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首页> 外文会议>International conference on emerging trends in information technology >Sleep Bruxism Disorder Detection and Feature Extraction Using Discrete Wavelet Transform
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Sleep Bruxism Disorder Detection and Feature Extraction Using Discrete Wavelet Transform

机译：采用离散小波变换的睡眠型染色障碍检测和特征提取

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摘要

Sleep Bruxism is characterized by an unconscious act of tooth grinding or clenching of teeth tightly during sleep or awake state. Early diagnosis is advantageous to overcome the damage of jaw, damage of teeth and other health related problems. This paper focuses clenching of teeth in the sleep state only. This paper presents sleep bruxism disease detection and feature extraction. The electroencephalogram (EEG) signal analysis is one of the useful methods for detecting sleep bruxism disorder. For this analysis 10 subjects are considered. For these 10 subjects the EEG signal is extracted from frontal and temporal electrodes F7-T3, T3-T5 and T4-T6. These EEG signals are decomposed into five sub-bands D6-gamma, D7-beta, D8-alpha, D9-theta and A9-delta. The decomposition is done in nine levels since the signals considered has a sampling frequency of 512 Hz. The signal is decomposed using Daubechies order 2 wavelet. From the decomposed signals the detailed coefficients (Dl to D9) and approximation coefficient (A9) are extracted. From extracted coefficient features like energy, variance, mean and standard deviation are calculated to detect sleep bruxism disorder.
机译：睡眠磨牙症的特点是在睡眠或清醒状态下紧紧牙齿的无意识行为或牙齿紧紧地握紧牙齿。早期诊断有利的是克服颌骨的损伤，牙齿损伤和其他健康相关问题。本文仅侧重于睡眠状态的牙齿。本文介绍了睡眠型疾病检测和特征提取。脑电图（EEG）信号分析是检测睡眠染色障碍的有用方法之一。对于该分析，考虑了10个受试者。对于这10个受试者，EEG信号由正面和时间电极F7-T3，T3-T5和T4-T6提取。这些EEG信号分解成五个亚带D6-Gamma，D7-β，D8-α，D9-θ和A9δ。分解在九个水平中完成，因为考虑的信号具有512Hz的采样频率。该信号使用Daubechies Order 2小波分解。从分解信号中提取详细系数（D1至D9）和近似系数（A9）。根据能量，方差，平均值和标准偏差等提取的系数特征来检测睡眠斑紊乱。

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