Development of ECG beat segmentation method by combining lowpass filter and irregular R-R interval checkup strategy
Development of ECG beat segmentation method by combining lowpass filter and irregular R-R interval checkup strategy
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DOI：
10.1016/j.eswa.2009.12.069
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发表时间：
2010-07-01
影响因子：
8.5
通讯作者：
Park, Hun-Kuk
中科院分区：
文献类型：
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作者：
Choi, Samjin;Adnane, Mourad;Park, Hun-Kuk
我们已经开发了一种长期的心肺传感器系统，包括具有自适应硬件滤波器和数据处理算法的可穿戴传感器探头（Choi & Jiang，2006，2008）。然而，为R-R间期（RRI）信息提取提出的数据处理算法在ECG信号具有基线偏移或肌肉伪影的情况下不能很好地工作。此外，由于决策机制薄弱，提取了许多虚假的ECG搏动。然后，这些假搏动产生不规则的RRI信息和错误的心率变异性结果。数据处理算法的修改是非常必要的。因此，本工作提出了一种有效的心搏分割方法，使用不规则的RRI检查策略分为五个连续的RRI模式。该算法由信号处理和心搏检测两部分组成。信号处理包括小波去噪、20 Hz低通滤波器消除基线漂移和单自由度解析模型提取包络曲线。心拍检测器包括候选心拍检测和分割，分别采用单阈值和不规则RRI检测策略。特别地，提出了四种异常RRI模式以找出假ECG搏动。MIT-BIH心律失常数据库被选为测试所提出的算法的数据集。提出的不规则RRI检查策略估计出5463个可疑假心搏，成功分割了其中的96.19%（5255个心搏）。实验结果表明，该算法具有很好的检测效果，检测误差为0.54%，灵敏度为99.66%，阳性预测率为99.80%。此外，我们的算法显示出非常高的准确性，因为数据库的心跳注释与我们获得的心跳发生时间之间的平均时间误差为7.75 ms。（C）2009 Elsevier Ltd.保留所有权利。
We have developed a long-term cardiorespiratory sensor system that includes a wearable sensor probe with adaptive hardware filters and data processing algorithms (Choi & Jiang, 2006, 2008). However, the data processing algorithm proposed for the R-R interval (RRI) information extraction did not work well in the case of ECG signals with baseline shifts or muscle artifacts. Furthermore, many false ECG beats were extracted due to a weak decision-making scheme. Then, those false beats produced irregular RRI information and erroneous heart rate variability results. Modification of data processing algorithm was strongly needed. Therefore, this work presented an efficient ECG beat segmentation method using an irregular RRI checkup strategy into five sequential RRI patterns. This algorithm was comprised of signal processing stage and ECG beat detector stage. The signal processing included the wavelet denoising, the baseline shift elimination by 20 Hz lowpass filter and the envelope curve extraction by a single degree of freedom analytical model. The ECG beat detector included the candidate ECG beat detection and segmentation by one threshold and by irregular RRI checkup strategy, respectively. In particular, four abnormal RRI patterns were proposed to find out false ECG beats. The MIT-BIH arrhythmia database was selected as the dataset for testing the proposed algorithm. The proposed irregular RRI checkup strategy estimated 5463 beats to the suspected false beats and succeeded in segmenting 96.19% (5255 beats) of them. The performance results showed that our algorithm had very good results such as the detection error of 0.54%, sensitivity of 99.66% and positive predictivity of 99.80%. Furthermore, our algorithm showed very high accuracy as the mean time error between the beat annotations of the database and our obtained beat occurence times was 7.75 ms. (C) 2009 Elsevier Ltd. All rights reserved.