging algorithm based on the principle of visual organization, it can extract the time-domain feature frequency and duration time better. Use these features with a simple algorithm to detect spindles achieving sensitivity of 92.5% and specificity of 98.1%, which verifies the validity of this method to detect the sleep spindles.
Highlights
After the collected EEG is processed with the merging algorithm based on the principle of visual organization, it can extract the time-domain feature frequency and duration time better
Sleep spindles (SS) generated from complex interactions between thalamic, limbic and cortical areas are the hallmarks of the non-REM stage 2 sleep (N2)
Taking the duration time of sleep spindles 2sec, we calculate the sensitivity and specificity according to the equations below and the results are shown in the Table 1: sen= sitivity true positive (TP) ×100% TP + false negative (FN)
Summary
Sleep spindles (SS) generated from complex interactions between thalamic, limbic and cortical areas are the hallmarks of the non-REM stage 2 sleep (N2). They are sinusoidal spindle-like waveforms which have the characteristic of progressively increasing, gradually decreasing lasting 0.5~3s with a frequency profile at 11 - 16 Hz. The figure characteristic of the waveforms is obvious [1]. Studies [2] have founded that during sleep, the more sleep spindles exist, noises can be more tolerated and the deep sleep can be more kept. Detecting the sleep spindles rapidly and efficiently has a great value in physiological, pathological and pharmacological studies during sleep
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