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Study of Using Fourier Transform to Capture the ECG Signals between Awakeness and Dozing

机译:利用傅里叶变换捕获清醒与打zing之间心电信号的研究

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The most obvious peak in electrocardiogram (ECG) is the R-wave. This paper contends that while many previous ECG and heart rate variability (HRV) studies have focused on using R-wave and R-wave interval (RRI) as the primary method for analyzing physiological data, this study differs from many conventional methods by using R-Wave Amplitude (RWA) as a source of data, and found that during awakeness, sleep and the transition between awakeness and sleep, RWA showed significant changes. This characteristic can be applied to determine if a subject is in the initial threshold of dozing, and allows the assessment program to respond clearly and rapidly. The study results showed that the analysis of RRI frequency domain represents autonomic nervous (ANS) activities in that a low/high frequency ratio of less than 1(LF / HF <;1) corresponding to a similar decrease in heart rate (HR) indicates the official dozing threshold. If the same RWA data is used for detrended fluctuation analysis (DFA), the results showed that although α1 is sensitive to changes during dozing, its characteristic irregularity cannot explain RWA changes.
机译:心电图(ECG)中最明显的峰是R波。本文认为,尽管以前的许多心电图和心率变异性(HRV)研究都集中于使用R波和R波间隔(RRI)作为分析生理数据的主要方法,但这项研究与许多常规方法不同,后者使用R波-Wave Amplitude(RWA)作为数据源,发现在觉醒,睡眠和觉醒与睡眠之间的过渡期间,RWA表现出显着变化。此特征可用于确定受试者是否处于打of的初始阈值,并允许评估程序清晰,快速地做出响应。研究结果表明,对RRI频域的分析代表了自主神经(ANS)活动,其低频/高频比小于1(LF / HF <; 1)对应于类似的心率(HR)降低,表明官方的打threshold门槛。如果将相同的RWA数据用于去趋势波动分析(DFA),结果表明,尽管α1在推土过程中对变化敏感,但其特征不规则性无法解释RWA的变化。

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