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

机译:使用傅里叶变换捕获令人醒来与混合之间的心电图信号的研究

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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)中最明显的峰值是R波。本文认为,虽然许多以前的ECG和心率变异性(HRV)研究已经集中在使用R波和R波间隔(RRI)作为分析生理数据的主要方法,但该研究与许多传统方法使用R - 波幅度(RWA)作为数据源,发现在令人醒来,睡眠和觉醒和睡眠之间的过渡期间,RWA显示出显着的变化。可以应用该特性以确定受试者是否处于解调的初始阈值,并且允许评估程序清晰且快速地响应。该研究结果表明,RRI频域的分析代表了对应于心率(HR)类似的低于1(LF / HF <; 1)的低/高频比的自主神经(ANS)活性。官方混合门槛。如果相同的RWA数据用于贬值的波动分析(DFA),则结果表明,尽管α1对混合过程中的变化敏感,但其特征不规则不能解释RWA变化。

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