论文标题
Boosted-springDTW，用于全面提取生理信号的特征
Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals
论文作者
论文摘要
目标：从生理信号中实现最高质量的全面特征，尽管发展波形形态，但可以实现精确的生理参数估计。方法：我们提出了一个概率的框架，该概率框架利用动态时间扭曲（DTW）和最小的域特异性启发式方法，以同时分段生理信号并识别代表心脏事件的基准点。自动动态模板适应不断发展的波形形态。我们使用基准PPG数据集验证了增强的SpringDTW性能，其形态包括受试者和呼吸诱导的变异。结果：在估计IBI时，Boosted-SpringDTW达到了精度，召回和F1得分超过0.96，以识别基准点和平均绝对误差值小于11.41毫秒。结论：增强的springDTW提高了F1得分，而基线提取算法的平均基线提取算法平均提高了35％，而IBI估计平均平均差异为16％。意义：可穿戴设备的精确血液动力学参数估计可以在患者的日常生活中进行连续的健康监测。
Goal: To achieve-high quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies. Methods: We propose Boosted-SpringDTW, a probabilistic framework that leverages dynamic time warping (DTW) and minimal domain-specific heuristics to simultaneously segment physiological signals and identify fiducial points that represent cardiac events. An automated dynamic template adapts to evolving waveform morphologies. We validate Boosted-SpringDTW performance with a benchmark PPG dataset whose morphologies include subject- and respiratory-induced variation. Results: Boosted-SpringDTW achieves precision, recall, and F1-scores over 0.96 for identifying fiducial points and mean absolute error values less than 11.41 milliseconds when estimating IBI. Conclusion: Boosted-SpringDTW improves F1-Scores compared to two baseline feature extraction algorithms by 35 percent on average for fiducial point identification and mean percent difference by 16 percent on average for IBI estimation. Significance: Precise hemodynamic parameter estimation with wearable devices enables continuous health monitoring throughout a patients' daily life.
