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Extracting Heartbeat Intervals Using Self-adaptive Method Based on Ballistocardiography(BCG)

机译:基于心动描记法(BCG)的自适应方法提取心跳间隔

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Ballistocardiogram (BCG) could reflect mechanical activity of cardiovascular system instead of ECG. And it is often acquired by sensitive mattress or chair without any constraints and limitations, but it contains many noise because of the impact of body and acquired equipment, those questions make heart rate detection difficult from the original BCG. In the paper, we propose an adaptive method which is used to extract heartbeat intervals (RR), and the method acquire automatically input parameters of Ensemble Empirical Mode Decomposition (EEMD) algorithm, and then decompose BCG signal using EEMD algorithm, and select adaptively decomposition component of BCG signal, whose periodicity is in accordance with the cardiac cycle completely as the target signal. Furthermore we detect the peak points and calculate the heartbeat intervals series using the target signal. In the result, the proposed method is tested using the BCG datasets from 18 subjects, including 8 females and 10 males (age 20-72). Finally, the heart rate from BCG will be compared with ECG, and the results are satisfactory and have a high accuracy.
机译:心动描记图(BCG)可以反映心血管系统的机械活动,而不是ECG。它通常是由敏感的床垫或椅子采集的,没有任何限制和限制,但是由于身体和所购设备的影响,它包含许多噪声,这些问题使原始BCG很难检测到心率。本文提出了一种自适应方法,该方法用于提取心跳间隔(RR),该方法自动获取集合经验模式分解(EEMD)算法的输入参数,然后使用EEMD算法分解BCG信号,然后选择自适应分解BCG信号的成分,其周期完全符合心动周期作为目标信号。此外,我们检测峰值并使用目标信号计算心跳间隔序列。结果,使用来自18位受试者的BCG数据集对提出的方法进行了测试,其中包括8位女性和10位男性(20-72岁)。最后,将BCG的心率与ECG进行比较,结果令人满意且具有较高的准确性。

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