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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >'Minimum average risk' as a new peak-detection algorithm applied to myofibrillar dynamics.
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'Minimum average risk' as a new peak-detection algorithm applied to myofibrillar dynamics.

机译：“最小平均风险”作为应用于肌原纤维动力学的新的峰值检测算法。

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摘要

We present a new peak-detection algorithm based on the method of 'minimum average risk' proposed by Kolmogorov and developed for signal processing in various fields. In this method, translations of features within a signal scan are quantified by minimizing the integrated pointwise product of each scan relative to the first derivative of the immediately previous scan. We have adapted this method for use in a new algorithm to monitor dynamic changes of sarcomere length in single myofibrillar sarcomeres of striated muscles, but the algorithm can also be used more generally for peak localization. We find that this method results in sub-nanometer precision and higher signal-to-noise ratio than current methods. At an equal noise level, the RMS deviation of the minimum average risk algorithm was 1.3 times lower than that of the center of mass method with modeled data and 3-4 times lower with actual data.
机译：我们提出了一种新的峰值检测算法，该算法基于Kolmogorov提出的“最小平均风险”方法，并针对各种领域的信号处理而开发。在这种方法中，信号扫描中特征的平移通过最小化每个扫描相对于前一次扫描的一阶导数的积分点积来量化。我们已经将此方法调整为用于新算法中，以监测横纹肌单个肌原纤维肉瘤中肌节长度的动态变化，但是该算法也可以更广泛地用于峰定位。我们发现，与当前方法相比，该方法可实现亚纳米级精度和更高的信噪比。在噪声水平相同的情况下，使用模型数据的最小平均风险算法的RMS偏差比质量中心方法的RMS偏差低1.3倍，对于实际数据，其RMS偏差低3-4倍。

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