Extraction Algorithm of Similar Parts from Multiple Time-Series Data of Cerebral Blood Flow
Extraction Algorithm of Similar Parts from Multiple Time-Series Data of Cerebral Blood Flow
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DOI：
10.1007/978-3-319-02753-1_14
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
T. Hiroyasu;Arika Fukushima;U. Yamamoto
中科院分区：
文献类型：
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作者：
T. Hiroyasu;Arika Fukushima;U. Yamamoto
我们提出了一种算法，可以从两个不同的脑血流时间序列数据集中提取相似的部分。该算法不仅能够提取完全相同的部分，而且能够提取有一些差异的相似部分，因为据报道脑血流的时间序列数据受到各种因素的影响，因此实际数据可能与模型系统不同。为了证实该算法的有效性，我们评估了两组脑血流时间序列数据：一组是人工数据，一组是实际数据，并通过视觉确认和相关系数分析来评估结果。这表明所提出的算法能够从脑血流时间序列数据中提取相似的部分。我们还发现，当数据包含高频噪声时，需要低通滤波器来处理脑血流的时间序列数据。
We propose an algorithm to extract similar parts from two different time-series data sets of cerebral blood flow. The proposed algorithm is capable of extracting not only parts that are exactly the same but also similar parts having a few differences since time-series data of cerebral blood flow is reported to be affected by various factors, and real data may therefore differ from a model system. To confirm the effectiveness of the proposed algorithm, we evaluated two sets of time-series data of cerebral blood flow: one artificial and one of actual data, and evaluated the results by visual confirmation as well as correlation coefficient analysis. This demonstrated that the proposed algorithm was able to extract similar parts from time-series data of cerebral blood flow. We also found that a Low-pass filter was needed to process time-series data of cerebral blood flow, when the data contained high-frequency noise.